In [4]:
import tensorflow as tf
from tensorflow.keras.layers import Dense, BatchNormalization, Flatten
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.models import Sequential
import tensorflow_model_optimization as tfmot
import numpy as np

import pickle

import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
from rpy2.robjects import FloatVector
import os
In [5]:
# os.environ['R_HOME'] = 'C:\Program Files\R\R-4.1.0' #path R installation
In [6]:
itses = importr('itses') # For SparseMAD() as itses()
est_iterative = robjects.r['itses']
In [7]:
class SureMaskedDense(Dense, tfmot.sparsity.keras.PrunableLayer):
    def __init__(self, units, **kwargs):
        super(SureMaskedDense, self).__init__(units, **kwargs)
        self.units = units
        self.masks = []


    def build(self, input_shape):
        super(SureMaskedDense, self).build(input_shape)
        
        for weight in self.get_prunable_weights():
            self.masks += [tf.Variable(tf.ones(weight.shape, tf.float32),
                                       trainable=False,
                                       name="kernel_mask")]

    def call(self, inputs):
        self.mask_weights()
        return super(SureMaskedDense, self).call(inputs)

    def mask_weights(self):
        for mask, weight in zip(self.masks, self.get_prunable_weights()):
            weight.assign(tf.squeeze(weight * mask))

    def get_prunable_weights(self):
        return [self.kernel]




def hard_threshold(weight, mask, sparsity):
    weights_r = tf.reshape(weight, [-1]).numpy()
    sparsity = sparsity[1].numpy()
    upper_percentile = np.percentile(np.abs(weights_r), sparsity*100)
    print("Wanted sparsity", sparsity)
    print("Upper percentile", upper_percentile)
    try:
        weights_r = FloatVector(weights_r)
        sparsity = FloatVector([sparsity])
        current_threshold = est_iterative(weights_r, method="HT", sparsity=sparsity)[0][0]
        print("Thresholhold", current_threshold)
        old_mask = False
        if(upper_percentile < current_threshold):
            print("Threshold over percentile. Lowering.")
            current_threshold = upper_percentile
        else:
            print("Using suggest threshold.")
    except:
        print("Keeping old")
        old_mask = True

    weight = mask * weight
    if not old_mask:
        print("Applying new mask")
        abs_weight = tf.math.abs(weight)
        new_mask = tf.logical_not(tf.math.greater_equal(abs_weight, current_threshold))
        mask = tf.cast(1. - mask, tf.bool)
        mask = tf.cast(tf.logical_not(tf.math.logical_or(mask, new_mask)), weight.dtype)
        print("Percentage zeros", tf.math.reduce_mean(1. - mask).numpy())
        print(mask)
        weight = weight * mask
    return weight, mask

class ShrinkCallback(Callback):
    def __init__(self, schedule, gradient_adjusment=False, data = None):
        super(ShrinkCallback, self).__init__()
        self.schedule = schedule
        self.gradient_adjusment = gradient_adjusment
        self.steps = 0
        self.data = data

    def prune(self, epoch, weights, masks, tape=None, loss=None, epsilon=1e-12):
        new_masks = []
        sparsity = self.schedule(epoch)

        for weight, mask in zip(weights, masks):
            if self.gradient_adjusment:
                grad = tape.gradient(loss, weight)
                weight = weight / (tf.math.abs(grad) + epsilon)
                new_weight, mask = hard_threshold(weight, mask, sparsity=sparsity)
                new_weight = new_weight * (tf.math.abs(grad) + epsilon)
            else:
                new_weight, mask = hard_threshold(weight, mask, sparsity=sparsity)
            weight.assign(new_weight)
            new_masks += [mask]
        return new_masks
    
    def on_train_begin(self, logs = None):
        self.steps = 0

    def on_batch_begin(self, batch, logs=None):
        self.steps += 1
        if self.schedule(self.steps)[0]:  
            if self.gradient_adjusment:
                for batch in self.data.take(1):
                    x = batch[0]
                    y = batch[1]
                with tf.GradientTape(persistent=True) as tape:
                    y_pred = self.model(x)
                    loss = self.model.loss(y, y_pred)
            else:                
                tape = None
                loss = None



            for layer in self.model.layers:
                if hasattr(layer, 'masks'):
                    layer.mask_weights()
                    weights = layer.get_prunable_weights()
                    new_masks = self.prune(self.steps, weights, layer.masks, tape=tape, loss=loss)

                    for new_mask, old_mask in zip(new_masks, layer.masks):
                        old_mask.assign(new_mask)
                    layer.mask_weights()

            if self.gradient_adjusment:
                del tape
        return
    

    
class SparsityCallback(Callback):
    def get_sparsity(self):
        weights_list = []
        for layer in self.model.layers:
            if isinstance(layer, tf.keras.layers.Wrapper):
                layer = layer.layer
            if isinstance(layer, tfmot.sparsity.keras.PrunableLayer):
                weights = layer.get_prunable_weights()
                weights_list += [tf.reshape(weight,-1).numpy() for weight in weights]
        weights_list = np.concatenate(weights_list)
        print(weights_list)
        return np.mean(weights_list == 0)
    def on_epoch_end(self, epoch, logs = None):
        sparsity = self.get_sparsity()
        logs["sparsity"] = sparsity
        print("Sparsity at:", sparsity)
      
    
def get_data(batch_size, buffer_size = 64):
    (x_train, y_train), (x_test, y_test)  = tf.keras.datasets.mnist.load_data()

    train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))

    def mapper(x_train, y_train):
        x_train = tf.cast(x_train, tf.float32)
        y_train = tf.cast(y_train, tf.int32)
        x_train = x_train/255.
        return x_train, y_train
    train_dataset = train_dataset.map(mapper).shuffle(buffer_size).batch(batch_size)
    test_dataset = test_dataset.map(mapper).shuffle(buffer_size).batch(batch_size)

    return train_dataset, test_dataset


def get_mnist_model(kernel_regularizer = 'l2', batch_norm = True):
    if  batch_norm:
        mnist_model = Sequential([
            Flatten(input_shape = (28, 28)),
            SureMaskedDense(300, activation = "relu",kernel_regularizer=kernel_regularizer),
            BatchNormalization(),
            SureMaskedDense(100, activation = "relu",kernel_regularizer=kernel_regularizer),
            BatchNormalization(),
            SureMaskedDense(10, activation = "softmax",kernel_regularizer=kernel_regularizer)])
    else:
        mnist_model = Sequential([
            Flatten(input_shape = (28, 28)),
            SureMaskedDense(64, activation = "tanh",kernel_regularizer=kernel_regularizer),
            SureMaskedDense(128, activation = "tanh",kernel_regularizer=kernel_regularizer),
            SureMaskedDense(10, activation = "softmax",kernel_regularizer=kernel_regularizer)])

    return mnist_model
        
def compile_model(model):
    model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                        optimizer=tf.keras.optimizers.Adam(0.001),
                        metrics=['accuracy'])
In [11]:
# Train base
epochs = 200
batch_size = 256
train_dataset, test_dataset = get_data(batch_size)
num_batches = len(list(train_dataset))
end_pruning_epoch =  450
pruning_epoch_frequency = 50
prune_epochs = 500
schedule = tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.50,
                                                               final_sparsity=0.99,
                                                               begin_step=1,
                                                               end_step= num_batches * end_pruning_epoch, 
                                                frequency = num_batches*pruning_epoch_frequency)
In [12]:
def get_base_model(epochs,batch_size, train_dataset, test_dataset, kernel_regularizer, batch_norm, seed = 1234):
    tf.random.set_seed(seed)
    mnist_model = get_mnist_model(kernel_regularizer = kernel_regularizer, batch_norm = batch_norm)
    compile_model(mnist_model)
    mnist_model.fit(train_dataset, epochs = epochs, validation_data = test_dataset)
    
    return mnist_model

def iterative_pruning(prune_epcochs, train_dataset, test_dataset, schedule, original_model, seed = 1234):
    iterative_mnist_model = tf.keras.models.clone_model(original_model)
    compile_model(iterative_mnist_model)
    sparsity_callback = SparsityCallback()
    shrink_callback = ShrinkCallback(schedule)
    tf.random.set_seed(seed)
    history_iterative_prune = iterative_mnist_model.fit(train_dataset,
                                                      epochs = prune_epcochs,
                                                      validation_data = test_dataset,
                                                      callbacks = [shrink_callback, sparsity_callback])
    return iterative_mnist_model, history_iterative_prune


def apply_pruning_to_dense(layer, pruning_schedule = schedule):
    if isinstance(layer, tf.keras.layers.Dense):
        return tfmot.sparsity.keras.prune_low_magnitude(layer, pruning_schedule = pruning_schedule)
    return layer

def magnitude_pruning(prune_epcochs, train_dataset, test_dataset, schedule, original_model, seed = 1234):
    tf.random.set_seed(seed)
    magnitude_mnist_model = tf.keras.models.clone_model(
        original_model,
        clone_function=apply_pruning_to_dense,
    )
    compile_model(magnitude_mnist_model)
    sparsity_callback = SparsityCallback()


    callbacks = [
      tfmot.sparsity.keras.UpdatePruningStep(),
        sparsity_callback
    ]
    history_magnitude_prune = magnitude_mnist_model.fit(train_dataset,
                                                      epochs = prune_epcochs,
                                                      validation_data = test_dataset,
                                                      callbacks = callbacks)
    return magnitude_mnist_model, history_magnitude_prune
In [13]:
for j in [1,2,3,4,5]:    
    l2_base_batch = get_base_model(epochs,batch_size, train_dataset, test_dataset, "l2", batch_norm  = True, seed = j)
    base_batch = get_base_model(epochs,batch_size, train_dataset, test_dataset, None, batch_norm  = True, seed = j)

    l2_base_no_batch = get_base_model(epochs,batch_size, train_dataset, test_dataset, "l2", batch_norm  = False, seed = j)
    base_no_batch = get_base_model(epochs,batch_size, train_dataset, test_dataset, None, batch_norm  = False, seed = j)

    models = [l2_base_batch, base_batch, l2_base_no_batch, base_no_batch]

    iterative_histories = []
    iterative_models = []
    for model in models: 
        model, history =  iterative_pruning(prune_epochs, train_dataset, test_dataset, schedule, model, seed = j)
        iterative_models += [model]
        iterative_histories += [history.history]

        with open('output/neural-network-pruning/pickle-jar/iterative_histories'+str(j)+'.pickle', 'wb') as file:
            pickle.dump(iterative_histories, file)

    magnitude_histories = []
    magnitude_models = []
    for model in models: 
        model, history =  magnitude_pruning(prune_epochs, train_dataset, test_dataset, schedule, model, seed = j)
        magnitude_models += [model]
        magnitude_histories += [history.history]
        with open('output/neural-network-pruning/pickle-jar/magnitude_histories'+str(j)+'.pickle', 'wb') as file:
            pickle.dump(magnitude_histories, file)
Epoch 1/200
235/235 [==============================] - 4s 15ms/step - loss: 2.1850 - accuracy: 0.9255 - val_loss: 1.5350 - val_accuracy: 0.9102
Epoch 2/200
235/235 [==============================] - 4s 16ms/step - loss: 0.4388 - accuracy: 0.9595 - val_loss: 0.4665 - val_accuracy: 0.9536
Epoch 3/200
235/235 [==============================] - 4s 15ms/step - loss: 0.3123 - accuracy: 0.9637 - val_loss: 0.3425 - val_accuracy: 0.9459
Epoch 4/200
235/235 [==============================] - 3s 15ms/step - loss: 0.2770 - accuracy: 0.9654 - val_loss: 0.3068 - val_accuracy: 0.9525
Epoch 5/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2603 - accuracy: 0.9667 - val_loss: 0.2895 - val_accuracy: 0.9511
Epoch 6/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2447 - accuracy: 0.9683 - val_loss: 0.2654 - val_accuracy: 0.9594
Epoch 7/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2345 - accuracy: 0.9699 - val_loss: 0.2728 - val_accuracy: 0.9549
Epoch 8/200
235/235 [==============================] - 4s 16ms/step - loss: 0.2302 - accuracy: 0.9691 - val_loss: 0.2528 - val_accuracy: 0.9604
Epoch 9/200
235/235 [==============================] - 3s 15ms/step - loss: 0.2189 - accuracy: 0.9705 - val_loss: 0.2361 - val_accuracy: 0.9655
Epoch 10/200
235/235 [==============================] - 3s 15ms/step - loss: 0.2143 - accuracy: 0.9703 - val_loss: 0.2403 - val_accuracy: 0.9626
Epoch 11/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2095 - accuracy: 0.9713 - val_loss: 0.2408 - val_accuracy: 0.9599
Epoch 12/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2071 - accuracy: 0.9718 - val_loss: 0.2392 - val_accuracy: 0.9593
Epoch 13/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2012 - accuracy: 0.9726 - val_loss: 0.2423 - val_accuracy: 0.9596
Epoch 14/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1952 - accuracy: 0.9732 - val_loss: 0.2257 - val_accuracy: 0.9632
Epoch 15/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1966 - accuracy: 0.9720 - val_loss: 0.2357 - val_accuracy: 0.9592
Epoch 16/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1915 - accuracy: 0.9731 - val_loss: 0.2331 - val_accuracy: 0.9592
Epoch 17/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1931 - accuracy: 0.9715 - val_loss: 0.2196 - val_accuracy: 0.9641
Epoch 18/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1860 - accuracy: 0.9738 - val_loss: 0.2182 - val_accuracy: 0.9624
Epoch 19/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1869 - accuracy: 0.9722 - val_loss: 0.2249 - val_accuracy: 0.9619
Epoch 20/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1829 - accuracy: 0.9731 - val_loss: 0.2106 - val_accuracy: 0.9653
Epoch 21/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1791 - accuracy: 0.9742 - val_loss: 0.2444 - val_accuracy: 0.9530
Epoch 22/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1819 - accuracy: 0.9734 - val_loss: 0.2232 - val_accuracy: 0.9612
Epoch 23/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1799 - accuracy: 0.9740 - val_loss: 0.2191 - val_accuracy: 0.9614
Epoch 24/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1783 - accuracy: 0.9737 - val_loss: 0.2106 - val_accuracy: 0.9643
Epoch 25/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1751 - accuracy: 0.9749 - val_loss: 0.2110 - val_accuracy: 0.9644
Epoch 26/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1744 - accuracy: 0.9746 - val_loss: 0.2312 - val_accuracy: 0.9572
Epoch 27/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1730 - accuracy: 0.9746 - val_loss: 0.2244 - val_accuracy: 0.9615
Epoch 28/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1745 - accuracy: 0.9735 - val_loss: 0.2129 - val_accuracy: 0.9617
Epoch 29/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1732 - accuracy: 0.9739 - val_loss: 0.2239 - val_accuracy: 0.9583
Epoch 30/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1714 - accuracy: 0.9744 - val_loss: 0.2074 - val_accuracy: 0.9644
Epoch 31/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1692 - accuracy: 0.9751 - val_loss: 0.2304 - val_accuracy: 0.9572
Epoch 32/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1702 - accuracy: 0.9746 - val_loss: 0.2422 - val_accuracy: 0.9530
Epoch 33/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1722 - accuracy: 0.9737 - val_loss: 0.2141 - val_accuracy: 0.9623
Epoch 34/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1637 - accuracy: 0.9757 - val_loss: 0.2165 - val_accuracy: 0.9610
Epoch 35/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1676 - accuracy: 0.9745 - val_loss: 0.2252 - val_accuracy: 0.9569
Epoch 36/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1669 - accuracy: 0.9749 - val_loss: 0.2298 - val_accuracy: 0.9597
Epoch 37/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1667 - accuracy: 0.9759 - val_loss: 0.2230 - val_accuracy: 0.9584
Epoch 38/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1636 - accuracy: 0.9753 - val_loss: 0.2309 - val_accuracy: 0.9541
Epoch 39/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1626 - accuracy: 0.9764 - val_loss: 0.2274 - val_accuracy: 0.9569
Epoch 40/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1647 - accuracy: 0.9751 - val_loss: 0.2323 - val_accuracy: 0.9553
Epoch 41/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1636 - accuracy: 0.9754 - val_loss: 0.2395 - val_accuracy: 0.9531
Epoch 42/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1653 - accuracy: 0.9751 - val_loss: 0.2211 - val_accuracy: 0.9616
Epoch 43/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1621 - accuracy: 0.9759 - val_loss: 0.2158 - val_accuracy: 0.9601
Epoch 44/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1603 - accuracy: 0.9768 - val_loss: 0.2527 - val_accuracy: 0.9481
Epoch 45/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1635 - accuracy: 0.9755 - val_loss: 0.2153 - val_accuracy: 0.9621
Epoch 46/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1620 - accuracy: 0.9764 - val_loss: 0.1961 - val_accuracy: 0.9670
Epoch 47/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1629 - accuracy: 0.9753 - val_loss: 0.2121 - val_accuracy: 0.9618
Epoch 48/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1608 - accuracy: 0.9759 - val_loss: 0.2367 - val_accuracy: 0.9557
Epoch 49/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1583 - accuracy: 0.9772 - val_loss: 0.2241 - val_accuracy: 0.9558
Epoch 50/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1582 - accuracy: 0.9767 - val_loss: 0.2063 - val_accuracy: 0.9619
Epoch 51/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1582 - accuracy: 0.9763 - val_loss: 0.2034 - val_accuracy: 0.9636
Epoch 52/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1572 - accuracy: 0.9773 - val_loss: 0.2067 - val_accuracy: 0.9611
Epoch 53/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1602 - accuracy: 0.9756 - val_loss: 0.2121 - val_accuracy: 0.9622
Epoch 54/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1578 - accuracy: 0.9770 - val_loss: 0.2315 - val_accuracy: 0.9571
Epoch 55/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1607 - accuracy: 0.9757 - val_loss: 0.2132 - val_accuracy: 0.9588
Epoch 56/200
235/235 [==============================] - 5s 19ms/step - loss: 0.1584 - accuracy: 0.9761 - val_loss: 0.2275 - val_accuracy: 0.9588
Epoch 57/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1588 - accuracy: 0.9760 - val_loss: 0.1913 - val_accuracy: 0.9672
Epoch 58/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1558 - accuracy: 0.9768 - val_loss: 0.2000 - val_accuracy: 0.9638
Epoch 59/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9773 - val_loss: 0.2128 - val_accuracy: 0.9612
Epoch 60/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1564 - accuracy: 0.9762 - val_loss: 0.2160 - val_accuracy: 0.9602
Epoch 61/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1538 - accuracy: 0.9771 - val_loss: 0.2297 - val_accuracy: 0.9566
Epoch 62/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1571 - accuracy: 0.9765 - val_loss: 0.2174 - val_accuracy: 0.9600
Epoch 63/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1560 - accuracy: 0.9768 - val_loss: 0.2252 - val_accuracy: 0.9568
Epoch 64/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1566 - accuracy: 0.9765 - val_loss: 0.2147 - val_accuracy: 0.9599
Epoch 65/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1546 - accuracy: 0.9766 - val_loss: 0.1942 - val_accuracy: 0.9649
Epoch 66/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1591 - accuracy: 0.9756 - val_loss: 0.1933 - val_accuracy: 0.9668
Epoch 67/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1583 - accuracy: 0.9760 - val_loss: 0.2080 - val_accuracy: 0.9625
Epoch 68/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1552 - accuracy: 0.9771 - val_loss: 0.2337 - val_accuracy: 0.9552
Epoch 69/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1558 - accuracy: 0.9763 - val_loss: 0.2177 - val_accuracy: 0.9578
Epoch 70/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1533 - accuracy: 0.9768 - val_loss: 0.2275 - val_accuracy: 0.9568
Epoch 71/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1533 - accuracy: 0.9773 - val_loss: 0.2447 - val_accuracy: 0.9499
Epoch 72/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1572 - accuracy: 0.9756 - val_loss: 0.2073 - val_accuracy: 0.9604
Epoch 73/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1540 - accuracy: 0.9765 - val_loss: 0.1984 - val_accuracy: 0.9630
Epoch 74/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1542 - accuracy: 0.9762 - val_loss: 0.2074 - val_accuracy: 0.9605
Epoch 75/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1529 - accuracy: 0.9776 - val_loss: 0.1973 - val_accuracy: 0.9636
Epoch 76/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1517 - accuracy: 0.9775 - val_loss: 0.2038 - val_accuracy: 0.9614
Epoch 77/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1522 - accuracy: 0.9778 - val_loss: 0.2000 - val_accuracy: 0.9651
Epoch 78/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1502 - accuracy: 0.9780 - val_loss: 0.2018 - val_accuracy: 0.9637
Epoch 79/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1530 - accuracy: 0.9763 - val_loss: 0.2123 - val_accuracy: 0.9588
Epoch 80/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1512 - accuracy: 0.9772 - val_loss: 0.1935 - val_accuracy: 0.9650
Epoch 81/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1512 - accuracy: 0.9777 - val_loss: 0.1965 - val_accuracy: 0.9651
Epoch 82/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1524 - accuracy: 0.9778 - val_loss: 0.2085 - val_accuracy: 0.9610
Epoch 83/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1547 - accuracy: 0.9766 - val_loss: 0.1900 - val_accuracy: 0.9665
Epoch 84/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1519 - accuracy: 0.9770 - val_loss: 0.2246 - val_accuracy: 0.9571
Epoch 85/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1480 - accuracy: 0.9783 - val_loss: 0.2230 - val_accuracy: 0.9568
Epoch 86/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1555 - accuracy: 0.9760 - val_loss: 0.2225 - val_accuracy: 0.9568
Epoch 87/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1518 - accuracy: 0.9768 - val_loss: 0.1952 - val_accuracy: 0.9649
Epoch 88/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1510 - accuracy: 0.9773 - val_loss: 0.2074 - val_accuracy: 0.9620
Epoch 89/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1480 - accuracy: 0.9779 - val_loss: 0.2077 - val_accuracy: 0.9612
Epoch 90/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1503 - accuracy: 0.9766 - val_loss: 0.2383 - val_accuracy: 0.9535
Epoch 91/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1496 - accuracy: 0.9771 - val_loss: 0.2005 - val_accuracy: 0.9625
Epoch 92/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1489 - accuracy: 0.9771 - val_loss: 0.1898 - val_accuracy: 0.9655
Epoch 93/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9779 - val_loss: 0.2269 - val_accuracy: 0.9557
Epoch 94/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1505 - accuracy: 0.9776 - val_loss: 0.2140 - val_accuracy: 0.9599
Epoch 95/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1504 - accuracy: 0.9771 - val_loss: 0.2064 - val_accuracy: 0.9601
Epoch 96/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1472 - accuracy: 0.9780 - val_loss: 0.2336 - val_accuracy: 0.9541
Epoch 97/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9780 - val_loss: 0.2234 - val_accuracy: 0.9550
Epoch 98/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9784 - val_loss: 0.2542 - val_accuracy: 0.9497
Epoch 99/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1530 - accuracy: 0.9760 - val_loss: 0.2299 - val_accuracy: 0.9537
Epoch 100/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1523 - accuracy: 0.9772 - val_loss: 0.2204 - val_accuracy: 0.9566
Epoch 101/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1512 - accuracy: 0.9774 - val_loss: 0.2180 - val_accuracy: 0.9576
Epoch 102/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1492 - accuracy: 0.9772 - val_loss: 0.1989 - val_accuracy: 0.9642
Epoch 103/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1475 - accuracy: 0.9782 - val_loss: 0.2135 - val_accuracy: 0.9595
Epoch 104/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9777 - val_loss: 0.1986 - val_accuracy: 0.9616
Epoch 105/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1462 - accuracy: 0.9782 - val_loss: 0.1918 - val_accuracy: 0.9644
Epoch 106/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.2034 - val_accuracy: 0.9622
Epoch 107/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9778 - val_loss: 0.2156 - val_accuracy: 0.9578
Epoch 108/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1485 - accuracy: 0.9774 - val_loss: 0.2294 - val_accuracy: 0.9558
Epoch 109/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9780 - val_loss: 0.2078 - val_accuracy: 0.9604
Epoch 110/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9785 - val_loss: 0.1991 - val_accuracy: 0.9631
Epoch 111/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9779 - val_loss: 0.1891 - val_accuracy: 0.9662
Epoch 112/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1433 - accuracy: 0.9789 - val_loss: 0.1992 - val_accuracy: 0.9637
Epoch 113/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1500 - accuracy: 0.9766 - val_loss: 0.2103 - val_accuracy: 0.9604
Epoch 114/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1455 - accuracy: 0.9785 - val_loss: 0.2115 - val_accuracy: 0.9611
Epoch 115/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9773 - val_loss: 0.2173 - val_accuracy: 0.9559
Epoch 116/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1473 - accuracy: 0.9775 - val_loss: 0.2280 - val_accuracy: 0.9569
Epoch 117/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1464 - accuracy: 0.9780 - val_loss: 0.2039 - val_accuracy: 0.9600
Epoch 118/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1456 - accuracy: 0.9778 - val_loss: 0.2392 - val_accuracy: 0.9543
Epoch 119/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1460 - accuracy: 0.9786 - val_loss: 0.2284 - val_accuracy: 0.9528
Epoch 120/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1478 - accuracy: 0.9776 - val_loss: 0.2207 - val_accuracy: 0.9581
Epoch 121/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1455 - accuracy: 0.9782 - val_loss: 0.1977 - val_accuracy: 0.9638
Epoch 122/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1468 - accuracy: 0.9776 - val_loss: 0.2137 - val_accuracy: 0.9597
Epoch 123/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9794 - val_loss: 0.2329 - val_accuracy: 0.9525
Epoch 124/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1447 - accuracy: 0.9778 - val_loss: 0.1982 - val_accuracy: 0.9660
Epoch 125/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1408 - accuracy: 0.9794 - val_loss: 0.2049 - val_accuracy: 0.9603
Epoch 126/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1448 - accuracy: 0.9788 - val_loss: 0.1992 - val_accuracy: 0.9625
Epoch 127/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1443 - accuracy: 0.9784 - val_loss: 0.2209 - val_accuracy: 0.9587
Epoch 128/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1444 - accuracy: 0.9786 - val_loss: 0.2193 - val_accuracy: 0.9574
Epoch 129/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1465 - accuracy: 0.9778 - val_loss: 0.1955 - val_accuracy: 0.9670
Epoch 130/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1441 - accuracy: 0.9786 - val_loss: 0.1928 - val_accuracy: 0.9646
Epoch 131/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1464 - accuracy: 0.9780 - val_loss: 0.2059 - val_accuracy: 0.9631
Epoch 132/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1423 - accuracy: 0.9787 - val_loss: 0.2376 - val_accuracy: 0.9503
Epoch 133/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1475 - accuracy: 0.9777 - val_loss: 0.1940 - val_accuracy: 0.9643
Epoch 134/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1457 - accuracy: 0.9780 - val_loss: 0.2448 - val_accuracy: 0.9506
Epoch 135/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1451 - accuracy: 0.9779 - val_loss: 0.2208 - val_accuracy: 0.9560
Epoch 136/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1444 - accuracy: 0.9786 - val_loss: 0.2308 - val_accuracy: 0.9522
Epoch 137/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1459 - accuracy: 0.9782 - val_loss: 0.2108 - val_accuracy: 0.9592
Epoch 138/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1415 - accuracy: 0.9792 - val_loss: 0.2005 - val_accuracy: 0.9629
Epoch 139/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1454 - accuracy: 0.9777 - val_loss: 0.2040 - val_accuracy: 0.9606
Epoch 140/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1461 - accuracy: 0.9777 - val_loss: 0.2290 - val_accuracy: 0.9556
Epoch 141/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1450 - accuracy: 0.9783 - val_loss: 0.1960 - val_accuracy: 0.9664
Epoch 142/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.1830 - val_accuracy: 0.9671
Epoch 143/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1406 - accuracy: 0.9795 - val_loss: 0.2100 - val_accuracy: 0.9609
Epoch 144/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1458 - accuracy: 0.9780 - val_loss: 0.1907 - val_accuracy: 0.9653
Epoch 145/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1444 - accuracy: 0.9781 - val_loss: 0.2211 - val_accuracy: 0.9570
Epoch 146/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1418 - accuracy: 0.9788 - val_loss: 0.2140 - val_accuracy: 0.9589
Epoch 147/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1452 - accuracy: 0.9777 - val_loss: 0.2107 - val_accuracy: 0.9584
Epoch 148/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1450 - accuracy: 0.9786 - val_loss: 0.1987 - val_accuracy: 0.9624
Epoch 149/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1453 - accuracy: 0.9776 - val_loss: 0.2217 - val_accuracy: 0.9565
Epoch 150/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1465 - accuracy: 0.9775 - val_loss: 0.2122 - val_accuracy: 0.9593
Epoch 151/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1433 - accuracy: 0.9787 - val_loss: 0.2099 - val_accuracy: 0.9583
Epoch 152/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1429 - accuracy: 0.9781 - val_loss: 0.2721 - val_accuracy: 0.9404
Epoch 153/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1477 - accuracy: 0.9774 - val_loss: 0.1997 - val_accuracy: 0.9591
Epoch 154/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1398 - accuracy: 0.9796 - val_loss: 0.2000 - val_accuracy: 0.9616
Epoch 155/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1426 - accuracy: 0.9780 - val_loss: 0.2272 - val_accuracy: 0.9545
Epoch 156/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1470 - accuracy: 0.9769 - val_loss: 0.1989 - val_accuracy: 0.9646
Epoch 157/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1419 - accuracy: 0.9789 - val_loss: 0.2099 - val_accuracy: 0.9604
Epoch 158/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1427 - accuracy: 0.9789 - val_loss: 0.2022 - val_accuracy: 0.9625
Epoch 159/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1410 - accuracy: 0.9787 - val_loss: 0.1942 - val_accuracy: 0.9684
Epoch 160/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1460 - accuracy: 0.9782 - val_loss: 0.2074 - val_accuracy: 0.9600
Epoch 161/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1408 - accuracy: 0.9788 - val_loss: 0.2110 - val_accuracy: 0.9608
Epoch 162/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1411 - accuracy: 0.9792 - val_loss: 0.1972 - val_accuracy: 0.9615
Epoch 163/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1449 - accuracy: 0.9780 - val_loss: 0.2182 - val_accuracy: 0.9592
Epoch 164/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1439 - accuracy: 0.9785 - val_loss: 0.2106 - val_accuracy: 0.9578
Epoch 165/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1432 - accuracy: 0.9776 - val_loss: 0.2197 - val_accuracy: 0.9569
Epoch 166/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9789 - val_loss: 0.2058 - val_accuracy: 0.9596
Epoch 167/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1414 - accuracy: 0.9784 - val_loss: 0.1894 - val_accuracy: 0.9662
Epoch 168/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1456 - accuracy: 0.9777 - val_loss: 0.2164 - val_accuracy: 0.9581
Epoch 169/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1422 - accuracy: 0.9781 - val_loss: 0.2028 - val_accuracy: 0.9615
Epoch 170/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1446 - accuracy: 0.9779 - val_loss: 0.2243 - val_accuracy: 0.9567
Epoch 171/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1423 - accuracy: 0.9791 - val_loss: 0.1984 - val_accuracy: 0.9622
Epoch 172/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1423 - accuracy: 0.9779 - val_loss: 0.1883 - val_accuracy: 0.9677
Epoch 173/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9785 - val_loss: 0.1981 - val_accuracy: 0.9654
Epoch 174/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1398 - accuracy: 0.9788 - val_loss: 0.2105 - val_accuracy: 0.9567
Epoch 175/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1401 - accuracy: 0.9791 - val_loss: 0.2116 - val_accuracy: 0.9603
Epoch 176/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9802 - val_loss: 0.2492 - val_accuracy: 0.9471
Epoch 177/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1425 - accuracy: 0.9772 - val_loss: 0.1953 - val_accuracy: 0.9653
Epoch 178/200
235/235 [==============================] - 4s 18ms/step - loss: 0.1428 - accuracy: 0.9780 - val_loss: 0.2331 - val_accuracy: 0.9557
Epoch 179/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1432 - accuracy: 0.9785 - val_loss: 0.2046 - val_accuracy: 0.9626
Epoch 180/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1424 - accuracy: 0.9784 - val_loss: 0.1952 - val_accuracy: 0.9649
Epoch 181/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1414 - accuracy: 0.9784 - val_loss: 0.1815 - val_accuracy: 0.9684
Epoch 182/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1398 - accuracy: 0.9786 - val_loss: 0.2220 - val_accuracy: 0.9568
Epoch 183/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2043 - val_accuracy: 0.9631
Epoch 184/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1392 - accuracy: 0.9789 - val_loss: 0.2293 - val_accuracy: 0.9538
Epoch 185/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1390 - accuracy: 0.9795 - val_loss: 0.2020 - val_accuracy: 0.9614
Epoch 186/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1422 - accuracy: 0.9784 - val_loss: 0.1947 - val_accuracy: 0.9643
Epoch 187/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1409 - accuracy: 0.9785 - val_loss: 0.2100 - val_accuracy: 0.9587
Epoch 188/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1402 - accuracy: 0.9789 - val_loss: 0.1805 - val_accuracy: 0.9680
Epoch 189/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1424 - accuracy: 0.9785 - val_loss: 0.2356 - val_accuracy: 0.9497
Epoch 190/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1419 - accuracy: 0.9782 - val_loss: 0.1840 - val_accuracy: 0.9672
Epoch 191/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1449 - accuracy: 0.9776 - val_loss: 0.2659 - val_accuracy: 0.9428
Epoch 192/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1391 - accuracy: 0.9791 - val_loss: 0.1957 - val_accuracy: 0.9618
Epoch 193/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1397 - accuracy: 0.9785 - val_loss: 0.1848 - val_accuracy: 0.9671
Epoch 194/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1403 - accuracy: 0.9783 - val_loss: 0.2444 - val_accuracy: 0.9485
Epoch 195/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1415 - accuracy: 0.9785 - val_loss: 0.2172 - val_accuracy: 0.9553
Epoch 196/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1428 - accuracy: 0.9778 - val_loss: 0.1856 - val_accuracy: 0.9667
Epoch 197/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9808 - val_loss: 0.2033 - val_accuracy: 0.9627
Epoch 198/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1379 - accuracy: 0.9791 - val_loss: 0.2216 - val_accuracy: 0.9544
Epoch 199/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1408 - accuracy: 0.9791 - val_loss: 0.2036 - val_accuracy: 0.9605
Epoch 200/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1381 - accuracy: 0.9796 - val_loss: 0.1780 - val_accuracy: 0.9682
Epoch 1/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2490 - accuracy: 0.9266 - val_loss: 0.2152 - val_accuracy: 0.9551
Epoch 2/200
235/235 [==============================] - 4s 16ms/step - loss: 0.0887 - accuracy: 0.9741 - val_loss: 0.1027 - val_accuracy: 0.9669
Epoch 3/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0507 - accuracy: 0.9859 - val_loss: 0.0945 - val_accuracy: 0.9689
Epoch 4/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0310 - accuracy: 0.9918 - val_loss: 0.0894 - val_accuracy: 0.9728
Epoch 5/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0198 - accuracy: 0.9953 - val_loss: 0.0912 - val_accuracy: 0.9734
Epoch 6/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0133 - accuracy: 0.9971 - val_loss: 0.0863 - val_accuracy: 0.9759
Epoch 7/200
235/235 [==============================] - 4s 17ms/step - loss: 0.0119 - accuracy: 0.9972 - val_loss: 0.0850 - val_accuracy: 0.9780
Epoch 8/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0116 - accuracy: 0.9969 - val_loss: 0.1076 - val_accuracy: 0.9714
Epoch 9/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0100 - accuracy: 0.9972 - val_loss: 0.1027 - val_accuracy: 0.9744
Epoch 10/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0119 - accuracy: 0.9964 - val_loss: 0.0968 - val_accuracy: 0.9742
Epoch 11/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0092 - accuracy: 0.9972 - val_loss: 0.0997 - val_accuracy: 0.9754
Epoch 12/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0083 - accuracy: 0.9974 - val_loss: 0.1004 - val_accuracy: 0.9751
Epoch 13/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0110 - accuracy: 0.9964 - val_loss: 0.1186 - val_accuracy: 0.9720
Epoch 14/200
235/235 [==============================] - 4s 16ms/step - loss: 0.0071 - accuracy: 0.9980 - val_loss: 0.0933 - val_accuracy: 0.9774
Epoch 15/200
235/235 [==============================] - 4s 17ms/step - loss: 0.0039 - accuracy: 0.9991 - val_loss: 0.0837 - val_accuracy: 0.9804
Epoch 16/200
235/235 [==============================] - 4s 16ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0832 - val_accuracy: 0.9807
Epoch 17/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0015 - accuracy: 0.9998 - val_loss: 0.0793 - val_accuracy: 0.9815
Epoch 18/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0765 - val_accuracy: 0.9823
Epoch 19/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0055 - accuracy: 0.9981 - val_loss: 0.1480 - val_accuracy: 0.9669
Epoch 20/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0206 - accuracy: 0.9933 - val_loss: 0.1313 - val_accuracy: 0.9703
Epoch 21/200
235/235 [==============================] - 4s 16ms/step - loss: 0.0128 - accuracy: 0.9955 - val_loss: 0.0908 - val_accuracy: 0.9787
Epoch 22/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0067 - accuracy: 0.9979 - val_loss: 0.0826 - val_accuracy: 0.9789
Epoch 23/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 0.0768 - val_accuracy: 0.9815
Epoch 24/200
235/235 [==============================] - 4s 15ms/step - loss: 9.0077e-04 - accuracy: 0.9999 - val_loss: 0.0708 - val_accuracy: 0.9827
Epoch 25/200
235/235 [==============================] - 3s 15ms/step - loss: 5.2012e-04 - accuracy: 0.9999 - val_loss: 0.0698 - val_accuracy: 0.9838
Epoch 26/200
235/235 [==============================] - 4s 15ms/step - loss: 2.9209e-04 - accuracy: 1.0000 - val_loss: 0.0698 - val_accuracy: 0.9832
Epoch 27/200
235/235 [==============================] - 4s 15ms/step - loss: 1.8796e-04 - accuracy: 1.0000 - val_loss: 0.0701 - val_accuracy: 0.9834
Epoch 28/200
235/235 [==============================] - 4s 17ms/step - loss: 1.2218e-04 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9837
Epoch 29/200
235/235 [==============================] - 4s 17ms/step - loss: 1.0851e-04 - accuracy: 1.0000 - val_loss: 0.0709 - val_accuracy: 0.9834
Epoch 30/200
235/235 [==============================] - 4s 15ms/step - loss: 1.1920e-04 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9839
Epoch 31/200
235/235 [==============================] - 4s 15ms/step - loss: 7.7386e-05 - accuracy: 1.0000 - val_loss: 0.0711 - val_accuracy: 0.9840
Epoch 32/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.1824 - val_accuracy: 0.9625
Epoch 33/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0379 - accuracy: 0.9881 - val_loss: 0.0997 - val_accuracy: 0.9771
Epoch 34/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0112 - accuracy: 0.9962 - val_loss: 0.0787 - val_accuracy: 0.9801
Epoch 35/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0748 - val_accuracy: 0.9821
Epoch 36/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0711 - val_accuracy: 0.9829
Epoch 37/200
235/235 [==============================] - 4s 15ms/step - loss: 5.5592e-04 - accuracy: 0.9999 - val_loss: 0.0709 - val_accuracy: 0.9829
Epoch 38/200
235/235 [==============================] - 4s 15ms/step - loss: 5.5829e-04 - accuracy: 0.9999 - val_loss: 0.0730 - val_accuracy: 0.9828
Epoch 39/200
235/235 [==============================] - 4s 15ms/step - loss: 2.9081e-04 - accuracy: 1.0000 - val_loss: 0.0704 - val_accuracy: 0.9845
Epoch 40/200
235/235 [==============================] - 4s 15ms/step - loss: 2.1149e-04 - accuracy: 1.0000 - val_loss: 0.0719 - val_accuracy: 0.9848
Epoch 41/200
235/235 [==============================] - 4s 15ms/step - loss: 2.5171e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9841
Epoch 42/200
235/235 [==============================] - 4s 15ms/step - loss: 1.6638e-04 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9838
Epoch 43/200
235/235 [==============================] - 4s 15ms/step - loss: 1.2381e-04 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9846
Epoch 44/200
235/235 [==============================] - 4s 16ms/step - loss: 9.4578e-05 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9845
Epoch 45/200
235/235 [==============================] - 4s 16ms/step - loss: 7.8825e-05 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9841
Epoch 46/200
235/235 [==============================] - 4s 15ms/step - loss: 6.1688e-05 - accuracy: 1.0000 - val_loss: 0.0760 - val_accuracy: 0.9843
Epoch 47/200
235/235 [==============================] - 4s 15ms/step - loss: 5.6006e-05 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9837
Epoch 48/200
235/235 [==============================] - 4s 15ms/step - loss: 5.5935e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9843
Epoch 49/200
235/235 [==============================] - 4s 15ms/step - loss: 8.0974e-04 - accuracy: 0.9998 - val_loss: 0.1241 - val_accuracy: 0.9767
Epoch 50/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0337 - accuracy: 0.9897 - val_loss: 0.1049 - val_accuracy: 0.9763
Epoch 51/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0093 - accuracy: 0.9971 - val_loss: 0.0831 - val_accuracy: 0.9812
Epoch 52/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0787 - val_accuracy: 0.9822
Epoch 53/200
235/235 [==============================] - 4s 15ms/step - loss: 7.9964e-04 - accuracy: 0.9999 - val_loss: 0.0780 - val_accuracy: 0.9836
Epoch 54/200
235/235 [==============================] - 4s 15ms/step - loss: 4.2629e-04 - accuracy: 0.9999 - val_loss: 0.0746 - val_accuracy: 0.9835
Epoch 55/200
235/235 [==============================] - 4s 15ms/step - loss: 2.5976e-04 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9837
Epoch 56/200
235/235 [==============================] - 4s 15ms/step - loss: 1.6868e-04 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9839
Epoch 57/200
235/235 [==============================] - 4s 15ms/step - loss: 1.5917e-04 - accuracy: 1.0000 - val_loss: 0.0764 - val_accuracy: 0.9841
Epoch 58/200
235/235 [==============================] - 4s 15ms/step - loss: 1.1439e-04 - accuracy: 1.0000 - val_loss: 0.0763 - val_accuracy: 0.9842
Epoch 59/200
235/235 [==============================] - 4s 15ms/step - loss: 1.0609e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9838
Epoch 60/200
235/235 [==============================] - 4s 15ms/step - loss: 8.9850e-05 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9836
Epoch 61/200
235/235 [==============================] - 4s 15ms/step - loss: 9.2628e-05 - accuracy: 1.0000 - val_loss: 0.0802 - val_accuracy: 0.9829
Epoch 62/200
235/235 [==============================] - 4s 15ms/step - loss: 1.3343e-04 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9827
Epoch 63/200
235/235 [==============================] - 4s 15ms/step - loss: 7.4608e-05 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9831
Epoch 64/200
235/235 [==============================] - 4s 15ms/step - loss: 5.0938e-05 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9828
Epoch 65/200
235/235 [==============================] - 4s 15ms/step - loss: 4.1187e-05 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9829
Epoch 66/200
235/235 [==============================] - 4s 15ms/step - loss: 3.8541e-05 - accuracy: 1.0000 - val_loss: 0.0820 - val_accuracy: 0.9835
Epoch 67/200
235/235 [==============================] - 4s 15ms/step - loss: 3.3755e-05 - accuracy: 1.0000 - val_loss: 0.0824 - val_accuracy: 0.9832
Epoch 68/200
235/235 [==============================] - 4s 15ms/step - loss: 3.1241e-05 - accuracy: 1.0000 - val_loss: 0.0816 - val_accuracy: 0.9840
Epoch 69/200
235/235 [==============================] - 4s 15ms/step - loss: 2.6725e-05 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9840
Epoch 70/200
235/235 [==============================] - 4s 15ms/step - loss: 2.6917e-05 - accuracy: 1.0000 - val_loss: 0.0850 - val_accuracy: 0.9837
Epoch 71/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.2888 - val_accuracy: 0.9523
Epoch 72/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0301 - accuracy: 0.9904 - val_loss: 0.1027 - val_accuracy: 0.9784
Epoch 73/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0065 - accuracy: 0.9978 - val_loss: 0.0820 - val_accuracy: 0.9814
Epoch 74/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0017 - accuracy: 0.9997 - val_loss: 0.0790 - val_accuracy: 0.9823
Epoch 75/200
235/235 [==============================] - 4s 15ms/step - loss: 6.8739e-04 - accuracy: 0.9999 - val_loss: 0.0786 - val_accuracy: 0.9828
Epoch 76/200
235/235 [==============================] - 4s 15ms/step - loss: 3.2982e-04 - accuracy: 1.0000 - val_loss: 0.0779 - val_accuracy: 0.9830
Epoch 77/200
235/235 [==============================] - 4s 16ms/step - loss: 2.3249e-04 - accuracy: 1.0000 - val_loss: 0.0779 - val_accuracy: 0.9832
Epoch 78/200
235/235 [==============================] - 4s 15ms/step - loss: 1.6352e-04 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9826
Epoch 79/200
235/235 [==============================] - 4s 15ms/step - loss: 1.4102e-04 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9832
Epoch 80/200
235/235 [==============================] - 3s 14ms/step - loss: 1.4744e-04 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 0.9833
Epoch 81/200
235/235 [==============================] - 3s 14ms/step - loss: 1.3711e-04 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9835
Epoch 82/200
235/235 [==============================] - 3s 15ms/step - loss: 9.2961e-05 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9834
Epoch 83/200
235/235 [==============================] - 3s 15ms/step - loss: 7.8279e-05 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9837
Epoch 84/200
235/235 [==============================] - 3s 14ms/step - loss: 6.1630e-05 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9839
Epoch 85/200
235/235 [==============================] - 3s 15ms/step - loss: 5.3944e-05 - accuracy: 1.0000 - val_loss: 0.0810 - val_accuracy: 0.9840
Epoch 86/200
235/235 [==============================] - 3s 15ms/step - loss: 4.7073e-05 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9839
Epoch 87/200
235/235 [==============================] - 3s 14ms/step - loss: 6.6588e-05 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9840
Epoch 88/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0061 - accuracy: 0.9981 - val_loss: 0.1814 - val_accuracy: 0.9652
Epoch 89/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0197 - accuracy: 0.9937 - val_loss: 0.0985 - val_accuracy: 0.9778
Epoch 90/200
235/235 [==============================] - 4s 16ms/step - loss: 0.0046 - accuracy: 0.9983 - val_loss: 0.0798 - val_accuracy: 0.9829
Epoch 91/200
235/235 [==============================] - 4s 16ms/step - loss: 9.5145e-04 - accuracy: 0.9998 - val_loss: 0.0747 - val_accuracy: 0.9837
Epoch 92/200
235/235 [==============================] - 4s 16ms/step - loss: 3.2547e-04 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9841
Epoch 93/200
235/235 [==============================] - 3s 15ms/step - loss: 2.0759e-04 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9846
Epoch 94/200
235/235 [==============================] - 3s 14ms/step - loss: 2.1273e-04 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9849
Epoch 95/200
235/235 [==============================] - 3s 14ms/step - loss: 1.6500e-04 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9838
Epoch 96/200
235/235 [==============================] - 3s 15ms/step - loss: 1.8844e-04 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9841
Epoch 97/200
235/235 [==============================] - 4s 15ms/step - loss: 1.4773e-04 - accuracy: 1.0000 - val_loss: 0.0786 - val_accuracy: 0.9837
Epoch 98/200
235/235 [==============================] - 3s 15ms/step - loss: 1.0762e-04 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9844
Epoch 99/200
235/235 [==============================] - 3s 14ms/step - loss: 1.5963e-04 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9837
Epoch 100/200
235/235 [==============================] - 3s 14ms/step - loss: 1.3944e-04 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9839
Epoch 101/200
235/235 [==============================] - 3s 15ms/step - loss: 6.3473e-05 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9837
Epoch 102/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1124 - val_accuracy: 0.9775
Epoch 103/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0097 - accuracy: 0.9972 - val_loss: 0.1407 - val_accuracy: 0.9716
Epoch 104/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.0979 - val_accuracy: 0.9804
Epoch 105/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.0878 - val_accuracy: 0.9819
Epoch 106/200
235/235 [==============================] - 3s 15ms/step - loss: 8.1914e-04 - accuracy: 0.9998 - val_loss: 0.0843 - val_accuracy: 0.9815
Epoch 107/200
235/235 [==============================] - 3s 14ms/step - loss: 3.3032e-04 - accuracy: 0.9999 - val_loss: 0.0839 - val_accuracy: 0.9828
Epoch 108/200
235/235 [==============================] - 3s 15ms/step - loss: 2.1028e-04 - accuracy: 1.0000 - val_loss: 0.0830 - val_accuracy: 0.9837
Epoch 109/200
235/235 [==============================] - 4s 17ms/step - loss: 1.0689e-04 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9840
Epoch 110/200
235/235 [==============================] - 4s 15ms/step - loss: 8.1077e-05 - accuracy: 1.0000 - val_loss: 0.0827 - val_accuracy: 0.9843
Epoch 111/200
235/235 [==============================] - 4s 19ms/step - loss: 5.6977e-05 - accuracy: 1.0000 - val_loss: 0.0826 - val_accuracy: 0.9840
Epoch 112/200
235/235 [==============================] - 4s 16ms/step - loss: 5.2850e-05 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9840
Epoch 113/200
235/235 [==============================] - 4s 15ms/step - loss: 3.5655e-04 - accuracy: 0.9999 - val_loss: 0.0849 - val_accuracy: 0.9832
Epoch 114/200
235/235 [==============================] - 4s 18ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1178 - val_accuracy: 0.9794
Epoch 115/200
235/235 [==============================] - 4s 17ms/step - loss: 0.0059 - accuracy: 0.9982 - val_loss: 0.1190 - val_accuracy: 0.9772
Epoch 116/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.0915 - val_accuracy: 0.9822
Epoch 117/200
235/235 [==============================] - 4s 18ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0907 - val_accuracy: 0.9826
Epoch 118/200
235/235 [==============================] - 4s 18ms/step - loss: 4.1760e-04 - accuracy: 0.9999 - val_loss: 0.0855 - val_accuracy: 0.9836
Epoch 119/200
235/235 [==============================] - 3s 14ms/step - loss: 1.5054e-04 - accuracy: 1.0000 - val_loss: 0.0846 - val_accuracy: 0.9831
Epoch 120/200
235/235 [==============================] - 4s 17ms/step - loss: 8.8659e-05 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9832
Epoch 121/200
235/235 [==============================] - 4s 17ms/step - loss: 5.9028e-05 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9838
Epoch 122/200
235/235 [==============================] - 4s 16ms/step - loss: 4.5024e-05 - accuracy: 1.0000 - val_loss: 0.0845 - val_accuracy: 0.9839
Epoch 123/200
235/235 [==============================] - 4s 15ms/step - loss: 4.1556e-05 - accuracy: 1.0000 - val_loss: 0.0838 - val_accuracy: 0.9842
Epoch 124/200
235/235 [==============================] - 4s 15ms/step - loss: 3.6180e-05 - accuracy: 1.0000 - val_loss: 0.0834 - val_accuracy: 0.9844
Epoch 125/200
235/235 [==============================] - 4s 15ms/step - loss: 2.8184e-05 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9839
Epoch 126/200
235/235 [==============================] - 4s 16ms/step - loss: 2.6983e-05 - accuracy: 1.0000 - val_loss: 0.0850 - val_accuracy: 0.9838
Epoch 127/200
235/235 [==============================] - 4s 15ms/step - loss: 2.2555e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9837
Epoch 128/200
235/235 [==============================] - 4s 15ms/step - loss: 2.1184e-05 - accuracy: 1.0000 - val_loss: 0.0858 - val_accuracy: 0.9836
Epoch 129/200
235/235 [==============================] - 4s 16ms/step - loss: 2.0863e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9841
Epoch 130/200
235/235 [==============================] - 4s 15ms/step - loss: 1.6529e-05 - accuracy: 1.0000 - val_loss: 0.0858 - val_accuracy: 0.9840
Epoch 131/200
235/235 [==============================] - 4s 15ms/step - loss: 1.6085e-05 - accuracy: 1.0000 - val_loss: 0.0853 - val_accuracy: 0.9845
Epoch 132/200
235/235 [==============================] - 4s 18ms/step - loss: 1.3094e-05 - accuracy: 1.0000 - val_loss: 0.0861 - val_accuracy: 0.9840
Epoch 133/200
235/235 [==============================] - 4s 16ms/step - loss: 1.1899e-05 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9843
Epoch 134/200
235/235 [==============================] - 4s 15ms/step - loss: 1.1602e-05 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9844
Epoch 135/200
235/235 [==============================] - 4s 15ms/step - loss: 9.8617e-06 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9842
Epoch 136/200
235/235 [==============================] - 4s 15ms/step - loss: 9.0324e-06 - accuracy: 1.0000 - val_loss: 0.0872 - val_accuracy: 0.9840
Epoch 137/200
235/235 [==============================] - 4s 15ms/step - loss: 8.8402e-06 - accuracy: 1.0000 - val_loss: 0.0887 - val_accuracy: 0.9841
Epoch 138/200
235/235 [==============================] - 4s 15ms/step - loss: 7.5928e-06 - accuracy: 1.0000 - val_loss: 0.0886 - val_accuracy: 0.9847
Epoch 139/200
235/235 [==============================] - 4s 15ms/step - loss: 6.4924e-06 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9840
Epoch 140/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0088 - accuracy: 0.9977 - val_loss: 0.2212 - val_accuracy: 0.9644
Epoch 141/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0146 - accuracy: 0.9955 - val_loss: 0.0902 - val_accuracy: 0.9814
Epoch 142/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0834 - val_accuracy: 0.9826
Epoch 143/200
235/235 [==============================] - 4s 15ms/step - loss: 3.7214e-04 - accuracy: 0.9999 - val_loss: 0.0813 - val_accuracy: 0.9835
Epoch 144/200
235/235 [==============================] - 4s 15ms/step - loss: 1.4315e-04 - accuracy: 1.0000 - val_loss: 0.0808 - val_accuracy: 0.9838
Epoch 145/200
235/235 [==============================] - 4s 15ms/step - loss: 1.0672e-04 - accuracy: 1.0000 - val_loss: 0.0809 - val_accuracy: 0.9834
Epoch 146/200
235/235 [==============================] - 4s 15ms/step - loss: 1.0374e-04 - accuracy: 1.0000 - val_loss: 0.0826 - val_accuracy: 0.9827
Epoch 147/200
235/235 [==============================] - 4s 15ms/step - loss: 7.3333e-05 - accuracy: 1.0000 - val_loss: 0.0817 - val_accuracy: 0.9838
Epoch 148/200
235/235 [==============================] - 4s 15ms/step - loss: 5.7958e-05 - accuracy: 1.0000 - val_loss: 0.0819 - val_accuracy: 0.9837
Epoch 149/200
235/235 [==============================] - 4s 15ms/step - loss: 5.2431e-05 - accuracy: 1.0000 - val_loss: 0.0821 - val_accuracy: 0.9835
Epoch 150/200
235/235 [==============================] - 4s 15ms/step - loss: 4.4060e-05 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9838
Epoch 151/200
235/235 [==============================] - 4s 15ms/step - loss: 4.2560e-05 - accuracy: 1.0000 - val_loss: 0.0828 - val_accuracy: 0.9837
Epoch 152/200
235/235 [==============================] - 4s 15ms/step - loss: 4.5031e-05 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9836
Epoch 153/200
235/235 [==============================] - 4s 15ms/step - loss: 3.1803e-05 - accuracy: 1.0000 - val_loss: 0.0831 - val_accuracy: 0.9841
Epoch 154/200
235/235 [==============================] - 4s 15ms/step - loss: 3.3424e-05 - accuracy: 1.0000 - val_loss: 0.0836 - val_accuracy: 0.9837
Epoch 155/200
235/235 [==============================] - 4s 15ms/step - loss: 5.7255e-04 - accuracy: 0.9999 - val_loss: 0.0916 - val_accuracy: 0.9824
Epoch 156/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.1131 - val_accuracy: 0.9797
Epoch 157/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0082 - accuracy: 0.9975 - val_loss: 0.1134 - val_accuracy: 0.9811
Epoch 158/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0025 - accuracy: 0.9991 - val_loss: 0.0973 - val_accuracy: 0.9820
Epoch 159/200
235/235 [==============================] - 4s 15ms/step - loss: 9.4665e-04 - accuracy: 0.9997 - val_loss: 0.0921 - val_accuracy: 0.9838
Epoch 160/200
235/235 [==============================] - 4s 15ms/step - loss: 2.1171e-04 - accuracy: 1.0000 - val_loss: 0.0887 - val_accuracy: 0.9841
Epoch 161/200
235/235 [==============================] - 4s 15ms/step - loss: 7.9641e-05 - accuracy: 1.0000 - val_loss: 0.0875 - val_accuracy: 0.9840
Epoch 162/200
235/235 [==============================] - 4s 15ms/step - loss: 7.4060e-05 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9842
Epoch 163/200
235/235 [==============================] - 4s 15ms/step - loss: 3.9003e-04 - accuracy: 0.9999 - val_loss: 0.0898 - val_accuracy: 0.9841
Epoch 164/200
235/235 [==============================] - 4s 15ms/step - loss: 2.4947e-04 - accuracy: 0.9999 - val_loss: 0.0881 - val_accuracy: 0.9837
Epoch 165/200
235/235 [==============================] - 4s 15ms/step - loss: 9.7543e-05 - accuracy: 1.0000 - val_loss: 0.0873 - val_accuracy: 0.9847
Epoch 166/200
235/235 [==============================] - 4s 15ms/step - loss: 4.6164e-05 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9843
Epoch 167/200
235/235 [==============================] - 4s 15ms/step - loss: 4.6077e-05 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9847
Epoch 168/200
235/235 [==============================] - 4s 15ms/step - loss: 4.5332e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9846
Epoch 169/200
235/235 [==============================] - 4s 15ms/step - loss: 3.8174e-05 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9842
Epoch 170/200
235/235 [==============================] - 4s 15ms/step - loss: 2.3581e-05 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9840
Epoch 171/200
235/235 [==============================] - 3s 14ms/step - loss: 2.7284e-04 - accuracy: 0.9999 - val_loss: 0.1047 - val_accuracy: 0.9824
Epoch 172/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9986 - val_loss: 0.1344 - val_accuracy: 0.9775
Epoch 173/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0051 - accuracy: 0.9982 - val_loss: 0.1013 - val_accuracy: 0.9811
Epoch 174/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0938 - val_accuracy: 0.9837
Epoch 175/200
235/235 [==============================] - 4s 15ms/step - loss: 2.9594e-04 - accuracy: 0.9999 - val_loss: 0.0903 - val_accuracy: 0.9841
Epoch 176/200
235/235 [==============================] - 4s 15ms/step - loss: 1.0165e-04 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9844
Epoch 177/200
235/235 [==============================] - 4s 15ms/step - loss: 1.7432e-04 - accuracy: 1.0000 - val_loss: 0.0880 - val_accuracy: 0.9848
Epoch 178/200
235/235 [==============================] - 4s 15ms/step - loss: 4.8831e-04 - accuracy: 0.9998 - val_loss: 0.0940 - val_accuracy: 0.9840
Epoch 179/200
235/235 [==============================] - 4s 15ms/step - loss: 4.4488e-04 - accuracy: 0.9999 - val_loss: 0.0905 - val_accuracy: 0.9848
Epoch 180/200
235/235 [==============================] - 4s 15ms/step - loss: 2.1769e-04 - accuracy: 0.9999 - val_loss: 0.0970 - val_accuracy: 0.9839
Epoch 181/200
235/235 [==============================] - 4s 16ms/step - loss: 2.9174e-04 - accuracy: 0.9999 - val_loss: 0.1058 - val_accuracy: 0.9828
Epoch 182/200
235/235 [==============================] - 4s 15ms/step - loss: 2.2263e-04 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9837
Epoch 183/200
235/235 [==============================] - 4s 15ms/step - loss: 9.7502e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9840
Epoch 184/200
235/235 [==============================] - 4s 15ms/step - loss: 6.8196e-04 - accuracy: 0.9999 - val_loss: 0.0926 - val_accuracy: 0.9844
Epoch 185/200
235/235 [==============================] - 3s 13ms/step - loss: 9.0729e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9854
Epoch 186/200
235/235 [==============================] - 4s 15ms/step - loss: 5.3547e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9846
Epoch 187/200
235/235 [==============================] - 4s 15ms/step - loss: 2.5749e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9850
Epoch 188/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.1209 - val_accuracy: 0.9793
Epoch 189/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0063 - accuracy: 0.9982 - val_loss: 0.1085 - val_accuracy: 0.9796
Epoch 190/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1024 - val_accuracy: 0.9829
Epoch 191/200
235/235 [==============================] - 3s 15ms/step - loss: 2.6499e-04 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9826
Epoch 192/200
235/235 [==============================] - 3s 14ms/step - loss: 2.3717e-04 - accuracy: 0.9999 - val_loss: 0.0964 - val_accuracy: 0.9833
Epoch 193/200
235/235 [==============================] - 3s 14ms/step - loss: 6.1717e-05 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9837
Epoch 194/200
235/235 [==============================] - 3s 14ms/step - loss: 4.5216e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9837
Epoch 195/200
235/235 [==============================] - 3s 14ms/step - loss: 3.2486e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9836
Epoch 196/200
235/235 [==============================] - 3s 14ms/step - loss: 2.8874e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9836
Epoch 197/200
235/235 [==============================] - 3s 14ms/step - loss: 1.4463e-04 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9843
Epoch 198/200
235/235 [==============================] - 3s 14ms/step - loss: 4.4490e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9842
Epoch 199/200
235/235 [==============================] - 3s 15ms/step - loss: 2.7753e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9842
Epoch 200/200
235/235 [==============================] - 3s 14ms/step - loss: 1.8208e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9841
Epoch 1/200
235/235 [==============================] - 3s 10ms/step - loss: 1.5573 - accuracy: 0.8554 - val_loss: 0.9210 - val_accuracy: 0.9018
Epoch 2/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8728 - accuracy: 0.8968 - val_loss: 0.8270 - val_accuracy: 0.8996
Epoch 3/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8335 - accuracy: 0.8975 - val_loss: 0.8117 - val_accuracy: 0.8994
Epoch 4/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8236 - accuracy: 0.8975 - val_loss: 0.8050 - val_accuracy: 0.8991
Epoch 5/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8182 - accuracy: 0.8980 - val_loss: 0.8013 - val_accuracy: 0.8991
Epoch 6/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.8982 - val_loss: 0.8000 - val_accuracy: 0.8985
Epoch 7/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8132 - accuracy: 0.8985 - val_loss: 0.7969 - val_accuracy: 0.8995
Epoch 8/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8118 - accuracy: 0.8982 - val_loss: 0.7969 - val_accuracy: 0.8994
Epoch 9/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8107 - accuracy: 0.8989 - val_loss: 0.7948 - val_accuracy: 0.8994
Epoch 10/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8094 - accuracy: 0.8988 - val_loss: 0.7936 - val_accuracy: 0.8997
Epoch 11/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8090 - accuracy: 0.8987 - val_loss: 0.7938 - val_accuracy: 0.8997
Epoch 12/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8085 - accuracy: 0.8985 - val_loss: 0.7933 - val_accuracy: 0.9003
Epoch 13/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8082 - accuracy: 0.8990 - val_loss: 0.7930 - val_accuracy: 0.9002
Epoch 14/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8081 - accuracy: 0.8988 - val_loss: 0.7921 - val_accuracy: 0.9001
Epoch 15/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8079 - accuracy: 0.8990 - val_loss: 0.7907 - val_accuracy: 0.9004
Epoch 16/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8074 - accuracy: 0.8990 - val_loss: 0.7909 - val_accuracy: 0.9003
Epoch 17/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8070 - accuracy: 0.8992 - val_loss: 0.7910 - val_accuracy: 0.9003
Epoch 18/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8068 - accuracy: 0.8992 - val_loss: 0.7896 - val_accuracy: 0.9007
Epoch 19/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8063 - accuracy: 0.8995 - val_loss: 0.7895 - val_accuracy: 0.9012
Epoch 20/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8066 - accuracy: 0.8990 - val_loss: 0.7901 - val_accuracy: 0.9010
Epoch 21/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8063 - accuracy: 0.8990 - val_loss: 0.7888 - val_accuracy: 0.9016
Epoch 22/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8058 - accuracy: 0.8994 - val_loss: 0.7882 - val_accuracy: 0.9012
Epoch 23/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8056 - accuracy: 0.8993 - val_loss: 0.7890 - val_accuracy: 0.9009
Epoch 24/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8057 - accuracy: 0.8991 - val_loss: 0.7888 - val_accuracy: 0.9021
Epoch 25/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8057 - accuracy: 0.8992 - val_loss: 0.7888 - val_accuracy: 0.9017
Epoch 26/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8054 - accuracy: 0.8992 - val_loss: 0.7889 - val_accuracy: 0.9006
Epoch 27/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8054 - accuracy: 0.8994 - val_loss: 0.7890 - val_accuracy: 0.9022
Epoch 28/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8994 - val_loss: 0.7893 - val_accuracy: 0.9008
Epoch 29/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8054 - accuracy: 0.8992 - val_loss: 0.7889 - val_accuracy: 0.9020
Epoch 30/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8997 - val_loss: 0.7883 - val_accuracy: 0.9021
Epoch 31/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8992 - val_loss: 0.7886 - val_accuracy: 0.9013
Epoch 32/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8997 - val_loss: 0.7885 - val_accuracy: 0.9018
Epoch 33/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8995 - val_loss: 0.7883 - val_accuracy: 0.9014
Epoch 34/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8994 - val_loss: 0.7885 - val_accuracy: 0.9011
Epoch 35/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8994 - val_loss: 0.7874 - val_accuracy: 0.9018
Epoch 36/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8998 - val_loss: 0.7884 - val_accuracy: 0.9018
Epoch 37/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8999 - val_loss: 0.7880 - val_accuracy: 0.9016
Epoch 38/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8999 - val_loss: 0.7875 - val_accuracy: 0.9021
Epoch 39/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8998 - val_loss: 0.7873 - val_accuracy: 0.9023
Epoch 40/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8996 - val_loss: 0.7881 - val_accuracy: 0.9027
Epoch 41/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8996 - val_loss: 0.7881 - val_accuracy: 0.9021
Epoch 42/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.9000 - val_loss: 0.7885 - val_accuracy: 0.9023
Epoch 43/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8998 - val_loss: 0.7873 - val_accuracy: 0.9029
Epoch 44/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.8994 - val_loss: 0.7880 - val_accuracy: 0.9025
Epoch 45/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.8999 - val_loss: 0.7865 - val_accuracy: 0.9026
Epoch 46/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.9000 - val_loss: 0.7870 - val_accuracy: 0.9022
Epoch 47/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.9002 - val_loss: 0.7876 - val_accuracy: 0.9030
Epoch 48/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9001 - val_loss: 0.7882 - val_accuracy: 0.9018
Epoch 49/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8997 - val_loss: 0.7871 - val_accuracy: 0.9020
Epoch 50/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.9001 - val_loss: 0.7877 - val_accuracy: 0.9020
Epoch 51/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.9000 - val_loss: 0.7867 - val_accuracy: 0.9024
Epoch 52/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.9003 - val_loss: 0.7868 - val_accuracy: 0.9026
Epoch 53/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.8997 - val_loss: 0.7868 - val_accuracy: 0.9025
Epoch 54/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9002 - val_loss: 0.7870 - val_accuracy: 0.9030
Epoch 55/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8999 - val_loss: 0.7869 - val_accuracy: 0.9030
Epoch 56/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9002 - val_loss: 0.7869 - val_accuracy: 0.9033
Epoch 57/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.9001 - val_loss: 0.7877 - val_accuracy: 0.9021
Epoch 58/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9002 - val_loss: 0.7871 - val_accuracy: 0.9026
Epoch 59/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9005 - val_loss: 0.7874 - val_accuracy: 0.9036
Epoch 60/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.9000 - val_loss: 0.7873 - val_accuracy: 0.9032
Epoch 61/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9000 - val_loss: 0.7864 - val_accuracy: 0.9025
Epoch 62/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9001 - val_loss: 0.7872 - val_accuracy: 0.9020
Epoch 63/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9003 - val_loss: 0.7876 - val_accuracy: 0.9028
Epoch 64/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9002 - val_loss: 0.7870 - val_accuracy: 0.9021
Epoch 65/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9005 - val_loss: 0.7872 - val_accuracy: 0.9031
Epoch 66/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9004 - val_loss: 0.7873 - val_accuracy: 0.9028
Epoch 67/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9006 - val_loss: 0.7868 - val_accuracy: 0.9026
Epoch 68/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9005 - val_loss: 0.7861 - val_accuracy: 0.9032
Epoch 69/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9005 - val_loss: 0.7869 - val_accuracy: 0.9032
Epoch 70/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7880 - val_accuracy: 0.9029
Epoch 71/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9002 - val_loss: 0.7878 - val_accuracy: 0.9029
Epoch 72/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8041 - accuracy: 0.9006 - val_loss: 0.7864 - val_accuracy: 0.9031
Epoch 73/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8041 - accuracy: 0.9005 - val_loss: 0.7868 - val_accuracy: 0.9030
Epoch 74/200
235/235 [==============================] - 3s 11ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7869 - val_accuracy: 0.9024
Epoch 75/200
235/235 [==============================] - 3s 12ms/step - loss: 0.8039 - accuracy: 0.9005 - val_loss: 0.7870 - val_accuracy: 0.9030
Epoch 76/200
235/235 [==============================] - 3s 11ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7863 - val_accuracy: 0.9032
Epoch 77/200
235/235 [==============================] - 3s 12ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9024
Epoch 78/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7867 - val_accuracy: 0.9029
Epoch 79/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8040 - accuracy: 0.9006 - val_loss: 0.7875 - val_accuracy: 0.9031
Epoch 80/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7884 - val_accuracy: 0.9031
Epoch 81/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9031
Epoch 82/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9006 - val_loss: 0.7867 - val_accuracy: 0.9032
Epoch 83/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7875 - val_accuracy: 0.9026
Epoch 84/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7875 - val_accuracy: 0.9028
Epoch 85/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7873 - val_accuracy: 0.9036
Epoch 86/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9009 - val_loss: 0.7871 - val_accuracy: 0.9034
Epoch 87/200
235/235 [==============================] - 3s 11ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7873 - val_accuracy: 0.9032
Epoch 88/200
235/235 [==============================] - 3s 13ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7871 - val_accuracy: 0.9034
Epoch 89/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7865 - val_accuracy: 0.9034
Epoch 90/200
235/235 [==============================] - 3s 11ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9029
Epoch 91/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7865 - val_accuracy: 0.9031
Epoch 92/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9035
Epoch 93/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7867 - val_accuracy: 0.9030
Epoch 94/200
235/235 [==============================] - 3s 11ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7872 - val_accuracy: 0.9033
Epoch 95/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7872 - val_accuracy: 0.9033
Epoch 96/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7869 - val_accuracy: 0.9032
Epoch 97/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8039 - accuracy: 0.9009 - val_loss: 0.7861 - val_accuracy: 0.9040
Epoch 98/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9029
Epoch 99/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7868 - val_accuracy: 0.9032
Epoch 100/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7873 - val_accuracy: 0.9032
Epoch 101/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7871 - val_accuracy: 0.9038
Epoch 102/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7869 - val_accuracy: 0.9031
Epoch 103/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7872 - val_accuracy: 0.9031
Epoch 104/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7869 - val_accuracy: 0.9030
Epoch 105/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7869 - val_accuracy: 0.9029
Epoch 106/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7865 - val_accuracy: 0.9029
Epoch 107/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7871 - val_accuracy: 0.9032
Epoch 108/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7869 - val_accuracy: 0.9039
Epoch 109/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9026
Epoch 110/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9028
Epoch 111/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7873 - val_accuracy: 0.9038
Epoch 112/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7867 - val_accuracy: 0.9034
Epoch 113/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7863 - val_accuracy: 0.9036
Epoch 114/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9029
Epoch 115/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7869 - val_accuracy: 0.9028
Epoch 116/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9021
Epoch 117/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7869 - val_accuracy: 0.9034
Epoch 118/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7882 - val_accuracy: 0.9025
Epoch 119/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7862 - val_accuracy: 0.9038
Epoch 120/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7863 - val_accuracy: 0.9036
Epoch 121/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7866 - val_accuracy: 0.9031
Epoch 122/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7871 - val_accuracy: 0.9026
Epoch 123/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9033
Epoch 124/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7867 - val_accuracy: 0.9028
Epoch 125/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9028
Epoch 126/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9028
Epoch 127/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9029
Epoch 128/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9028
Epoch 129/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7871 - val_accuracy: 0.9029
Epoch 130/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9012 - val_loss: 0.7869 - val_accuracy: 0.9029
Epoch 131/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9030
Epoch 132/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9031
Epoch 133/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7876 - val_accuracy: 0.9033
Epoch 134/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7871 - val_accuracy: 0.9029
Epoch 135/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9008 - val_loss: 0.7871 - val_accuracy: 0.9025
Epoch 136/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9033
Epoch 137/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7865 - val_accuracy: 0.9030
Epoch 138/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7858 - val_accuracy: 0.9040
Epoch 139/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7865 - val_accuracy: 0.9031
Epoch 140/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7865 - val_accuracy: 0.9031
Epoch 141/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9033
Epoch 142/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7873 - val_accuracy: 0.9030
Epoch 143/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9028
Epoch 144/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7868 - val_accuracy: 0.9032
Epoch 145/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7869 - val_accuracy: 0.9026
Epoch 146/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7863 - val_accuracy: 0.9030
Epoch 147/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9023
Epoch 148/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8030 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9031
Epoch 149/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9025
Epoch 150/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7867 - val_accuracy: 0.9028
Epoch 151/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7867 - val_accuracy: 0.9030
Epoch 152/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9029
Epoch 153/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9034
Epoch 154/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7867 - val_accuracy: 0.9032
Epoch 155/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7868 - val_accuracy: 0.9042
Epoch 156/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9030
Epoch 157/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9032
Epoch 158/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7867 - val_accuracy: 0.9029
Epoch 159/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7870 - val_accuracy: 0.9033
Epoch 160/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9029
Epoch 161/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9027
Epoch 162/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9029
Epoch 163/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9028
Epoch 164/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9034
Epoch 165/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7870 - val_accuracy: 0.9035
Epoch 166/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7871 - val_accuracy: 0.9039
Epoch 167/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7869 - val_accuracy: 0.9032
Epoch 168/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7870 - val_accuracy: 0.9035
Epoch 169/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7883 - val_accuracy: 0.9027
Epoch 170/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9030
Epoch 171/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9033
Epoch 172/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9024
Epoch 173/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7868 - val_accuracy: 0.9035
Epoch 174/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9030
Epoch 175/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7870 - val_accuracy: 0.9029
Epoch 176/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9033
Epoch 177/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7871 - val_accuracy: 0.9022
Epoch 178/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7869 - val_accuracy: 0.9031
Epoch 179/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7868 - val_accuracy: 0.9032
Epoch 180/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9029
Epoch 181/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7863 - val_accuracy: 0.9033
Epoch 182/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7875 - val_accuracy: 0.9039
Epoch 183/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9034
Epoch 184/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7868 - val_accuracy: 0.9030
Epoch 185/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9033
Epoch 186/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9031
Epoch 187/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7874 - val_accuracy: 0.9038
Epoch 188/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9033
Epoch 189/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7864 - val_accuracy: 0.9029
Epoch 190/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9036
Epoch 191/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9032
Epoch 192/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9035
Epoch 193/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8031 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9035
Epoch 194/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7877 - val_accuracy: 0.9032
Epoch 195/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9030
Epoch 196/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9037
Epoch 197/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9028
Epoch 198/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9028
Epoch 199/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9032
Epoch 200/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8031 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9032
Epoch 1/200
235/235 [==============================] - 3s 10ms/step - loss: 0.4704 - accuracy: 0.8695 - val_loss: 0.2471 - val_accuracy: 0.9290
Epoch 2/200
235/235 [==============================] - 2s 10ms/step - loss: 0.2262 - accuracy: 0.9347 - val_loss: 0.1815 - val_accuracy: 0.9472
Epoch 3/200
235/235 [==============================] - 2s 9ms/step - loss: 0.1690 - accuracy: 0.9512 - val_loss: 0.1488 - val_accuracy: 0.9564
Epoch 4/200
235/235 [==============================] - 2s 10ms/step - loss: 0.1341 - accuracy: 0.9609 - val_loss: 0.1292 - val_accuracy: 0.9615
Epoch 5/200
235/235 [==============================] - 2s 9ms/step - loss: 0.1104 - accuracy: 0.9674 - val_loss: 0.1171 - val_accuracy: 0.9649
Epoch 6/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0924 - accuracy: 0.9730 - val_loss: 0.1098 - val_accuracy: 0.9663
Epoch 7/200
235/235 [==============================] - 2s 10ms/step - loss: 0.0784 - accuracy: 0.9773 - val_loss: 0.1055 - val_accuracy: 0.9662
Epoch 8/200
235/235 [==============================] - 2s 10ms/step - loss: 0.0673 - accuracy: 0.9806 - val_loss: 0.1017 - val_accuracy: 0.9677
Epoch 9/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0580 - accuracy: 0.9837 - val_loss: 0.1005 - val_accuracy: 0.9684
Epoch 10/200
235/235 [==============================] - 2s 10ms/step - loss: 0.0503 - accuracy: 0.9861 - val_loss: 0.0994 - val_accuracy: 0.9695
Epoch 11/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0439 - accuracy: 0.9883 - val_loss: 0.0981 - val_accuracy: 0.9704
Epoch 12/200
235/235 [==============================] - 2s 10ms/step - loss: 0.0378 - accuracy: 0.9902 - val_loss: 0.0991 - val_accuracy: 0.9708
Epoch 13/200
235/235 [==============================] - 2s 10ms/step - loss: 0.0328 - accuracy: 0.9921 - val_loss: 0.0990 - val_accuracy: 0.9715
Epoch 14/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0282 - accuracy: 0.9934 - val_loss: 0.0988 - val_accuracy: 0.9725
Epoch 15/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0242 - accuracy: 0.9947 - val_loss: 0.1004 - val_accuracy: 0.9727
Epoch 16/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0207 - accuracy: 0.9956 - val_loss: 0.0999 - val_accuracy: 0.9730
Epoch 17/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.1040 - val_accuracy: 0.9728
Epoch 18/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0154 - accuracy: 0.9974 - val_loss: 0.1061 - val_accuracy: 0.9723
Epoch 19/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0137 - accuracy: 0.9979 - val_loss: 0.1100 - val_accuracy: 0.9729
Epoch 20/200
235/235 [==============================] - 2s 10ms/step - loss: 0.0122 - accuracy: 0.9981 - val_loss: 0.1138 - val_accuracy: 0.9732
Epoch 21/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0112 - accuracy: 0.9981 - val_loss: 0.1104 - val_accuracy: 0.9747
Epoch 22/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0107 - accuracy: 0.9981 - val_loss: 0.1074 - val_accuracy: 0.9762
Epoch 23/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0111 - accuracy: 0.9973 - val_loss: 0.1125 - val_accuracy: 0.9759
Epoch 24/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0108 - accuracy: 0.9970 - val_loss: 0.1102 - val_accuracy: 0.9762
Epoch 25/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0100 - accuracy: 0.9972 - val_loss: 0.1140 - val_accuracy: 0.9752
Epoch 26/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0098 - accuracy: 0.9974 - val_loss: 0.1259 - val_accuracy: 0.9734
Epoch 27/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0080 - accuracy: 0.9979 - val_loss: 0.1358 - val_accuracy: 0.9710
Epoch 28/200
235/235 [==============================] - 2s 10ms/step - loss: 0.0065 - accuracy: 0.9984 - val_loss: 0.1181 - val_accuracy: 0.9751
Epoch 29/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0048 - accuracy: 0.9991 - val_loss: 0.1120 - val_accuracy: 0.9754
Epoch 30/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0039 - accuracy: 0.9995 - val_loss: 0.1284 - val_accuracy: 0.9725
Epoch 31/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0028 - accuracy: 0.9997 - val_loss: 0.1235 - val_accuracy: 0.9744
Epoch 32/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.1201 - val_accuracy: 0.9767
Epoch 33/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 0.1248 - val_accuracy: 0.9750
Epoch 34/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0019 - accuracy: 0.9998 - val_loss: 0.1256 - val_accuracy: 0.9764
Epoch 35/200
235/235 [==============================] - 2s 10ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9763
Epoch 36/200
235/235 [==============================] - 2s 10ms/step - loss: 9.9739e-04 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9770
Epoch 37/200
235/235 [==============================] - 2s 9ms/step - loss: 7.7755e-04 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9772
Epoch 38/200
235/235 [==============================] - 2s 9ms/step - loss: 6.3688e-04 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9773
Epoch 39/200
235/235 [==============================] - 2s 10ms/step - loss: 5.1745e-04 - accuracy: 1.0000 - val_loss: 0.1247 - val_accuracy: 0.9770
Epoch 40/200
235/235 [==============================] - 2s 9ms/step - loss: 4.3214e-04 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9776
Epoch 41/200
235/235 [==============================] - 2s 9ms/step - loss: 3.7065e-04 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9777
Epoch 42/200
235/235 [==============================] - 2s 9ms/step - loss: 3.1979e-04 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9776
Epoch 43/200
235/235 [==============================] - 2s 9ms/step - loss: 2.7523e-04 - accuracy: 1.0000 - val_loss: 0.1279 - val_accuracy: 0.9778
Epoch 44/200
235/235 [==============================] - 2s 9ms/step - loss: 2.3767e-04 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9776
Epoch 45/200
235/235 [==============================] - 2s 9ms/step - loss: 2.0828e-04 - accuracy: 1.0000 - val_loss: 0.1312 - val_accuracy: 0.9776
Epoch 46/200
235/235 [==============================] - 2s 9ms/step - loss: 1.8280e-04 - accuracy: 1.0000 - val_loss: 0.1330 - val_accuracy: 0.9774
Epoch 47/200
235/235 [==============================] - 2s 9ms/step - loss: 1.6160e-04 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9772
Epoch 48/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4284e-04 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9772
Epoch 49/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2653e-04 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9774
Epoch 50/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1231e-04 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9772
Epoch 51/200
235/235 [==============================] - 2s 9ms/step - loss: 9.9600e-05 - accuracy: 1.0000 - val_loss: 0.1406 - val_accuracy: 0.9772
Epoch 52/200
235/235 [==============================] - 2s 9ms/step - loss: 8.7962e-05 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9773
Epoch 53/200
235/235 [==============================] - 2s 9ms/step - loss: 7.8065e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9772
Epoch 54/200
235/235 [==============================] - 2s 9ms/step - loss: 6.9024e-05 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9771
Epoch 55/200
235/235 [==============================] - 2s 9ms/step - loss: 6.1254e-05 - accuracy: 1.0000 - val_loss: 0.1472 - val_accuracy: 0.9769
Epoch 56/200
235/235 [==============================] - 2s 9ms/step - loss: 5.4070e-05 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9772
Epoch 57/200
235/235 [==============================] - 2s 9ms/step - loss: 4.7747e-05 - accuracy: 1.0000 - val_loss: 0.1505 - val_accuracy: 0.9771
Epoch 58/200
235/235 [==============================] - 2s 9ms/step - loss: 4.2182e-05 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9772
Epoch 59/200
235/235 [==============================] - 2s 9ms/step - loss: 3.7287e-05 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9772
Epoch 60/200
235/235 [==============================] - 2s 9ms/step - loss: 3.2869e-05 - accuracy: 1.0000 - val_loss: 0.1556 - val_accuracy: 0.9772
Epoch 61/200
235/235 [==============================] - 2s 9ms/step - loss: 2.9063e-05 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9771
Epoch 62/200
235/235 [==============================] - 2s 9ms/step - loss: 2.5500e-05 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9773
Epoch 63/200
235/235 [==============================] - 2s 9ms/step - loss: 2.2495e-05 - accuracy: 1.0000 - val_loss: 0.1608 - val_accuracy: 0.9773
Epoch 64/200
235/235 [==============================] - 2s 9ms/step - loss: 1.9868e-05 - accuracy: 1.0000 - val_loss: 0.1628 - val_accuracy: 0.9771
Epoch 65/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7457e-05 - accuracy: 1.0000 - val_loss: 0.1644 - val_accuracy: 0.9772
Epoch 66/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5375e-05 - accuracy: 1.0000 - val_loss: 0.1662 - val_accuracy: 0.9775
Epoch 67/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3563e-05 - accuracy: 1.0000 - val_loss: 0.1679 - val_accuracy: 0.9775
Epoch 68/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1917e-05 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9776
Epoch 69/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0508e-05 - accuracy: 1.0000 - val_loss: 0.1715 - val_accuracy: 0.9774
Epoch 70/200
235/235 [==============================] - 2s 9ms/step - loss: 9.2394e-06 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9777
Epoch 71/200
235/235 [==============================] - 2s 9ms/step - loss: 8.1362e-06 - accuracy: 1.0000 - val_loss: 0.1749 - val_accuracy: 0.9776
Epoch 72/200
235/235 [==============================] - 2s 9ms/step - loss: 7.1619e-06 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9775
Epoch 73/200
235/235 [==============================] - 2s 9ms/step - loss: 6.3073e-06 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9777
Epoch 74/200
235/235 [==============================] - 2s 9ms/step - loss: 5.5540e-06 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9776
Epoch 75/200
235/235 [==============================] - 2s 9ms/step - loss: 4.8807e-06 - accuracy: 1.0000 - val_loss: 0.1817 - val_accuracy: 0.9775
Epoch 76/200
235/235 [==============================] - 2s 9ms/step - loss: 4.3057e-06 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9776
Epoch 77/200
235/235 [==============================] - 2s 9ms/step - loss: 3.7866e-06 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9776
Epoch 78/200
235/235 [==============================] - 2s 9ms/step - loss: 3.3445e-06 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9774
Epoch 79/200
235/235 [==============================] - 2s 9ms/step - loss: 2.9457e-06 - accuracy: 1.0000 - val_loss: 0.1886 - val_accuracy: 0.9775
Epoch 80/200
235/235 [==============================] - 2s 9ms/step - loss: 2.5947e-06 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9774
Epoch 81/200
235/235 [==============================] - 2s 9ms/step - loss: 2.2864e-06 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9775
Epoch 82/200
235/235 [==============================] - 2s 9ms/step - loss: 2.0161e-06 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9775
Epoch 83/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7798e-06 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9774
Epoch 84/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5695e-06 - accuracy: 1.0000 - val_loss: 0.1971 - val_accuracy: 0.9774
Epoch 85/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3889e-06 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9772
Epoch 86/200
235/235 [==============================] - 2s 10ms/step - loss: 1.2239e-06 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9772
Epoch 87/200
235/235 [==============================] - 2s 10ms/step - loss: 1.0846e-06 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9772
Epoch 88/200
235/235 [==============================] - 2s 9ms/step - loss: 9.5878e-07 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9772
Epoch 89/200
235/235 [==============================] - 2s 9ms/step - loss: 8.5063e-07 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9773
Epoch 90/200
235/235 [==============================] - 2s 9ms/step - loss: 7.5222e-07 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9773
Epoch 91/200
235/235 [==============================] - 2s 9ms/step - loss: 6.6803e-07 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9773
Epoch 92/200
235/235 [==============================] - 2s 9ms/step - loss: 5.9362e-07 - accuracy: 1.0000 - val_loss: 0.2100 - val_accuracy: 0.9773
Epoch 93/200
235/235 [==============================] - 2s 9ms/step - loss: 5.2587e-07 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9772
Epoch 94/200
235/235 [==============================] - 2s 9ms/step - loss: 4.6713e-07 - accuracy: 1.0000 - val_loss: 0.2131 - val_accuracy: 0.9772
Epoch 95/200
235/235 [==============================] - 2s 9ms/step - loss: 4.1617e-07 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9773
Epoch 96/200
235/235 [==============================] - 2s 9ms/step - loss: 3.7071e-07 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 0.9773
Epoch 97/200
235/235 [==============================] - 2s 8ms/step - loss: 3.3102e-07 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9773
Epoch 98/200
235/235 [==============================] - 2s 9ms/step - loss: 2.9511e-07 - accuracy: 1.0000 - val_loss: 0.2190 - val_accuracy: 0.9773
Epoch 99/200
235/235 [==============================] - 2s 9ms/step - loss: 2.6466e-07 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9773
Epoch 100/200
235/235 [==============================] - 2s 9ms/step - loss: 2.3678e-07 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9773
Epoch 101/200
235/235 [==============================] - 2s 9ms/step - loss: 2.1255e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9774
Epoch 102/200
235/235 [==============================] - 2s 8ms/step - loss: 1.9094e-07 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9776
Epoch 103/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7185e-07 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9775
Epoch 104/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5509e-07 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9775
Epoch 105/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4000e-07 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 0.9776
Epoch 106/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2681e-07 - accuracy: 1.0000 - val_loss: 0.2298 - val_accuracy: 0.9775
Epoch 107/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1556e-07 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9775
Epoch 108/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0466e-07 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9775
Epoch 109/200
235/235 [==============================] - 2s 9ms/step - loss: 9.5405e-08 - accuracy: 1.0000 - val_loss: 0.2334 - val_accuracy: 0.9776
Epoch 110/200
235/235 [==============================] - 2s 9ms/step - loss: 8.7096e-08 - accuracy: 1.0000 - val_loss: 0.2343 - val_accuracy: 0.9775
Epoch 111/200
235/235 [==============================] - 2s 9ms/step - loss: 7.9731e-08 - accuracy: 1.0000 - val_loss: 0.2355 - val_accuracy: 0.9775
Epoch 112/200
235/235 [==============================] - 2s 9ms/step - loss: 7.3077e-08 - accuracy: 1.0000 - val_loss: 0.2364 - val_accuracy: 0.9775
Epoch 113/200
235/235 [==============================] - 2s 9ms/step - loss: 6.7186e-08 - accuracy: 1.0000 - val_loss: 0.2375 - val_accuracy: 0.9775
Epoch 114/200
235/235 [==============================] - 2s 8ms/step - loss: 6.2054e-08 - accuracy: 1.0000 - val_loss: 0.2384 - val_accuracy: 0.9776
Epoch 115/200
235/235 [==============================] - 2s 9ms/step - loss: 5.7270e-08 - accuracy: 1.0000 - val_loss: 0.2394 - val_accuracy: 0.9776
Epoch 116/200
235/235 [==============================] - 2s 9ms/step - loss: 5.2937e-08 - accuracy: 1.0000 - val_loss: 0.2402 - val_accuracy: 0.9776
Epoch 117/200
235/235 [==============================] - 2s 9ms/step - loss: 4.9106e-08 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9777
Epoch 118/200
235/235 [==============================] - 2s 9ms/step - loss: 4.5886e-08 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9777
Epoch 119/200
235/235 [==============================] - 2s 9ms/step - loss: 4.2740e-08 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9777
Epoch 120/200
235/235 [==============================] - 2s 9ms/step - loss: 4.0009e-08 - accuracy: 1.0000 - val_loss: 0.2437 - val_accuracy: 0.9776
Epoch 121/200
235/235 [==============================] - 2s 9ms/step - loss: 3.7563e-08 - accuracy: 1.0000 - val_loss: 0.2445 - val_accuracy: 0.9776
Epoch 122/200
235/235 [==============================] - 2s 9ms/step - loss: 3.5097e-08 - accuracy: 1.0000 - val_loss: 0.2452 - val_accuracy: 0.9775
Epoch 123/200
235/235 [==============================] - 2s 9ms/step - loss: 3.3055e-08 - accuracy: 1.0000 - val_loss: 0.2460 - val_accuracy: 0.9775
Epoch 124/200
235/235 [==============================] - 2s 10ms/step - loss: 3.1187e-08 - accuracy: 1.0000 - val_loss: 0.2467 - val_accuracy: 0.9775
Epoch 125/200
235/235 [==============================] - 2s 9ms/step - loss: 2.9596e-08 - accuracy: 1.0000 - val_loss: 0.2473 - val_accuracy: 0.9774
Epoch 126/200
235/235 [==============================] - 2s 8ms/step - loss: 2.8016e-08 - accuracy: 1.0000 - val_loss: 0.2479 - val_accuracy: 0.9774
Epoch 127/200
235/235 [==============================] - 2s 9ms/step - loss: 2.6528e-08 - accuracy: 1.0000 - val_loss: 0.2486 - val_accuracy: 0.9773
Epoch 128/200
235/235 [==============================] - 2s 9ms/step - loss: 2.5205e-08 - accuracy: 1.0000 - val_loss: 0.2491 - val_accuracy: 0.9773
Epoch 129/200
235/235 [==============================] - 2s 9ms/step - loss: 2.3901e-08 - accuracy: 1.0000 - val_loss: 0.2498 - val_accuracy: 0.9773
Epoch 130/200
235/235 [==============================] - 2s 9ms/step - loss: 2.2854e-08 - accuracy: 1.0000 - val_loss: 0.2504 - val_accuracy: 0.9772
Epoch 131/200
235/235 [==============================] - 2s 9ms/step - loss: 2.1787e-08 - accuracy: 1.0000 - val_loss: 0.2509 - val_accuracy: 0.9772
Epoch 132/200
235/235 [==============================] - 2s 9ms/step - loss: 2.0754e-08 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9774
Epoch 133/200
235/235 [==============================] - 2s 9ms/step - loss: 1.9942e-08 - accuracy: 1.0000 - val_loss: 0.2520 - val_accuracy: 0.9774
Epoch 134/200
235/235 [==============================] - 2s 9ms/step - loss: 1.9111e-08 - accuracy: 1.0000 - val_loss: 0.2525 - val_accuracy: 0.9774
Epoch 135/200
235/235 [==============================] - 2s 9ms/step - loss: 1.8366e-08 - accuracy: 1.0000 - val_loss: 0.2531 - val_accuracy: 0.9775
Epoch 136/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7627e-08 - accuracy: 1.0000 - val_loss: 0.2535 - val_accuracy: 0.9775
Epoch 137/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7001e-08 - accuracy: 1.0000 - val_loss: 0.2540 - val_accuracy: 0.9776
Epoch 138/200
235/235 [==============================] - 2s 9ms/step - loss: 1.6312e-08 - accuracy: 1.0000 - val_loss: 0.2544 - val_accuracy: 0.9776
Epoch 139/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5795e-08 - accuracy: 1.0000 - val_loss: 0.2550 - val_accuracy: 0.9776
Epoch 140/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5302e-08 - accuracy: 1.0000 - val_loss: 0.2554 - val_accuracy: 0.9775
Epoch 141/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4802e-08 - accuracy: 1.0000 - val_loss: 0.2560 - val_accuracy: 0.9774
Epoch 142/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4351e-08 - accuracy: 1.0000 - val_loss: 0.2564 - val_accuracy: 0.9775
Epoch 143/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3938e-08 - accuracy: 1.0000 - val_loss: 0.2569 - val_accuracy: 0.9774
Epoch 144/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3526e-08 - accuracy: 1.0000 - val_loss: 0.2573 - val_accuracy: 0.9774
Epoch 145/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3093e-08 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9775
Epoch 146/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2690e-08 - accuracy: 1.0000 - val_loss: 0.2581 - val_accuracy: 0.9775
Epoch 147/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2292e-08 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9775
Epoch 148/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1973e-08 - accuracy: 1.0000 - val_loss: 0.2588 - val_accuracy: 0.9775
Epoch 149/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1663e-08 - accuracy: 1.0000 - val_loss: 0.2592 - val_accuracy: 0.9775
Epoch 150/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1327e-08 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9776
Epoch 151/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1011e-08 - accuracy: 1.0000 - val_loss: 0.2598 - val_accuracy: 0.9776
Epoch 152/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0677e-08 - accuracy: 1.0000 - val_loss: 0.2601 - val_accuracy: 0.9775
Epoch 153/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0415e-08 - accuracy: 1.0000 - val_loss: 0.2604 - val_accuracy: 0.9775
Epoch 154/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0234e-08 - accuracy: 1.0000 - val_loss: 0.2608 - val_accuracy: 0.9775
Epoch 155/200
235/235 [==============================] - 2s 9ms/step - loss: 9.9003e-09 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9776
Epoch 156/200
235/235 [==============================] - 2s 9ms/step - loss: 9.7434e-09 - accuracy: 1.0000 - val_loss: 0.2612 - val_accuracy: 0.9777
Epoch 157/200
235/235 [==============================] - 2s 10ms/step - loss: 9.4672e-09 - accuracy: 1.0000 - val_loss: 0.2614 - val_accuracy: 0.9776
Epoch 158/200
235/235 [==============================] - 2s 10ms/step - loss: 9.2049e-09 - accuracy: 1.0000 - val_loss: 0.2617 - val_accuracy: 0.9776
Epoch 159/200
235/235 [==============================] - 2s 10ms/step - loss: 9.0381e-09 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9777
Epoch 160/200
235/235 [==============================] - 2s 9ms/step - loss: 8.8394e-09 - accuracy: 1.0000 - val_loss: 0.2621 - val_accuracy: 0.9777
Epoch 161/200
235/235 [==============================] - 2s 9ms/step - loss: 8.6983e-09 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9777
Epoch 162/200
235/235 [==============================] - 2s 9ms/step - loss: 8.4599e-09 - accuracy: 1.0000 - val_loss: 0.2626 - val_accuracy: 0.9776
Epoch 163/200
235/235 [==============================] - 2s 9ms/step - loss: 8.2751e-09 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9777
Epoch 164/200
235/235 [==============================] - 2s 9ms/step - loss: 8.0844e-09 - accuracy: 1.0000 - val_loss: 0.2630 - val_accuracy: 0.9776
Epoch 165/200
235/235 [==============================] - 2s 9ms/step - loss: 7.9354e-09 - accuracy: 1.0000 - val_loss: 0.2631 - val_accuracy: 0.9776
Epoch 166/200
235/235 [==============================] - 2s 9ms/step - loss: 7.7645e-09 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9775
Epoch 167/200
235/235 [==============================] - 2s 9ms/step - loss: 7.6274e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9774
Epoch 168/200
235/235 [==============================] - 2s 9ms/step - loss: 7.4347e-09 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9774
Epoch 169/200
235/235 [==============================] - 2s 9ms/step - loss: 7.2956e-09 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9774
Epoch 170/200
235/235 [==============================] - 2s 9ms/step - loss: 7.1704e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9774
Epoch 171/200
235/235 [==============================] - 2s 9ms/step - loss: 7.0035e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9776
Epoch 172/200
235/235 [==============================] - 2s 9ms/step - loss: 6.9062e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9776
Epoch 173/200
235/235 [==============================] - 2s 10ms/step - loss: 6.7671e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9775
Epoch 174/200
235/235 [==============================] - 2s 9ms/step - loss: 6.6102e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9775
Epoch 175/200
235/235 [==============================] - 2s 9ms/step - loss: 6.4810e-09 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9775
Epoch 176/200
235/235 [==============================] - 2s 9ms/step - loss: 6.3697e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9773
Epoch 177/200
235/235 [==============================] - 2s 9ms/step - loss: 6.2446e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9773
Epoch 178/200
235/235 [==============================] - 2s 9ms/step - loss: 6.1611e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9773
Epoch 179/200
235/235 [==============================] - 2s 9ms/step - loss: 6.0598e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9775
Epoch 180/200
235/235 [==============================] - 2s 9ms/step - loss: 5.9783e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9775
Epoch 181/200
235/235 [==============================] - 2s 9ms/step - loss: 5.8651e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9775
Epoch 182/200
235/235 [==============================] - 2s 9ms/step - loss: 5.7459e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9775
Epoch 183/200
235/235 [==============================] - 2s 9ms/step - loss: 5.6525e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9775
Epoch 184/200
235/235 [==============================] - 2s 9ms/step - loss: 5.5691e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9775
Epoch 185/200
235/235 [==============================] - 2s 9ms/step - loss: 5.4995e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9775
Epoch 186/200
235/235 [==============================] - 2s 9ms/step - loss: 5.4061e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9776
Epoch 187/200
235/235 [==============================] - 2s 9ms/step - loss: 5.3326e-09 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9775
Epoch 188/200
235/235 [==============================] - 2s 9ms/step - loss: 5.2373e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9776
Epoch 189/200
235/235 [==============================] - 2s 9ms/step - loss: 5.1399e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9776
Epoch 190/200
235/235 [==============================] - 2s 9ms/step - loss: 5.0465e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9776
Epoch 191/200
235/235 [==============================] - 2s 9ms/step - loss: 4.9611e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9776
Epoch 192/200
235/235 [==============================] - 2s 9ms/step - loss: 4.8677e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9776
Epoch 193/200
235/235 [==============================] - 2s 9ms/step - loss: 4.8121e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9777
Epoch 194/200
235/235 [==============================] - 2s 9ms/step - loss: 4.7048e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9776
Epoch 195/200
235/235 [==============================] - 2s 9ms/step - loss: 4.6750e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9777
Epoch 196/200
235/235 [==============================] - 2s 9ms/step - loss: 4.6055e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9777
Epoch 197/200
235/235 [==============================] - 2s 9ms/step - loss: 4.5439e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9777
Epoch 198/200
235/235 [==============================] - 2s 9ms/step - loss: 4.4942e-09 - accuracy: 1.0000 - val_loss: 0.2677 - val_accuracy: 0.9777
Epoch 199/200
235/235 [==============================] - 2s 9ms/step - loss: 4.3929e-09 - accuracy: 1.0000 - val_loss: 0.2678 - val_accuracy: 0.9777
Epoch 200/200
235/235 [==============================] - 2s 9ms/step - loss: 4.3233e-09 - accuracy: 1.0000 - val_loss: 0.2679 - val_accuracy: 0.9777
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.03711757808923721
Thresholhold -0.0392148457467556
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.06093437783420086
Thresholhold 0.05750960856676102
Using suggest threshold.
Applying new mask
Percentage zeros 0.47363332
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 0. 1.]
 ...
 [1. 1. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.11950229108333588
Thresholhold -0.07819221913814545
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
  5/235 [..............................] - ETA: 2s - loss: 7.5759 - accuracy: 0.4180     WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0122s vs `on_train_batch_begin` time: 11.8797s). Check your callbacks.
235/235 [==============================] - 76s 17ms/step - loss: 2.1743 - accuracy: 0.9234 - val_loss: 1.6469 - val_accuracy: 0.8219
[ 2.6182713e-07  2.7255973e-07 -5.4995253e-10 ...  6.9539361e-02
  1.7933668e-01 -1.4609440e-01]
Sparsity at: 0.05337716003005259
Epoch 2/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4467 - accuracy: 0.9609 - val_loss: 0.5192 - val_accuracy: 0.9575
[ 1.27812452e-12  1.25070071e-12 -1.26711987e-14 ...  6.27598241e-02
  1.56362295e-01 -1.05136074e-01]
Sparsity at: 0.05337716003005259
Epoch 3/500
235/235 [==============================] - 4s 16ms/step - loss: 0.3087 - accuracy: 0.9639 - val_loss: 0.3475 - val_accuracy: 0.9466
[ 3.6899336e-18  5.4916910e-18 -3.3695073e-21 ...  6.2770337e-02
  1.4459158e-01 -7.6582618e-02]
Sparsity at: 0.05337716003005259
Epoch 4/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2789 - accuracy: 0.9666 - val_loss: 0.3000 - val_accuracy: 0.9551
[-2.4170925e-23 -2.1056073e-23  2.8305059e-25 ...  5.2354824e-02
  1.3261829e-01 -5.5241436e-02]
Sparsity at: 0.05337716003005259
Epoch 5/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2613 - accuracy: 0.9681 - val_loss: 0.3123 - val_accuracy: 0.9486
[-5.1553792e-29  7.0079728e-29 -2.8508540e-31 ...  4.3344885e-02
  1.2501729e-01 -4.8855189e-02]
Sparsity at: 0.05337716003005259
Epoch 6/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2522 - accuracy: 0.9682 - val_loss: 0.2599 - val_accuracy: 0.9612
[ 5.0166462e-34 -3.9579262e-35  7.1719144e-33 ...  3.2381877e-02
  1.1879123e-01 -4.0542919e-02]
Sparsity at: 0.05337716003005259
Epoch 7/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2371 - accuracy: 0.9704 - val_loss: 0.2990 - val_accuracy: 0.9478
[ 5.0166462e-34 -3.9579262e-35  1.9939151e-05 ...  1.6526887e-02
  1.1332396e-01 -3.4175690e-02]
Sparsity at: 0.053380916604057096
Epoch 8/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2296 - accuracy: 0.9707 - val_loss: 0.2618 - val_accuracy: 0.9607
[ 5.0166462e-34 -3.9579262e-35 -3.0223157e-10 ...  1.1858181e-02
  1.0491302e-01 -2.6528461e-02]
Sparsity at: 0.053380916604057096
Epoch 9/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2237 - accuracy: 0.9717 - val_loss: 0.2793 - val_accuracy: 0.9527
[ 5.0166462e-34 -3.9579262e-35  2.4131467e-15 ...  4.6975757e-03
  9.8115675e-02 -2.0106828e-02]
Sparsity at: 0.053380916604057096
Epoch 10/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2207 - accuracy: 0.9715 - val_loss: 0.2619 - val_accuracy: 0.9564
[ 5.0166462e-34 -3.9579262e-35  1.5732008e-20 ...  3.9040274e-03
  9.3779020e-02 -2.1069059e-02]
Sparsity at: 0.053380916604057096
Epoch 11/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2095 - accuracy: 0.9724 - val_loss: 0.2776 - val_accuracy: 0.9511
[ 5.0166462e-34 -3.9579262e-35  3.9994102e-08 ...  1.4377410e-03
  9.1147780e-02 -2.6767194e-02]
Sparsity at: 0.05338467317806161
Epoch 12/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2057 - accuracy: 0.9733 - val_loss: 0.2524 - val_accuracy: 0.9580
[ 5.0166462e-34 -3.9579262e-35  3.0700200e-09 ... -2.8368442e-03
  7.4733689e-02 -2.9467864e-02]
Sparsity at: 0.05338467317806161
Epoch 13/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2038 - accuracy: 0.9724 - val_loss: 0.2603 - val_accuracy: 0.9508
[ 5.0166462e-34 -3.9579262e-35 -1.3601838e-14 ... -3.3074119e-03
  6.4808674e-02 -2.2849694e-02]
Sparsity at: 0.05338467317806161
Epoch 14/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1968 - accuracy: 0.9739 - val_loss: 0.2722 - val_accuracy: 0.9519
[ 5.0166462e-34 -3.9579262e-35 -1.9443069e-19 ...  8.4357371e-04
  5.6750868e-02 -2.0543942e-02]
Sparsity at: 0.05338467317806161
Epoch 15/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1948 - accuracy: 0.9742 - val_loss: 0.2612 - val_accuracy: 0.9500
[ 5.0166462e-34 -3.9579262e-35 -5.5318935e-07 ...  6.5877554e-03
  5.0148167e-02 -2.2757500e-02]
Sparsity at: 0.05338467317806161
Epoch 16/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1912 - accuracy: 0.9745 - val_loss: 0.2624 - val_accuracy: 0.9533
[ 5.0166462e-34 -3.9579262e-35 -6.2919158e-13 ...  2.7826377e-03
  4.2091645e-02 -2.5734803e-02]
Sparsity at: 0.05338467317806161
Epoch 17/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1893 - accuracy: 0.9742 - val_loss: 0.2541 - val_accuracy: 0.9515
[ 5.0166462e-34 -3.9579262e-35  3.9528993e-18 ...  4.2283740e-03
  3.3975795e-02 -2.3419119e-02]
Sparsity at: 0.05338467317806161
Epoch 18/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1901 - accuracy: 0.9740 - val_loss: 0.2272 - val_accuracy: 0.9583
[ 5.0166462e-34 -3.9579262e-35  3.8717550e-07 ...  1.6569829e-02
  2.5503192e-02 -3.0548433e-02]
Sparsity at: 0.05338467317806161
Epoch 19/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1863 - accuracy: 0.9744 - val_loss: 0.2450 - val_accuracy: 0.9585
[ 5.0166462e-34 -3.9579262e-35 -3.4265086e-12 ...  1.9503133e-02
  2.1660034e-02 -3.2672361e-02]
Sparsity at: 0.05338467317806161
Epoch 20/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1824 - accuracy: 0.9752 - val_loss: 0.2235 - val_accuracy: 0.9608
[ 5.0166462e-34 -3.9579262e-35  2.2170715e-13 ...  2.4956504e-02
  1.5207321e-02 -1.9665049e-02]
Sparsity at: 0.05338467317806161
Epoch 21/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1789 - accuracy: 0.9755 - val_loss: 0.2169 - val_accuracy: 0.9616
[ 5.0166462e-34 -3.9579262e-35 -2.9218391e-08 ...  2.0570545e-02
  1.1101723e-02 -1.9768164e-02]
Sparsity at: 0.05338467317806161
Epoch 22/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1804 - accuracy: 0.9744 - val_loss: 0.2287 - val_accuracy: 0.9607
[ 5.0166462e-34 -3.9579262e-35  1.9623945e-13 ...  1.7647263e-02
  7.3507223e-03 -1.5398765e-02]
Sparsity at: 0.05338467317806161
Epoch 23/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1771 - accuracy: 0.9754 - val_loss: 0.2116 - val_accuracy: 0.9656
[ 5.0166462e-34 -3.9579262e-35  3.2583202e-06 ...  1.0712972e-02
  4.2403848e-03 -1.7174795e-02]
Sparsity at: 0.05338467317806161
Epoch 24/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1755 - accuracy: 0.9752 - val_loss: 0.2310 - val_accuracy: 0.9579
[ 5.0166462e-34 -3.9579262e-35  3.8201664e-11 ...  1.3251809e-02
 -9.6835248e-04 -1.6826019e-02]
Sparsity at: 0.05338467317806161
Epoch 25/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1735 - accuracy: 0.9760 - val_loss: 0.2171 - val_accuracy: 0.9632
[ 5.0166462e-34 -3.9579262e-35 -1.4232317e-11 ...  1.0068036e-02
  1.2489640e-03 -9.7984700e-03]
Sparsity at: 0.05338467317806161
Epoch 26/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1741 - accuracy: 0.9761 - val_loss: 0.2150 - val_accuracy: 0.9613
[ 5.0166462e-34 -3.9579262e-35 -6.5875181e-09 ...  1.0325787e-02
 -1.2203654e-03 -7.4751927e-03]
Sparsity at: 0.05338467317806161
Epoch 27/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1728 - accuracy: 0.9751 - val_loss: 0.2427 - val_accuracy: 0.9546
[ 5.0166462e-34 -3.9579262e-35 -9.7149512e-15 ...  1.3703915e-02
 -1.3237198e-02 -9.7690606e-03]
Sparsity at: 0.05338467317806161
Epoch 28/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1716 - accuracy: 0.9760 - val_loss: 0.2414 - val_accuracy: 0.9534
[ 5.0166462e-34 -3.9579262e-35 -2.1649271e-07 ...  7.1665542e-03
 -1.8820412e-02 -1.9201253e-02]
Sparsity at: 0.05338467317806161
Epoch 29/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1698 - accuracy: 0.9755 - val_loss: 0.2365 - val_accuracy: 0.9535
[ 5.0166462e-34 -3.9579262e-35 -4.3212448e-12 ...  1.0658165e-02
 -1.8117230e-02 -1.9476769e-02]
Sparsity at: 0.05338467317806161
Epoch 30/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1699 - accuracy: 0.9755 - val_loss: 0.2184 - val_accuracy: 0.9595
[ 5.0166462e-34 -3.9579262e-35 -1.8915485e-05 ...  1.2829820e-02
 -2.3846824e-02 -2.4275405e-02]
Sparsity at: 0.05338467317806161
Epoch 31/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1691 - accuracy: 0.9760 - val_loss: 0.2381 - val_accuracy: 0.9545
[ 5.0166462e-34 -3.9579262e-35  7.3397177e-11 ...  9.7641647e-03
 -2.8089058e-02 -1.8779812e-02]
Sparsity at: 0.05338467317806161
Epoch 32/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1632 - accuracy: 0.9767 - val_loss: 0.2316 - val_accuracy: 0.9566
[ 5.0166462e-34 -3.9579262e-35  3.7741330e-09 ...  1.3506287e-02
 -2.2983087e-02 -1.7499605e-02]
Sparsity at: 0.05338467317806161
Epoch 33/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1687 - accuracy: 0.9748 - val_loss: 0.2118 - val_accuracy: 0.9622
[ 5.0166462e-34 -3.9579262e-35  5.6163501e-09 ...  1.4217476e-02
 -3.0416472e-02 -1.2726890e-02]
Sparsity at: 0.05338467317806161
Epoch 34/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1645 - accuracy: 0.9768 - val_loss: 0.2123 - val_accuracy: 0.9618
[ 5.0166462e-34 -3.9579262e-35  1.1473854e-13 ...  1.5452255e-02
 -3.5286523e-02 -1.1925710e-02]
Sparsity at: 0.05338467317806161
Epoch 35/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1635 - accuracy: 0.9761 - val_loss: 0.2275 - val_accuracy: 0.9574
[ 5.0166462e-34 -3.9579262e-35 -6.3272822e-08 ...  1.4674430e-02
 -2.7876941e-02 -1.2471416e-02]
Sparsity at: 0.05338467317806161
Epoch 36/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1667 - accuracy: 0.9758 - val_loss: 0.2257 - val_accuracy: 0.9594
[ 5.0166462e-34 -3.9579262e-35 -7.2662926e-13 ...  1.4933964e-02
 -3.2886770e-02 -6.4423378e-03]
Sparsity at: 0.05338467317806161
Epoch 37/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1634 - accuracy: 0.9764 - val_loss: 0.1973 - val_accuracy: 0.9669
[ 5.0166462e-34 -3.9579262e-35  1.1768242e-06 ...  1.0340488e-02
 -2.6485782e-02 -1.0832785e-02]
Sparsity at: 0.05338467317806161
Epoch 38/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1628 - accuracy: 0.9763 - val_loss: 0.2128 - val_accuracy: 0.9615
[ 5.0166462e-34 -3.9579262e-35  7.2762013e-12 ...  1.0905896e-02
 -2.4131672e-02 -8.0275489e-03]
Sparsity at: 0.05338467317806161
Epoch 39/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1631 - accuracy: 0.9760 - val_loss: 0.2010 - val_accuracy: 0.9656
[ 5.0166462e-34 -3.9579262e-35  2.3399634e-06 ...  8.1663085e-03
 -2.4927408e-02 -1.4740099e-02]
Sparsity at: 0.05338467317806161
Epoch 40/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1599 - accuracy: 0.9773 - val_loss: 0.2234 - val_accuracy: 0.9590
[ 5.0166462e-34 -3.9579262e-35  4.6575004e-11 ...  1.0923654e-02
 -3.5388030e-02 -1.5200370e-02]
Sparsity at: 0.05338467317806161
Epoch 41/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1625 - accuracy: 0.9758 - val_loss: 0.2111 - val_accuracy: 0.9609
[ 5.0166462e-34 -3.9579262e-35 -8.3646737e-06 ...  2.1427993e-03
 -2.2664459e-02 -1.9955078e-02]
Sparsity at: 0.05338467317806161
Epoch 42/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1633 - accuracy: 0.9751 - val_loss: 0.2607 - val_accuracy: 0.9496
[ 5.0166462e-34 -3.9579262e-35  9.4319538e-11 ...  4.2143008e-03
 -3.8583081e-02 -1.4358026e-02]
Sparsity at: 0.05338467317806161
Epoch 43/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1600 - accuracy: 0.9771 - val_loss: 0.2015 - val_accuracy: 0.9646
[ 5.0166462e-34 -3.9579262e-35  3.5953258e-09 ...  5.6377659e-03
 -2.3705304e-02 -1.7880758e-02]
Sparsity at: 0.05338467317806161
Epoch 44/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1576 - accuracy: 0.9772 - val_loss: 0.2094 - val_accuracy: 0.9626
[ 5.0166462e-34 -3.9579262e-35  5.0679145e-09 ...  1.4536776e-02
 -3.0341765e-02 -1.8914200e-02]
Sparsity at: 0.05338467317806161
Epoch 45/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1612 - accuracy: 0.9762 - val_loss: 0.1974 - val_accuracy: 0.9672
[ 5.0166462e-34 -3.9579262e-35 -1.8783124e-12 ...  2.3019256e-02
 -3.4548748e-02 -1.5799886e-02]
Sparsity at: 0.05338467317806161
Epoch 46/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1592 - accuracy: 0.9762 - val_loss: 0.1872 - val_accuracy: 0.9704
[ 5.0166462e-34 -3.9579262e-35  3.2089194e-08 ...  1.9440114e-02
 -3.1856921e-02 -2.6698433e-02]
Sparsity at: 0.05338467317806161
Epoch 47/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1606 - accuracy: 0.9765 - val_loss: 0.2049 - val_accuracy: 0.9628
[ 5.0166462e-34 -3.9579262e-35 -1.5128660e-13 ...  1.4182773e-02
 -2.7691234e-02 -2.3910008e-02]
Sparsity at: 0.05338467317806161
Epoch 48/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1606 - accuracy: 0.9765 - val_loss: 0.2064 - val_accuracy: 0.9665loss: 0.1613 - accu
[ 5.0166462e-34 -3.9579262e-35  5.5486066e-08 ...  1.4384116e-02
 -2.4636896e-02 -2.0474030e-02]
Sparsity at: 0.05338467317806161
Epoch 49/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1575 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9501
[ 5.0166462e-34 -3.9579262e-35  1.0389762e-12 ...  1.7705215e-02
 -3.6468856e-02 -1.9604597e-02]
Sparsity at: 0.05338467317806161
Epoch 50/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1572 - accuracy: 0.9767 - val_loss: 0.1959 - val_accuracy: 0.9684
[ 5.0166462e-34 -3.9579262e-35  3.3418155e-07 ...  1.4178975e-02
 -3.0434823e-02 -1.7258856e-02]
Sparsity at: 0.05338467317806161
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 4.127688005100672e-34
Thresholhold 5.0166462207447374e-34
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [1. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 1. 1. 0.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 2.1617254299427043e-08
Thresholhold 0.0006648348644375801
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 0. 1.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.03189364128356509
Thresholhold 0.008343097753822803
Using suggest threshold.
Applying new mask
Percentage zeros 0.179
tf.Tensor(
[[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 161s 16ms/step - loss: 0.1535 - accuracy: 0.9777 - val_loss: 0.2277 - val_accuracy: 0.9568
[ 5.0166462e-34  0.0000000e+00  4.0623971e-12 ...  1.3479545e-02
 -2.9032305e-02 -1.4903450e-02]
Sparsity at: 0.6441059353869272
Epoch 52/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1578 - accuracy: 0.9764 - val_loss: 0.2105 - val_accuracy: 0.9610
[ 5.01664622e-34  0.00000000e+00 -1.89223988e-06 ...  1.42970905e-02
 -3.20940502e-02 -1.25430441e-02]
Sparsity at: 0.6441059353869272
Epoch 53/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1581 - accuracy: 0.9767 - val_loss: 0.2061 - val_accuracy: 0.9630
[ 5.0166462e-34  0.0000000e+00 -2.9856560e-12 ...  1.0371569e-02
 -2.4364771e-02 -7.7982987e-03]
Sparsity at: 0.6441059353869272
Epoch 54/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1571 - accuracy: 0.9762 - val_loss: 0.2224 - val_accuracy: 0.9597
[ 5.0166462e-34  0.0000000e+00 -8.8333909e-06 ...  1.9195370e-02
 -2.4613300e-02 -1.9365584e-02]
Sparsity at: 0.6441059353869272
Epoch 55/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1554 - accuracy: 0.9770 - val_loss: 0.1912 - val_accuracy: 0.9674
[ 5.0166462e-34  0.0000000e+00 -4.7829588e-12 ...  2.1803521e-02
 -2.5473414e-02 -1.5462674e-02]
Sparsity at: 0.6441059353869272
Epoch 56/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1591 - accuracy: 0.9763 - val_loss: 0.2186 - val_accuracy: 0.9585
[ 5.0166462e-34  0.0000000e+00 -9.7146876e-06 ...  2.4091730e-02
 -3.2752600e-02 -1.6575871e-02]
Sparsity at: 0.6441059353869272
Epoch 57/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1561 - accuracy: 0.9767 - val_loss: 0.2191 - val_accuracy: 0.9593
[ 5.0166462e-34  0.0000000e+00 -3.1474162e-10 ...  2.6636785e-02
 -3.1338960e-02 -2.8078005e-02]
Sparsity at: 0.6441059353869272
Epoch 58/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1570 - accuracy: 0.9764 - val_loss: 0.2238 - val_accuracy: 0.9562
[ 5.0166462e-34  0.0000000e+00 -1.8697406e-07 ...  2.4864063e-02
 -2.6355470e-02 -2.6583431e-02]
Sparsity at: 0.6441059353869272
Epoch 59/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1544 - accuracy: 0.9774 - val_loss: 0.1986 - val_accuracy: 0.9660
[ 5.0166462e-34  0.0000000e+00 -1.1679702e-09 ...  1.3369675e-02
 -3.8235225e-02 -1.8721934e-02]
Sparsity at: 0.6441059353869272
Epoch 60/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1535 - accuracy: 0.9777 - val_loss: 0.2132 - val_accuracy: 0.9631
[ 5.0166462e-34  0.0000000e+00 -6.3867418e-11 ...  4.4711653e-04
 -3.8320549e-02 -1.9163255e-02]
Sparsity at: 0.6441059353869272
Epoch 61/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1552 - accuracy: 0.9778 - val_loss: 0.2310 - val_accuracy: 0.9544
[ 5.0166462e-34  0.0000000e+00 -1.0883481e-09 ...  7.9630557e-03
 -3.0702971e-02 -2.2882050e-02]
Sparsity at: 0.6441059353869272
Epoch 62/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1535 - accuracy: 0.9770 - val_loss: 0.2194 - val_accuracy: 0.9596
[ 5.0166462e-34  0.0000000e+00  9.8072332e-13 ...  9.8965326e-03
 -3.3567477e-02 -2.5015902e-02]
Sparsity at: 0.6441059353869272
Epoch 63/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1583 - accuracy: 0.9758 - val_loss: 0.2064 - val_accuracy: 0.9626
[ 5.0166462e-34  0.0000000e+00 -5.5153365e-08 ...  1.2028722e-02
 -2.7328763e-02 -2.8118985e-02]
Sparsity at: 0.6441059353869272
Epoch 64/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1511 - accuracy: 0.9779 - val_loss: 0.2052 - val_accuracy: 0.9588
[ 5.0166462e-34  0.0000000e+00 -3.3608088e-13 ...  1.3083044e-02
 -2.6202209e-02 -2.7347594e-02]
Sparsity at: 0.6441059353869272
Epoch 65/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1511 - accuracy: 0.9777 - val_loss: 0.1986 - val_accuracy: 0.9618
[ 5.0166462e-34  0.0000000e+00 -1.9550271e-07 ...  8.4086014e-03
 -2.3641180e-02 -1.5738163e-02]
Sparsity at: 0.6441059353869272
Epoch 66/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1495 - accuracy: 0.9777 - val_loss: 0.2364 - val_accuracy: 0.9514
[ 5.0166462e-34  0.0000000e+00  9.6862091e-13 ...  9.3865478e-03
 -3.8449239e-02 -1.5006943e-02]
Sparsity at: 0.6441059353869272
Epoch 67/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1545 - accuracy: 0.9765 - val_loss: 0.1978 - val_accuracy: 0.9650
[ 5.0166462e-34  0.0000000e+00  1.1961954e-06 ...  4.5176172e-03
 -3.0956587e-02 -1.9349823e-02]
Sparsity at: 0.6441059353869272
Epoch 68/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1503 - accuracy: 0.9776 - val_loss: 0.2365 - val_accuracy: 0.9534
[ 5.0166462e-34  0.0000000e+00 -6.0259497e-12 ...  2.6058832e-03
 -3.0703098e-02 -1.9611778e-02]
Sparsity at: 0.6441059353869272
Epoch 69/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1480 - accuracy: 0.9782 - val_loss: 0.2041 - val_accuracy: 0.9637
[ 5.0166462e-34  0.0000000e+00 -1.4919138e-05 ... -4.0144813e-03
 -2.4919795e-02 -1.1992470e-02]
Sparsity at: 0.6441059353869272
Epoch 70/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1499 - accuracy: 0.9773 - val_loss: 0.2215 - val_accuracy: 0.9591
[ 5.0166462e-34  0.0000000e+00 -2.7269520e-11 ... -5.1395381e-03
 -2.8584626e-02 -1.5197810e-02]
Sparsity at: 0.6441059353869272
Epoch 71/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1495 - accuracy: 0.9783 - val_loss: 0.2231 - val_accuracy: 0.9566
[ 5.0166462e-34  0.0000000e+00  1.2278906e-04 ...  7.0645693e-03
 -3.0856764e-02 -1.1189264e-02]
Sparsity at: 0.6441059353869272
Epoch 72/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1516 - accuracy: 0.9766 - val_loss: 0.2088 - val_accuracy: 0.9610
[ 5.0166462e-34  0.0000000e+00  3.0474456e-10 ...  9.5961168e-03
 -2.7384391e-02 -1.2730742e-02]
Sparsity at: 0.6441059353869272
Epoch 73/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1514 - accuracy: 0.9767 - val_loss: 0.2165 - val_accuracy: 0.9601
[ 5.0166462e-34  0.0000000e+00 -7.8143442e-10 ...  2.2332519e-03
 -1.3410567e-02 -1.2902572e-02]
Sparsity at: 0.6441059353869272
Epoch 74/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1497 - accuracy: 0.9776 - val_loss: 0.2270 - val_accuracy: 0.9536
[ 5.016646e-34  0.000000e+00 -8.154175e-10 ...  7.903611e-03 -3.088829e-02
 -1.119129e-02]
Sparsity at: 0.6441059353869272
Epoch 75/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1482 - accuracy: 0.9778 - val_loss: 0.1891 - val_accuracy: 0.9665
[ 5.0166462e-34  0.0000000e+00 -4.8531670e-13 ... -1.2072031e-03
 -2.8435392e-02 -7.9397941e-03]
Sparsity at: 0.6441059353869272
Epoch 76/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1475 - accuracy: 0.9778 - val_loss: 0.2053 - val_accuracy: 0.9612
[ 5.0166462e-34  0.0000000e+00  6.5276836e-08 ...  6.3317469e-03
 -3.1204470e-02 -1.6628483e-02]
Sparsity at: 0.6441059353869272
Epoch 77/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1494 - accuracy: 0.9773 - val_loss: 0.2220 - val_accuracy: 0.9579
[ 5.0166462e-34  0.0000000e+00  4.9464366e-13 ...  7.4462583e-03
 -2.2881800e-02 -1.8518686e-02]
Sparsity at: 0.6441059353869272
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1472 - accuracy: 0.9779 - val_loss: 0.2034 - val_accuracy: 0.9626
[ 5.0166462e-34  0.0000000e+00 -1.2117837e-07 ... -1.8681637e-04
 -2.6354477e-02 -1.8567117e-02]
Sparsity at: 0.6441059353869272
Epoch 79/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9781 - val_loss: 0.2069 - val_accuracy: 0.9632
[ 5.0166462e-34  0.0000000e+00 -2.1818647e-12 ... -1.2605761e-03
 -1.8196050e-02 -9.6477866e-03]
Sparsity at: 0.6441059353869272
Epoch 80/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1461 - accuracy: 0.9783 - val_loss: 0.2220 - val_accuracy: 0.9553
[ 5.0166462e-34  0.0000000e+00  6.7258503e-07 ... -8.7429502e-04
 -2.1387301e-02 -1.0669601e-02]
Sparsity at: 0.6441059353869272
Epoch 81/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1487 - accuracy: 0.9778 - val_loss: 0.1931 - val_accuracy: 0.9651
[ 5.0166462e-34  0.0000000e+00 -6.0830421e-12 ... -3.3901175e-03
 -1.9290127e-02 -1.2895949e-02]
Sparsity at: 0.6441059353869272
Epoch 82/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1483 - accuracy: 0.9777 - val_loss: 0.2021 - val_accuracy: 0.9599
[ 5.0166462e-34  0.0000000e+00  1.5140465e-05 ... -8.1940005e-03
 -2.1523420e-02 -2.2413734e-02]
Sparsity at: 0.6441059353869272
Epoch 83/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1486 - accuracy: 0.9773 - val_loss: 0.2217 - val_accuracy: 0.9569
[ 5.0166462e-34  0.0000000e+00  6.8045181e-11 ... -5.2355332e-03
 -7.5918632e-03 -2.2861354e-02]
Sparsity at: 0.6441059353869272
Epoch 84/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9780 - val_loss: 0.2229 - val_accuracy: 0.9584
[ 5.0166462e-34  0.0000000e+00  6.1325278e-05 ...  1.3714503e-03
 -2.0043679e-02 -1.7996848e-02]
Sparsity at: 0.6441059353869272
Epoch 85/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1463 - accuracy: 0.9782 - val_loss: 0.1940 - val_accuracy: 0.9661
[ 5.0166462e-34  0.0000000e+00 -6.1296201e-10 ...  1.4417048e-03
 -2.0907404e-02 -7.3444662e-03]
Sparsity at: 0.6441059353869272
Epoch 86/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1471 - accuracy: 0.9783 - val_loss: 0.1946 - val_accuracy: 0.9628
[ 5.0166462e-34  0.0000000e+00 -7.0597839e-09 ... -1.6848978e-02
 -1.2460677e-02 -1.6703008e-02]
Sparsity at: 0.6441059353869272
Epoch 87/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1479 - accuracy: 0.9777 - val_loss: 0.2036 - val_accuracy: 0.9604
[ 5.0166462e-34  0.0000000e+00 -3.1662686e-09 ... -3.4173774e-03
 -1.5515212e-02 -1.9193310e-02]
Sparsity at: 0.6441059353869272
Epoch 88/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1458 - accuracy: 0.9779 - val_loss: 0.2112 - val_accuracy: 0.9596
[ 5.0166462e-34  0.0000000e+00 -5.5103916e-12 ... -3.9985105e-03
 -1.6525606e-02 -1.8901506e-02]
Sparsity at: 0.6441059353869272
Epoch 89/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1428 - accuracy: 0.9792 - val_loss: 0.2194 - val_accuracy: 0.9612
[ 5.0166462e-34  0.0000000e+00 -6.1624661e-09 ... -2.5452795e-03
 -8.1167966e-03 -1.7700922e-02]
Sparsity at: 0.6441059353869272
Epoch 90/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9775 - val_loss: 0.1938 - val_accuracy: 0.9658
[ 5.0166462e-34  0.0000000e+00 -2.4435442e-13 ...  1.3421847e-03
 -1.6745526e-02 -2.0333633e-02]
Sparsity at: 0.6441059353869272
Epoch 91/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1465 - accuracy: 0.9779 - val_loss: 0.2139 - val_accuracy: 0.9597
[ 5.0166462e-34  0.0000000e+00 -1.3257443e-07 ... -6.0850023e-03
 -1.0935331e-02 -1.6774198e-02]
Sparsity at: 0.6441059353869272
Epoch 92/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1464 - accuracy: 0.9781 - val_loss: 0.2053 - val_accuracy: 0.9613
[ 5.0166462e-34  0.0000000e+00  2.1528997e-13 ... -1.7070226e-02
 -2.2637552e-02 -1.1558738e-02]
Sparsity at: 0.6441059353869272
Epoch 93/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1482 - accuracy: 0.9778 - val_loss: 0.2040 - val_accuracy: 0.9612
[ 5.0166462e-34  0.0000000e+00  1.5209645e-07 ... -1.7273937e-03
 -1.8124724e-02 -2.0251274e-02]
Sparsity at: 0.6441059353869272
Epoch 94/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1470 - accuracy: 0.9778 - val_loss: 0.2489 - val_accuracy: 0.9530
[ 5.01664622e-34  0.00000000e+00  1.05434324e-14 ... -5.50695695e-03
 -2.21271366e-02 -1.62406899e-02]
Sparsity at: 0.6441059353869272
Epoch 95/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1482 - accuracy: 0.9777 - val_loss: 0.2087 - val_accuracy: 0.9596
[ 5.0166462e-34  0.0000000e+00 -3.7033842e-06 ... -8.3438465e-03
 -1.8339496e-02 -1.5137114e-02]
Sparsity at: 0.6441059353869272
Epoch 96/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1458 - accuracy: 0.9783 - val_loss: 0.2354 - val_accuracy: 0.9507
[ 5.01664622e-34  0.00000000e+00 -1.52993347e-11 ... -1.13332085e-02
 -1.55136567e-02 -4.46621887e-03]
Sparsity at: 0.6441059353869272
Epoch 97/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1441 - accuracy: 0.9784 - val_loss: 0.2132 - val_accuracy: 0.9597 0s - loss: 0
[ 5.0166462e-34  0.0000000e+00 -3.3091434e-05 ... -4.2730221e-03
 -1.7229708e-02 -7.2580515e-03]
Sparsity at: 0.6441059353869272
Epoch 98/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1447 - accuracy: 0.9787 - val_loss: 0.2106 - val_accuracy: 0.9601
[ 5.0166462e-34  0.0000000e+00  3.1454078e-10 ... -3.7654387e-03
 -2.0741191e-02 -1.1330617e-02]
Sparsity at: 0.6441059353869272
Epoch 99/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1472 - accuracy: 0.9773 - val_loss: 0.2123 - val_accuracy: 0.9622
[ 5.0166462e-34  0.0000000e+00  3.4703738e-09 ... -8.4781321e-03
 -2.5007870e-02 -3.4283102e-03]
Sparsity at: 0.6441059353869272
Epoch 100/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1500 - accuracy: 0.9768 - val_loss: 0.2188 - val_accuracy: 0.9575
[ 5.0166462e-34  0.0000000e+00 -3.4889458e-09 ... -5.7886345e-03
 -1.3090068e-02 -4.3662172e-03]
Sparsity at: 0.6441059353869272
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 4.843794464026135e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [1. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 1. 1. 0.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 7.005245859754415e-08
Thresholhold -1.1641729358302655e-08
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 0. 1.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.04016310498981923
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.179
tf.Tensor(
[[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 294s 15ms/step - loss: 0.1452 - accuracy: 0.9786 - val_loss: 0.2239 - val_accuracy: 0.9571
[ 5.0166462e-34  0.0000000e+00  1.6359491e-13 ... -4.5295502e-03
 -1.3375368e-02 -4.4609844e-03]
Sparsity at: 0.6441059353869272
Epoch 102/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1509 - accuracy: 0.9769 - val_loss: 0.2283 - val_accuracy: 0.9563
[ 5.0166462e-34  0.0000000e+00  5.8804844e-08 ... -6.9973022e-03
 -1.6044024e-02 -9.6602067e-03]
Sparsity at: 0.6441059353869272
Epoch 103/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1468 - accuracy: 0.9781 - val_loss: 0.1983 - val_accuracy: 0.9631
[ 5.0166462e-34  0.0000000e+00 -7.2934551e-15 ... -3.6859934e-03
 -1.6095224e-03 -2.1698272e-02]
Sparsity at: 0.6441059353869272
Epoch 104/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1465 - accuracy: 0.9772 - val_loss: 0.2169 - val_accuracy: 0.9596
[ 5.0166462e-34  0.0000000e+00 -1.3184379e-06 ... -8.1925420e-03
 -8.4327478e-03 -2.7418265e-02]
Sparsity at: 0.6441059353869272
Epoch 105/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1440 - accuracy: 0.9786 - val_loss: 0.2141 - val_accuracy: 0.9596
[ 5.0166462e-34  0.0000000e+00 -7.4742651e-12 ...  2.4576788e-03
 -6.0032932e-03 -1.8727563e-02]
Sparsity at: 0.6441059353869272
Epoch 106/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1446 - accuracy: 0.9787 - val_loss: 0.2119 - val_accuracy: 0.9598
[ 5.0166462e-34  0.0000000e+00 -2.2651082e-05 ... -5.7037799e-03
 -9.7872298e-03 -1.5475204e-02]
Sparsity at: 0.6441059353869272
Epoch 107/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1491 - accuracy: 0.9773 - val_loss: 0.2048 - val_accuracy: 0.9632
[ 5.01664622e-34  0.00000000e+00 -8.17483026e-11 ... -8.65891483e-03
 -1.50544485e-02 -1.73985399e-02]
Sparsity at: 0.6441059353869272
Epoch 108/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1429 - accuracy: 0.9789 - val_loss: 0.1970 - val_accuracy: 0.9640
[ 5.0166462e-34  0.0000000e+00  1.8529958e-04 ...  1.0554134e-02
 -1.5312563e-02 -1.6008602e-02]
Sparsity at: 0.6441059353869272
Epoch 109/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1458 - accuracy: 0.9775 - val_loss: 0.2070 - val_accuracy: 0.9609
[ 5.0166462e-34  0.0000000e+00  7.8281592e-10 ...  2.0660977e-03
 -1.0992130e-02 -1.0932870e-02]
Sparsity at: 0.6441059353869272
Epoch 110/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9780 - val_loss: 0.2190 - val_accuracy: 0.9578
[ 5.0166462e-34  0.0000000e+00  1.5836985e-08 ... -2.8291738e-03
 -1.0496937e-02 -1.3941357e-02]
Sparsity at: 0.6441059353869272
Epoch 111/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1463 - accuracy: 0.9772 - val_loss: 0.1908 - val_accuracy: 0.9639
[ 5.0166462e-34  0.0000000e+00 -2.0012987e-09 ...  1.8041985e-03
 -1.7547794e-02 -1.3510820e-02]
Sparsity at: 0.6441059353869272
Epoch 112/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1441 - accuracy: 0.9788 - val_loss: 0.2137 - val_accuracy: 0.9591
[ 5.0166462e-34  0.0000000e+00 -2.4272511e-11 ... -6.7572546e-05
 -1.2409200e-02 -1.3275783e-02]
Sparsity at: 0.6441059353869272
Epoch 113/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1439 - accuracy: 0.9780 - val_loss: 0.1923 - val_accuracy: 0.9642
[ 5.0166462e-34  0.0000000e+00  1.4537335e-08 ...  4.4772658e-04
 -1.8451089e-02 -7.5737289e-03]
Sparsity at: 0.6441059353869272
Epoch 114/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1429 - accuracy: 0.9790 - val_loss: 0.1950 - val_accuracy: 0.9670
[ 5.0166462e-34  0.0000000e+00 -1.0241354e-12 ... -9.4325794e-04
 -1.3005402e-02 -1.4674718e-02]
Sparsity at: 0.6441059353869272
Epoch 115/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1416 - accuracy: 0.9783 - val_loss: 0.1990 - val_accuracy: 0.9634
[ 5.01664622e-34  0.00000000e+00  4.82964779e-08 ... -1.43142715e-02
 -1.02217747e-02 -9.05843358e-03]
Sparsity at: 0.6441059353869272
Epoch 116/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9785 - val_loss: 0.2259 - val_accuracy: 0.9569
[ 5.0166462e-34  0.0000000e+00  5.3147428e-13 ... -8.8199405e-03
 -5.7418658e-03 -1.5898623e-02]
Sparsity at: 0.6441059353869272
Epoch 117/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1395 - accuracy: 0.9798 - val_loss: 0.2017 - val_accuracy: 0.9621
[ 5.0166462e-34  0.0000000e+00 -1.3179192e-07 ... -8.7538138e-03
 -1.5333158e-02 -9.5256632e-03]
Sparsity at: 0.6441059353869272
Epoch 118/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1421 - accuracy: 0.9780 - val_loss: 0.2504 - val_accuracy: 0.9473
[ 5.0166462e-34  0.0000000e+00 -8.4940258e-13 ... -5.1972885e-03
 -1.3927585e-02 -7.8176390e-03]
Sparsity at: 0.6441059353869272
Epoch 119/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1430 - accuracy: 0.9787 - val_loss: 0.2387 - val_accuracy: 0.9514
[ 5.0166462e-34  0.0000000e+00  1.0370704e-07 ... -6.9519563e-04
 -1.0389775e-02 -1.6700611e-02]
Sparsity at: 0.6441059353869272
Epoch 120/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1436 - accuracy: 0.9780 - val_loss: 0.1896 - val_accuracy: 0.9659
[ 5.0166462e-34  0.0000000e+00  2.1134531e-12 ... -4.9449233e-03
 -1.6929176e-02 -1.8318223e-02]
Sparsity at: 0.6441059353869272
Epoch 121/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1413 - accuracy: 0.9786 - val_loss: 0.2451 - val_accuracy: 0.9497
[ 5.0166462e-34  0.0000000e+00 -2.5602327e-07 ... -1.3167212e-02
 -1.8170573e-02 -1.7048089e-02]
Sparsity at: 0.6441059353869272
Epoch 122/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1401 - accuracy: 0.9793 - val_loss: 0.1895 - val_accuracy: 0.9649
[ 5.0166462e-34  0.0000000e+00 -3.3407982e-13 ... -7.8073479e-03
 -1.5467769e-02 -1.1018596e-02]
Sparsity at: 0.6441059353869272
Epoch 123/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1420 - accuracy: 0.9786 - val_loss: 0.2002 - val_accuracy: 0.9649
[ 5.016646e-34  0.000000e+00 -5.315796e-07 ... -6.627698e-03 -1.676392e-02
 -5.065035e-03]
Sparsity at: 0.6441059353869272
Epoch 124/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1430 - accuracy: 0.9783 - val_loss: 0.1982 - val_accuracy: 0.9637
[ 5.0166462e-34  0.0000000e+00 -5.5410776e-12 ... -3.3665195e-03
 -1.3678995e-02 -7.4321218e-03]
Sparsity at: 0.6441059353869272
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9783 - val_loss: 0.1996 - val_accuracy: 0.9631
[ 5.0166462e-34  0.0000000e+00 -1.1658190e-06 ... -6.3401705e-04
 -5.7373382e-03 -1.3058696e-02]
Sparsity at: 0.6441059353869272
Epoch 126/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1428 - accuracy: 0.9790 - val_loss: 0.2054 - val_accuracy: 0.9619
[ 5.0166462e-34  0.0000000e+00 -2.5690450e-11 ... -6.6906838e-03
 -1.0618143e-02 -1.6082095e-02]
Sparsity at: 0.6441059353869272
Epoch 127/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1433 - accuracy: 0.9785 - val_loss: 0.2406 - val_accuracy: 0.9519
[ 5.0166462e-34  0.0000000e+00 -8.1194667e-06 ... -1.3427250e-02
  1.4790015e-03 -1.8008687e-02]
Sparsity at: 0.6441059353869272
Epoch 128/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1435 - accuracy: 0.9787 - val_loss: 0.1943 - val_accuracy: 0.9638
[ 5.0166462e-34  0.0000000e+00  2.4100999e-11 ... -9.7214151e-03
 -1.6177583e-02 -2.3430079e-02]
Sparsity at: 0.6441059353869272
Epoch 129/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1420 - accuracy: 0.9785 - val_loss: 0.2121 - val_accuracy: 0.9599
[ 5.0166462e-34  0.0000000e+00  8.8364341e-06 ... -1.7442445e-03
 -1.3602024e-02 -2.8492359e-02]
Sparsity at: 0.6441059353869272
Epoch 130/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1413 - accuracy: 0.9792 - val_loss: 0.2103 - val_accuracy: 0.9600
[ 5.0166462e-34  0.0000000e+00 -1.4257053e-09 ... -1.4578969e-03
 -6.7315223e-03 -2.0727526e-02]
Sparsity at: 0.6441059353869272
Epoch 131/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1404 - accuracy: 0.9789 - val_loss: 0.2277 - val_accuracy: 0.9566
[ 5.0166462e-34  0.0000000e+00  1.0530149e-10 ... -6.1588828e-03
 -5.3962730e-03 -1.4300667e-02]
Sparsity at: 0.6441059353869272
Epoch 132/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1423 - accuracy: 0.9787 - val_loss: 0.2316 - val_accuracy: 0.9550
[ 5.0166462e-34  0.0000000e+00  3.8762371e-09 ...  1.0364726e-03
 -1.5876614e-02 -8.6401878e-03]
Sparsity at: 0.6441059353869272
Epoch 133/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1439 - accuracy: 0.9780 - val_loss: 0.1938 - val_accuracy: 0.9656
[ 5.0166462e-34  0.0000000e+00 -4.4175351e-13 ... -4.8034624e-03
 -6.9611799e-03 -7.5629111e-03]
Sparsity at: 0.6441059353869272
Epoch 134/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1420 - accuracy: 0.9781 - val_loss: 0.1989 - val_accuracy: 0.9625
[ 5.016646e-34  0.000000e+00 -6.965106e-08 ... -6.485253e-03 -5.513031e-03
 -9.988348e-03]
Sparsity at: 0.6441059353869272
Epoch 135/500
235/235 [==============================] - 5s 19ms/step - loss: 0.1400 - accuracy: 0.9784 - val_loss: 0.2211 - val_accuracy: 0.9570
[ 5.0166462e-34  0.0000000e+00 -5.4137809e-13 ... -5.8609359e-03
 -5.4674139e-03 -1.3368375e-02]
Sparsity at: 0.6441059353869272
Epoch 136/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1406 - accuracy: 0.9794 - val_loss: 0.2129 - val_accuracy: 0.9589
[ 5.0166462e-34  0.0000000e+00  1.8019051e-08 ... -9.6572062e-04
 -1.3946003e-02 -7.3130005e-03]
Sparsity at: 0.6441059353869272
Epoch 137/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1421 - accuracy: 0.9781 - val_loss: 0.2171 - val_accuracy: 0.9566
[ 5.0166462e-34  0.0000000e+00  5.9782799e-13 ... -3.1107927e-03
 -6.7630424e-03 -1.2689728e-02]
Sparsity at: 0.6441059353869272
Epoch 138/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1452 - accuracy: 0.9776 - val_loss: 0.2126 - val_accuracy: 0.9597
[ 5.0166462e-34  0.0000000e+00  7.9289117e-07 ... -4.2895153e-03
 -3.7860803e-03 -1.5833996e-02]
Sparsity at: 0.6441059353869272
Epoch 139/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1422 - accuracy: 0.9779 - val_loss: 0.2242 - val_accuracy: 0.9598
[ 5.0166462e-34  0.0000000e+00 -6.3239813e-12 ... -1.6388968e-02
 -1.0843138e-03 -1.3391660e-02]
Sparsity at: 0.6441059353869272
Epoch 140/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1410 - accuracy: 0.9782 - val_loss: 0.2220 - val_accuracy: 0.9565
[ 5.0166462e-34  0.0000000e+00  2.6486509e-06 ... -1.8014932e-02
  4.5895278e-03 -6.4535742e-04]
Sparsity at: 0.6441059353869272
Epoch 141/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1426 - accuracy: 0.9781 - val_loss: 0.2058 - val_accuracy: 0.9629
[ 5.0166462e-34  0.0000000e+00  3.0427015e-12 ... -1.1716771e-02
 -1.1202599e-02 -2.1562644e-03]
Sparsity at: 0.6441059353869272
Epoch 142/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1400 - accuracy: 0.9794 - val_loss: 0.2095 - val_accuracy: 0.9619
[ 5.0166462e-34  0.0000000e+00  2.0596019e-05 ... -1.3577618e-02
 -9.6821934e-03 -8.6959582e-03]
Sparsity at: 0.6441059353869272
Epoch 143/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1394 - accuracy: 0.9793 - val_loss: 0.2424 - val_accuracy: 0.9495
[ 5.0166462e-34  0.0000000e+00  1.4210935e-10 ... -2.0182857e-02
 -1.4675720e-02  2.8802040e-03]
Sparsity at: 0.6441059353869272
Epoch 144/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1406 - accuracy: 0.9790 - val_loss: 0.2081 - val_accuracy: 0.9618
[ 5.0166462e-34  0.0000000e+00 -2.2241055e-07 ... -2.7923353e-02
 -1.2339463e-02  5.1958975e-03]
Sparsity at: 0.6441059353869272
Epoch 145/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1420 - accuracy: 0.9782 - val_loss: 0.2035 - val_accuracy: 0.9623
[ 5.0166462e-34  0.0000000e+00 -2.5706077e-09 ... -1.5227474e-02
 -1.3130808e-02  9.5693553e-03]
Sparsity at: 0.6441059353869272
Epoch 146/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1418 - accuracy: 0.9793 - val_loss: 0.2013 - val_accuracy: 0.9632
[ 5.0166462e-34  0.0000000e+00  8.8383066e-13 ... -2.8191656e-02
 -6.4869400e-04 -9.0897731e-05]
Sparsity at: 0.6441059353869272
Epoch 147/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1388 - accuracy: 0.9795 - val_loss: 0.2008 - val_accuracy: 0.9635
[ 5.0166462e-34  0.0000000e+00 -4.4478345e-08 ... -1.9833276e-02
  2.4158790e-04 -6.5873638e-03]
Sparsity at: 0.6441059353869272
Epoch 148/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1367 - accuracy: 0.9798 - val_loss: 0.2053 - val_accuracy: 0.9599
[ 5.0166462e-34  0.0000000e+00 -3.7843570e-13 ... -2.2054341e-02
 -1.2026252e-03 -1.3040462e-02]
Sparsity at: 0.6441059353869272
Epoch 149/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2027 - val_accuracy: 0.9633
[ 5.0166462e-34  0.0000000e+00  1.2201292e-07 ... -3.1325094e-02
 -7.8931851e-03 -1.0048496e-02]
Sparsity at: 0.6441059353869272
Epoch 150/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1409 - accuracy: 0.9785 - val_loss: 0.1932 - val_accuracy: 0.9631
[ 5.0166462e-34  0.0000000e+00 -8.1458473e-13 ... -2.8685726e-02
 -1.4671220e-02 -1.1146025e-02]
Sparsity at: 0.6441059353869272
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 5.561928792053335e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [1. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 1. 1. 0.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 1.7915367989284735e-05
Thresholhold 1.4058052329346538e-05
Using suggest threshold.
Applying new mask
Percentage zeros 0.84183335
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.048193727521117724
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.179
tf.Tensor(
[[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 330s 16ms/step - loss: 0.1398 - accuracy: 0.9791 - val_loss: 0.2020 - val_accuracy: 0.9616
[ 5.0166462e-34  0.0000000e+00 -1.0428030e-06 ... -1.3755406e-02
 -1.0549697e-02 -9.6231019e-03]
Sparsity at: 0.666190833959429
Epoch 152/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1388 - accuracy: 0.9788 - val_loss: 0.2024 - val_accuracy: 0.9629
[ 5.0166462e-34  0.0000000e+00 -1.1950715e-11 ... -2.7210250e-02
  1.9448778e-03 -1.7553082e-02]
Sparsity at: 0.666190833959429
Epoch 153/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1379 - accuracy: 0.9793 - val_loss: 0.2246 - val_accuracy: 0.9553
[ 5.0166462e-34  0.0000000e+00  1.4553223e-05 ... -2.3162231e-02
  6.8728172e-04 -1.1814996e-02]
Sparsity at: 0.666190833959429
Epoch 154/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1409 - accuracy: 0.9785 - val_loss: 0.2271 - val_accuracy: 0.9546
[ 5.0166462e-34  0.0000000e+00 -9.7470670e-11 ... -2.3616191e-02
 -7.4772034e-03 -1.7338131e-02]
Sparsity at: 0.666190833959429
Epoch 155/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1414 - accuracy: 0.9789 - val_loss: 0.2001 - val_accuracy: 0.9628
[ 5.0166462e-34  0.0000000e+00  4.7177826e-05 ... -2.1276670e-02
 -1.0550897e-02  1.1847372e-04]
Sparsity at: 0.666190833959429
Epoch 156/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1419 - accuracy: 0.9784 - val_loss: 0.1943 - val_accuracy: 0.9636
[ 5.0166462e-34  0.0000000e+00  4.3932383e-10 ... -1.4112723e-02
  6.8439594e-03 -1.0257018e-02]
Sparsity at: 0.666190833959429
Epoch 157/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1392 - accuracy: 0.9797 - val_loss: 0.2009 - val_accuracy: 0.9645
[ 5.0166462e-34  0.0000000e+00 -2.6344167e-06 ... -4.0357420e-03
 -1.1591322e-02 -1.5631227e-02]
Sparsity at: 0.666190833959429
Epoch 158/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1396 - accuracy: 0.9792 - val_loss: 0.1950 - val_accuracy: 0.9642
[ 5.0166462e-34  0.0000000e+00  1.0939798e-09 ... -1.2036924e-02
 -1.5667819e-03 -1.1506682e-02]
Sparsity at: 0.666190833959429
Epoch 159/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1340 - accuracy: 0.9804 - val_loss: 0.1983 - val_accuracy: 0.9636
[ 5.0166462e-34  0.0000000e+00 -3.3897234e-09 ... -9.2213098e-03
 -8.3209975e-03 -9.8570054e-03]
Sparsity at: 0.666190833959429
Epoch 160/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1410 - accuracy: 0.9784 - val_loss: 0.2082 - val_accuracy: 0.9606
[ 5.0166462e-34  0.0000000e+00  6.3108283e-09 ... -1.6320411e-02
 -7.0029320e-03 -9.7289449e-03]
Sparsity at: 0.666190833959429
Epoch 161/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1401 - accuracy: 0.9785 - val_loss: 0.2060 - val_accuracy: 0.9611
[ 5.0166462e-34  0.0000000e+00  3.2755746e-11 ... -1.9962240e-02
 -3.2161083e-03 -8.7259011e-03]
Sparsity at: 0.666190833959429
Epoch 162/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1422 - accuracy: 0.9783 - val_loss: 0.1984 - val_accuracy: 0.9649
[ 5.0166462e-34  0.0000000e+00 -3.6269352e-09 ... -1.4742458e-02
 -9.9299923e-03 -9.6295439e-03]
Sparsity at: 0.666190833959429
Epoch 163/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1404 - accuracy: 0.9789 - val_loss: 0.2012 - val_accuracy: 0.9640 0s - loss:
[ 5.0166462e-34  0.0000000e+00  6.7924111e-13 ... -2.4452128e-02
 -1.8198988e-03 -2.6614077e-03]
Sparsity at: 0.666190833959429
Epoch 164/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1392 - accuracy: 0.9797 - val_loss: 0.1992 - val_accuracy: 0.9631
[ 5.0166462e-34  0.0000000e+00 -2.1372152e-08 ... -2.8700300e-02
 -1.0452360e-03 -5.4271794e-03]
Sparsity at: 0.666190833959429
Epoch 165/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1405 - accuracy: 0.9791 - val_loss: 0.2055 - val_accuracy: 0.9601
[ 5.0166462e-34  0.0000000e+00 -4.4497170e-13 ... -2.5781730e-02
  2.3416094e-03 -1.2780037e-03]
Sparsity at: 0.666190833959429
Epoch 166/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1362 - accuracy: 0.9795 - val_loss: 0.2041 - val_accuracy: 0.9608
[ 5.0166462e-34  0.0000000e+00  1.3665303e-07 ... -2.1483352e-02
 -1.0857211e-02  4.1082455e-03]
Sparsity at: 0.666190833959429
Epoch 167/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1411 - accuracy: 0.9785 - val_loss: 0.2248 - val_accuracy: 0.9540
[ 5.0166462e-34  0.0000000e+00  1.7451446e-12 ... -2.4913087e-02
 -4.2846669e-03 -3.5073892e-03]
Sparsity at: 0.666190833959429
Epoch 168/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1388 - accuracy: 0.9795 - val_loss: 0.1889 - val_accuracy: 0.9658
[ 5.0166462e-34  0.0000000e+00 -1.2982126e-06 ... -1.3665289e-02
 -1.4549807e-02 -1.5015699e-02]
Sparsity at: 0.666190833959429
Epoch 169/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1421 - accuracy: 0.9788 - val_loss: 0.2104 - val_accuracy: 0.9600
[ 5.0166462e-34  0.0000000e+00  8.4399840e-12 ... -2.2227364e-02
 -9.4286175e-03 -5.9940829e-03]
Sparsity at: 0.666190833959429
Epoch 170/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1419 - accuracy: 0.9785 - val_loss: 0.2022 - val_accuracy: 0.9611
[ 5.0166462e-34  0.0000000e+00 -1.2240731e-05 ... -2.2379842e-02
 -5.5304966e-03 -1.1375462e-02]
Sparsity at: 0.666190833959429
Epoch 171/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1396 - accuracy: 0.9787 - val_loss: 0.2275 - val_accuracy: 0.9545
[ 5.0166462e-34  0.0000000e+00  4.0207782e-11 ... -2.3706652e-02
 -1.4869887e-02 -1.5742071e-02]
Sparsity at: 0.666190833959429
Epoch 172/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.2255 - val_accuracy: 0.9567
[ 5.0166462e-34  0.0000000e+00  3.3586414e-07 ... -2.2746198e-02
 -1.8505011e-03 -1.3185559e-02]
Sparsity at: 0.666190833959429
Epoch 173/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1446 - accuracy: 0.9776 - val_loss: 0.2494 - val_accuracy: 0.9472
[ 5.0166462e-34  0.0000000e+00  2.6877958e-09 ... -8.9747962e-03
 -6.7959023e-03 -8.7169651e-03]
Sparsity at: 0.666190833959429
Epoch 174/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1380 - accuracy: 0.9797 - val_loss: 0.2292 - val_accuracy: 0.9531
[ 5.0166462e-34  0.0000000e+00  2.2312755e-12 ... -2.3496486e-02
 -6.9485772e-03 -1.3585822e-02]
Sparsity at: 0.666190833959429
Epoch 175/500
235/235 [==============================] - 5s 19ms/step - loss: 0.1399 - accuracy: 0.9786 - val_loss: 0.1941 - val_accuracy: 0.9615
[ 5.0166462e-34  0.0000000e+00  2.8667881e-08 ... -7.3658563e-03
 -4.9074353e-03 -9.2885997e-03]
Sparsity at: 0.666190833959429
Epoch 176/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1360 - accuracy: 0.9800 - val_loss: 0.1938 - val_accuracy: 0.9634
[ 5.01664622e-34  0.00000000e+00 -1.13291944e-13 ... -1.52935423e-02
  5.22935670e-03 -1.65853910e-02]
Sparsity at: 0.666190833959429
Epoch 177/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.2191 - val_accuracy: 0.9560
[ 5.0166462e-34  0.0000000e+00  8.5603986e-08 ... -2.6603287e-02
  2.4573365e-03 -2.1253873e-02]
Sparsity at: 0.666190833959429
Epoch 178/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1393 - accuracy: 0.9789 - val_loss: 0.1863 - val_accuracy: 0.9681
[ 5.0166462e-34  0.0000000e+00  3.5700476e-13 ... -2.3556106e-02
 -6.5545430e-03 -6.2924280e-04]
Sparsity at: 0.666190833959429
Epoch 179/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1388 - accuracy: 0.9798 - val_loss: 0.2111 - val_accuracy: 0.9585
[ 5.0166462e-34  0.0000000e+00  2.4601832e-07 ... -2.7308574e-02
 -4.7198711e-03 -5.8484529e-03]
Sparsity at: 0.666190833959429
Epoch 180/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1386 - accuracy: 0.9786 - val_loss: 0.1787 - val_accuracy: 0.9692
[ 5.0166462e-34  0.0000000e+00  2.5584331e-13 ... -7.2520124e-03
 -6.7848782e-03 -2.7476251e-03]
Sparsity at: 0.666190833959429
Epoch 181/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1387 - accuracy: 0.9795 - val_loss: 0.2056 - val_accuracy: 0.9603
[ 5.0166462e-34  0.0000000e+00  4.0222976e-06 ... -1.6783053e-02
  1.8976850e-03 -1.4208882e-02]
Sparsity at: 0.666190833959429
Epoch 182/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1390 - accuracy: 0.9790 - val_loss: 0.2564 - val_accuracy: 0.9451
[ 5.0166462e-34  0.0000000e+00  2.0630522e-11 ... -1.5837548e-02
 -5.7896036e-03 -1.1394896e-02]
Sparsity at: 0.666190833959429
Epoch 183/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1370 - accuracy: 0.9797 - val_loss: 0.1918 - val_accuracy: 0.9634
[ 5.0166462e-34  0.0000000e+00  2.9752009e-05 ... -2.2433538e-02
 -1.5479299e-03 -1.8344555e-02]
Sparsity at: 0.666190833959429
Epoch 184/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1365 - accuracy: 0.9791 - val_loss: 0.2368 - val_accuracy: 0.9497
[ 5.01664622e-34  0.00000000e+00  1.51760132e-10 ... -1.29018575e-02
  3.42086283e-03 -1.33878635e-02]
Sparsity at: 0.666190833959429
Epoch 185/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9793 - val_loss: 0.2352 - val_accuracy: 0.9497
[ 5.0166462e-34  0.0000000e+00  4.9737298e-07 ... -1.1504206e-02
  4.6023787e-03 -1.1970945e-02]
Sparsity at: 0.666190833959429
Epoch 186/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1400 - accuracy: 0.9791 - val_loss: 0.2036 - val_accuracy: 0.9618
[ 5.0166462e-34  0.0000000e+00 -2.3621656e-09 ... -8.8635217e-03
  1.9548852e-02 -1.1269747e-02]
Sparsity at: 0.666190833959429
Epoch 187/500
235/235 [==============================] - 5s 19ms/step - loss: 0.1379 - accuracy: 0.9798 - val_loss: 0.2146 - val_accuracy: 0.9601
[ 5.0166462e-34  0.0000000e+00  7.6479573e-11 ... -3.4631761e-03
  9.5573440e-03 -1.7066875e-02]
Sparsity at: 0.666190833959429
Epoch 188/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1363 - accuracy: 0.9796 - val_loss: 0.1877 - val_accuracy: 0.9646
[ 5.0166462e-34  0.0000000e+00 -1.6892114e-09 ... -6.7601171e-03
  1.0735933e-02 -9.9162692e-03]
Sparsity at: 0.666190833959429
Epoch 189/500
235/235 [==============================] - 4s 19ms/step - loss: 0.1388 - accuracy: 0.9788 - val_loss: 0.1918 - val_accuracy: 0.9635
[ 5.0166462e-34  0.0000000e+00 -1.8575834e-13 ... -1.0103357e-02
  6.3989991e-03 -5.3814324e-03]
Sparsity at: 0.666190833959429
Epoch 190/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1387 - accuracy: 0.9792 - val_loss: 0.1942 - val_accuracy: 0.9633
[ 5.016646e-34  0.000000e+00 -5.643024e-08 ... -5.884890e-03  9.144009e-04
 -9.003337e-03]
Sparsity at: 0.666190833959429
Epoch 191/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1360 - accuracy: 0.9797 - val_loss: 0.2116 - val_accuracy: 0.9584
[ 5.0166462e-34  0.0000000e+00 -6.5754731e-13 ... -3.4714427e-03
  1.1487506e-02 -3.1153958e-03]
Sparsity at: 0.666190833959429
Epoch 192/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1375 - accuracy: 0.9793 - val_loss: 0.2352 - val_accuracy: 0.9514
[ 5.0166462e-34  0.0000000e+00  6.8042425e-07 ... -6.5463083e-03
  1.7846018e-02 -1.0635743e-02]
Sparsity at: 0.666190833959429
Epoch 193/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1400 - accuracy: 0.9785 - val_loss: 0.1945 - val_accuracy: 0.9641
[ 5.0166462e-34  0.0000000e+00  1.2146959e-12 ... -5.3144572e-03
  1.3442005e-02 -1.1392523e-02]
Sparsity at: 0.666190833959429
Epoch 194/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9802 - val_loss: 0.1941 - val_accuracy: 0.9644
[ 5.0166462e-34  0.0000000e+00 -1.8330716e-06 ... -1.5116739e-03
  5.0201197e-03 -1.5820652e-02]
Sparsity at: 0.666190833959429
Epoch 195/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1361 - accuracy: 0.9797 - val_loss: 0.2186 - val_accuracy: 0.9568
[ 5.0166462e-34  0.0000000e+00  2.3349385e-11 ... -1.7341573e-02
  1.3230094e-02 -7.9691513e-03]
Sparsity at: 0.666190833959429
Epoch 196/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.1895 - val_accuracy: 0.9646
[ 5.0166462e-34  0.0000000e+00 -1.2386603e-05 ... -9.1612972e-03
  9.3596466e-03 -6.0775355e-03]
Sparsity at: 0.666190833959429
Epoch 197/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1362 - accuracy: 0.9798 - val_loss: 0.2263 - val_accuracy: 0.9529
[ 5.0166462e-34  0.0000000e+00 -4.0690201e-11 ... -1.4374478e-02
  1.6040500e-02 -4.7292556e-03]
Sparsity at: 0.666190833959429
Epoch 198/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1398 - accuracy: 0.9785 - val_loss: 0.1862 - val_accuracy: 0.9661
[ 5.0166462e-34  0.0000000e+00 -4.9593000e-05 ... -1.0642501e-02
  6.1431583e-03 -5.4059713e-03]
Sparsity at: 0.666190833959429
Epoch 199/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1369 - accuracy: 0.9797 - val_loss: 0.2385 - val_accuracy: 0.9486
[ 5.0166462e-34  0.0000000e+00  2.4752533e-10 ... -8.4419176e-03
  3.0181359e-03 -8.3414475e-03]
Sparsity at: 0.666190833959429
Epoch 200/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1387 - accuracy: 0.9797 - val_loss: 0.1900 - val_accuracy: 0.9654
[ 5.01664622e-34  0.00000000e+00  6.58159569e-11 ... -1.37163531e-02
  1.26233045e-02  1.03443069e-02]
Sparsity at: 0.666190833959429
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.0008035016810398754
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [1. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 1. 1. 0.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 2.352087186751218e-05
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.84183335
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.05744847457906932
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.179
tf.Tensor(
[[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 320s 16ms/step - loss: 0.1377 - accuracy: 0.9795 - val_loss: 0.1851 - val_accuracy: 0.9653
[ 5.0166462e-34  0.0000000e+00  4.8755098e-09 ... -1.3043190e-02
  4.4964748e-03 -5.4804692e-03]
Sparsity at: 0.666190833959429
Epoch 202/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1374 - accuracy: 0.9796 - val_loss: 0.1951 - val_accuracy: 0.9628
[ 5.0166462e-34  0.0000000e+00  6.6320593e-14 ... -1.7589958e-02
  2.0040069e-03 -8.5466104e-03]
Sparsity at: 0.666190833959429
Epoch 203/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1418 - accuracy: 0.9776 - val_loss: 0.2233 - val_accuracy: 0.9564
[ 5.01664622e-34  0.00000000e+00  2.25073222e-07 ... -1.64648555e-02
 -1.13766305e-02 -2.02886648e-02]
Sparsity at: 0.666190833959429
Epoch 204/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1343 - accuracy: 0.9804 - val_loss: 0.1888 - val_accuracy: 0.9651
[ 5.0166462e-34  0.0000000e+00  1.2062463e-12 ... -8.8463724e-03
  6.7390283e-05 -8.8814348e-03]
Sparsity at: 0.666190833959429
Epoch 205/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1387 - accuracy: 0.9789 - val_loss: 0.2194 - val_accuracy: 0.9541
[ 5.0166462e-34  0.0000000e+00  7.5119174e-06 ... -2.0170030e-03
 -2.6937143e-03 -1.1178119e-02]
Sparsity at: 0.666190833959429
Epoch 206/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1359 - accuracy: 0.9802 - val_loss: 0.2448 - val_accuracy: 0.9467
[ 5.0166462e-34  0.0000000e+00 -1.4233156e-11 ... -1.0380659e-02
  2.4750275e-03 -9.6675260e-03]
Sparsity at: 0.666190833959429
Epoch 207/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1378 - accuracy: 0.9789 - val_loss: 0.2178 - val_accuracy: 0.9590
[ 5.0166462e-34  0.0000000e+00  1.2507968e-04 ... -1.5268379e-02
 -2.2254761e-03 -1.1236632e-02]
Sparsity at: 0.666190833959429
Epoch 208/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1372 - accuracy: 0.9792 - val_loss: 0.1893 - val_accuracy: 0.9661
[ 5.0166462e-34  0.0000000e+00 -5.8355049e-10 ... -9.2871971e-03
 -3.8212744e-04 -1.4200823e-02]
Sparsity at: 0.666190833959429
Epoch 209/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1366 - accuracy: 0.9794 - val_loss: 0.2224 - val_accuracy: 0.9556
[ 5.0166462e-34  0.0000000e+00 -5.3764255e-09 ... -1.7078537e-02
  2.6902920e-03 -1.6608965e-02]
Sparsity at: 0.666190833959429
Epoch 210/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1371 - accuracy: 0.9789 - val_loss: 0.2162 - val_accuracy: 0.9556
[ 5.0166462e-34  0.0000000e+00  3.2753267e-10 ... -1.7577101e-02
  3.3908749e-03 -1.7387353e-02]
Sparsity at: 0.666190833959429
Epoch 211/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1380 - accuracy: 0.9791 - val_loss: 0.2367 - val_accuracy: 0.9520
[ 5.0166462e-34  0.0000000e+00  2.7855871e-12 ... -1.8230313e-02
  4.1310475e-03 -6.3017914e-03]
Sparsity at: 0.666190833959429
Epoch 212/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1350 - accuracy: 0.9799 - val_loss: 0.2018 - val_accuracy: 0.9614
[ 5.0166462e-34  0.0000000e+00 -2.3446454e-08 ... -1.3320859e-02
  1.4658483e-02 -6.6999830e-03]
Sparsity at: 0.666190833959429
Epoch 213/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9800 - val_loss: 0.2188 - val_accuracy: 0.9576
[ 5.0166462e-34  0.0000000e+00 -3.4396807e-13 ... -7.5290818e-03
  2.0089974e-03 -3.3436914e-03]
Sparsity at: 0.666190833959429
Epoch 214/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9797 - val_loss: 0.2184 - val_accuracy: 0.9555
[ 5.0166462e-34  0.0000000e+00 -8.3651230e-08 ... -7.7453237e-03
 -1.8298705e-03 -1.5288311e-02]
Sparsity at: 0.666190833959429
Epoch 215/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1396 - accuracy: 0.9777 - val_loss: 0.2546 - val_accuracy: 0.9473
[ 5.0166462e-34  0.0000000e+00  3.2415837e-13 ... -1.5400510e-02
  5.4168068e-03 -8.5148066e-03]
Sparsity at: 0.666190833959429
Epoch 216/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1392 - accuracy: 0.9786 - val_loss: 0.1908 - val_accuracy: 0.9619
[ 5.0166462e-34  0.0000000e+00  3.9851767e-07 ... -8.4415851e-03
 -2.3003173e-05 -5.5112964e-03]
Sparsity at: 0.666190833959429
Epoch 217/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1343 - accuracy: 0.9800 - val_loss: 0.1939 - val_accuracy: 0.9639
[ 5.0166462e-34  0.0000000e+00 -3.0048481e-12 ... -2.3809563e-02
 -4.9921237e-03 -6.3833883e-03]
Sparsity at: 0.666190833959429
Epoch 218/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1387 - accuracy: 0.9778 - val_loss: 0.2303 - val_accuracy: 0.9536
[ 5.0166462e-34  0.0000000e+00 -7.6785036e-06 ... -9.3865786e-03
  1.8736207e-03  2.1538506e-03]
Sparsity at: 0.666190833959429
Epoch 219/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1383 - accuracy: 0.9790 - val_loss: 0.1969 - val_accuracy: 0.9620
[ 5.0166462e-34  0.0000000e+00 -2.6505522e-11 ... -2.8311048e-02
 -5.1541807e-05 -1.5137208e-03]
Sparsity at: 0.666190833959429
Epoch 220/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9792 - val_loss: 0.2000 - val_accuracy: 0.9628
[ 5.0166462e-34  0.0000000e+00  1.4177978e-07 ... -1.3272017e-03
  5.7211183e-03 -8.4905652e-03]
Sparsity at: 0.666190833959429
Epoch 221/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1393 - accuracy: 0.9786 - val_loss: 0.2153 - val_accuracy: 0.9581
[ 5.0166462e-34  0.0000000e+00 -3.0743816e-09 ... -1.3453597e-02
 -5.1749282e-04 -1.3276461e-02]
Sparsity at: 0.666190833959429
Epoch 222/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1381 - accuracy: 0.9793 - val_loss: 0.1855 - val_accuracy: 0.9638
[ 5.0166462e-34  0.0000000e+00 -5.9684510e-14 ... -3.8985512e-03
  1.5372393e-03 -6.9678058e-03]
Sparsity at: 0.666190833959429
Epoch 223/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9793 - val_loss: 0.1879 - val_accuracy: 0.9646
[ 5.01664622e-34  0.00000000e+00 -1.12355004e-07 ... -4.09543468e-03
  3.52310226e-03  1.07571235e-04]
Sparsity at: 0.666190833959429
Epoch 224/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2018 - val_accuracy: 0.9625
[ 5.0166462e-34  0.0000000e+00  1.2780354e-13 ... -1.0804565e-03
 -3.9763697e-03  1.3977815e-02]
Sparsity at: 0.666190833959429
Epoch 225/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1366 - accuracy: 0.9786 - val_loss: 0.2036 - val_accuracy: 0.9611
[ 5.0166462e-34  0.0000000e+00 -8.2392512e-07 ... -1.2893980e-05
 -2.7332548e-03  2.2523210e-03]
Sparsity at: 0.666190833959429
Epoch 226/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1366 - accuracy: 0.9793 - val_loss: 0.2055 - val_accuracy: 0.9612
[ 5.0166462e-34  0.0000000e+00  8.9900830e-12 ... -1.5774058e-02
 -6.8947161e-03  4.6047298e-03]
Sparsity at: 0.666190833959429
Epoch 227/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1383 - accuracy: 0.9780 - val_loss: 0.2100 - val_accuracy: 0.9587
[ 5.0166462e-34  0.0000000e+00 -9.5743788e-05 ... -2.3246925e-02
  4.2233109e-03 -3.0906203e-03]
Sparsity at: 0.666190833959429
Epoch 228/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9803 - val_loss: 0.2068 - val_accuracy: 0.9609
[ 5.0166462e-34  0.0000000e+00 -9.7018643e-11 ... -1.2659587e-02
  3.0478816e-03 -1.0195106e-03]
Sparsity at: 0.666190833959429
Epoch 229/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1370 - accuracy: 0.9787 - val_loss: 0.2018 - val_accuracy: 0.9621
[ 5.0166462e-34  0.0000000e+00  2.3127650e-10 ... -1.0595206e-02
 -3.3647071e-03  1.1063647e-02]
Sparsity at: 0.666190833959429
Epoch 230/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1350 - accuracy: 0.9792 - val_loss: 0.2200 - val_accuracy: 0.9557
[ 5.0166462e-34  0.0000000e+00  8.8874952e-09 ... -1.7448664e-02
 -1.1313785e-02 -8.7357225e-04]
Sparsity at: 0.666190833959429
Epoch 231/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1329 - accuracy: 0.9802 - val_loss: 0.2070 - val_accuracy: 0.9594
[ 5.01664622e-34  0.00000000e+00  1.04616856e-13 ... -6.34946628e-03
  6.82687969e-04 -1.38360411e-02]
Sparsity at: 0.666190833959429
Epoch 232/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1329 - accuracy: 0.9802 - val_loss: 0.2007 - val_accuracy: 0.9604
[ 5.0166462e-34  0.0000000e+00 -1.6428581e-07 ... -8.2641831e-03
  2.8233682e-03 -9.6772099e-03]
Sparsity at: 0.666190833959429
Epoch 233/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9798 - val_loss: 0.1911 - val_accuracy: 0.9635
[ 5.0166462e-34  0.0000000e+00  5.3890833e-13 ... -3.1657279e-03
 -4.1182539e-03 -5.2120602e-03]
Sparsity at: 0.666190833959429
Epoch 234/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.2087 - val_accuracy: 0.9623
[ 5.0166462e-34  0.0000000e+00 -2.6554526e-06 ... -2.8246432e-03
  1.4247248e-03 -5.9326608e-03]
Sparsity at: 0.666190833959429
Epoch 235/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9794 - val_loss: 0.2071 - val_accuracy: 0.9576
[ 5.0166462e-34  0.0000000e+00  1.6300149e-11 ... -8.6762160e-03
 -3.8412286e-03 -1.4873040e-02]
Sparsity at: 0.666190833959429
Epoch 236/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1394 - accuracy: 0.9784 - val_loss: 0.2336 - val_accuracy: 0.9543
[5.0166462e-34 0.0000000e+00 1.0179226e-12 ... 1.7033176e-03 9.5056588e-05
 1.4884573e-03]
Sparsity at: 0.666190833959429
Epoch 237/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.2159 - val_accuracy: 0.9566
[ 5.0166462e-34  0.0000000e+00  2.0724180e-08 ... -7.5409352e-04
 -4.3460294e-03  2.4321808e-03]
Sparsity at: 0.666190833959429
Epoch 238/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1364 - accuracy: 0.9788 - val_loss: 0.1938 - val_accuracy: 0.9626
[ 5.01664622e-34  0.00000000e+00  1.08640514e-13 ... -4.23583435e-03
 -4.96472884e-03  5.31715713e-03]
Sparsity at: 0.666190833959429
Epoch 239/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9801 - val_loss: 0.1937 - val_accuracy: 0.9631
[ 5.0166462e-34  0.0000000e+00  1.9580233e-05 ... -1.3631639e-02
 -6.0756169e-03 -1.2440525e-03]
Sparsity at: 0.666190833959429
Epoch 240/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1367 - accuracy: 0.9785 - val_loss: 0.1894 - val_accuracy: 0.9652
[ 5.0166462e-34  0.0000000e+00  1.1746920e-10 ... -1.3547566e-02
 -9.0799510e-04 -4.4999793e-03]
Sparsity at: 0.666190833959429
Epoch 241/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9800 - val_loss: 0.1846 - val_accuracy: 0.9664
[ 5.0166462e-34  0.0000000e+00 -1.5227401e-15 ... -9.5827887e-03
 -3.1739252e-03  3.7934701e-03]
Sparsity at: 0.666190833959429
Epoch 242/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1365 - accuracy: 0.9788 - val_loss: 0.1951 - val_accuracy: 0.9625
[ 5.0166462e-34  0.0000000e+00 -1.1672378e-07 ... -8.2868366e-03
 -7.1084765e-03 -8.3195716e-03]
Sparsity at: 0.666190833959429
Epoch 243/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1344 - accuracy: 0.9794 - val_loss: 0.2223 - val_accuracy: 0.9545
[ 5.0166462e-34  0.0000000e+00 -1.0759066e-12 ... -7.5666057e-03
  5.6932876e-03 -8.3142025e-03]
Sparsity at: 0.666190833959429
Epoch 244/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1370 - accuracy: 0.9791 - val_loss: 0.1992 - val_accuracy: 0.9610
[ 5.0166462e-34  0.0000000e+00 -9.7063530e-06 ... -1.6686749e-02
  1.3403445e-02 -7.5690686e-03]
Sparsity at: 0.666190833959429
Epoch 245/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9805 - val_loss: 0.2073 - val_accuracy: 0.9612
[ 5.0166462e-34  0.0000000e+00 -1.7113921e-10 ... -2.1975081e-02
  1.9176187e-02 -2.6952343e-03]
Sparsity at: 0.666190833959429
Epoch 246/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1413 - accuracy: 0.9778 - val_loss: 0.2215 - val_accuracy: 0.9552
[ 5.0166462e-34  0.0000000e+00 -7.8157289e-15 ... -1.2002042e-02
  6.7663607e-03 -1.1201041e-02]
Sparsity at: 0.666190833959429
Epoch 247/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9801 - val_loss: 0.2019 - val_accuracy: 0.9611
[ 5.0166462e-34  0.0000000e+00  2.0312747e-09 ... -1.4106735e-02
  2.4849037e-02 -3.7968036e-04]
Sparsity at: 0.666190833959429
Epoch 248/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1408 - accuracy: 0.9776 - val_loss: 0.1986 - val_accuracy: 0.9606
[ 5.0166462e-34  0.0000000e+00  3.8714745e-13 ... -1.4783944e-02
  2.0893378e-02 -1.0648832e-02]
Sparsity at: 0.666190833959429
Epoch 249/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9806 - val_loss: 0.1940 - val_accuracy: 0.9627
[ 5.0166462e-34  0.0000000e+00 -5.2437619e-05 ... -5.1252232e-03
  1.4152872e-02 -7.4495953e-03]
Sparsity at: 0.666190833959429
Epoch 250/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1374 - accuracy: 0.9788 - val_loss: 0.2244 - val_accuracy: 0.9537
[ 5.0166462e-34  0.0000000e+00 -4.2562615e-10 ... -2.0444019e-02
  1.6940340e-02 -1.3357890e-02]
Sparsity at: 0.666190833959429
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.005424971972829762
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [1. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 1. 1. 0.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.0050160842121825255
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.84183335
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.06822590968293785
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.179
tf.Tensor(
[[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 289s 16ms/step - loss: 0.1336 - accuracy: 0.9800 - val_loss: 0.1841 - val_accuracy: 0.9667
[ 5.0166462e-34  0.0000000e+00  4.1078277e-15 ... -8.8087656e-03
  2.8525928e-02 -8.2089501e-03]
Sparsity at: 0.666190833959429
Epoch 252/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1311 - accuracy: 0.9796 - val_loss: 0.2054 - val_accuracy: 0.9571
[ 5.0166462e-34  0.0000000e+00  2.8870778e-07 ... -2.6478749e-03
  1.0651431e-02 -9.5302248e-03]
Sparsity at: 0.666190833959429
Epoch 253/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9796 - val_loss: 0.2103 - val_accuracy: 0.9578
[ 5.0166462e-34  0.0000000e+00 -6.0715538e-13 ... -3.3162530e-03
  8.2712178e-04 -1.0158852e-02]
Sparsity at: 0.666190833959429
Epoch 254/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9795 - val_loss: 0.2110 - val_accuracy: 0.9584
[ 5.0166462e-34  0.0000000e+00 -1.0203345e-05 ... -1.5708815e-02
  2.0667030e-03 -6.5091378e-03]
Sparsity at: 0.666190833959429
Epoch 255/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1350 - accuracy: 0.9790 - val_loss: 0.1953 - val_accuracy: 0.9631
[ 5.0166462e-34  0.0000000e+00 -1.7835675e-10 ... -5.9302510e-03
  4.5689554e-03 -6.2460299e-03]
Sparsity at: 0.6661945905334336
Epoch 256/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9794 - val_loss: 0.2231 - val_accuracy: 0.9535
[ 5.0166462e-34  0.0000000e+00  2.7929162e-11 ... -1.2968758e-03
 -6.9119567e-03 -1.4083045e-02]
Sparsity at: 0.6661945905334336
Epoch 257/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9791 - val_loss: 0.2260 - val_accuracy: 0.9535
[ 5.0166462e-34  0.0000000e+00  1.4479717e-08 ... -3.5293407e-03
 -2.1664179e-03 -2.5572847e-03]
Sparsity at: 0.6661945905334336
Epoch 258/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.2060 - val_accuracy: 0.9572
[ 5.0166462e-34  0.0000000e+00 -1.4109815e-13 ... -1.3492821e-02
 -3.9002132e-03 -6.8006635e-04]
Sparsity at: 0.6661945905334336
Epoch 259/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9804 - val_loss: 0.2166 - val_accuracy: 0.9543
[ 5.01664622e-34  0.00000000e+00 -2.57906464e-07 ... -8.65873974e-03
 -3.20448889e-03 -1.48104215e-02]
Sparsity at: 0.6661945905334336
Epoch 260/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1359 - accuracy: 0.9789 - val_loss: 0.2219 - val_accuracy: 0.9553
[ 5.0166462e-34  0.0000000e+00  4.1291861e-13 ... -3.8195117e-03
 -7.9606066e-04  1.2356406e-02]
Sparsity at: 0.6661945905334336
Epoch 261/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1355 - accuracy: 0.9789 - val_loss: 0.2206 - val_accuracy: 0.9543
[ 5.0166462e-34  0.0000000e+00 -2.1737378e-07 ... -6.8226671e-03
 -1.5340904e-03 -6.8431385e-03]
Sparsity at: 0.6661945905334336
Epoch 262/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1312 - accuracy: 0.9804 - val_loss: 0.2316 - val_accuracy: 0.9526
[ 5.0166462e-34  0.0000000e+00 -1.6277379e-11 ... -5.3265733e-03
 -5.0257863e-03 -6.2855678e-03]
Sparsity at: 0.6661945905334336
Epoch 263/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1395 - accuracy: 0.9781 - val_loss: 0.1996 - val_accuracy: 0.9633
[ 5.0166462e-34  0.0000000e+00  6.1380160e-06 ... -7.3786494e-03
 -5.7332884e-03 -1.5937607e-03]
Sparsity at: 0.6661945905334336
Epoch 264/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.2046 - val_accuracy: 0.9602
[ 5.0166462e-34  0.0000000e+00  1.0025018e-10 ... -1.4628764e-02
 -8.5540852e-03 -8.9091081e-03]
Sparsity at: 0.6661945905334336
Epoch 265/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9791 - val_loss: 0.2181 - val_accuracy: 0.9566
[ 5.0166462e-34  0.0000000e+00 -4.2933354e-07 ... -1.5277706e-02
 -5.7815476e-03 -7.1723065e-03]
Sparsity at: 0.6661945905334336
Epoch 266/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9792 - val_loss: 0.1945 - val_accuracy: 0.9623
[ 5.0166462e-34  0.0000000e+00 -2.1603159e-09 ... -6.6790855e-03
 -9.6520623e-03  4.4465205e-03]
Sparsity at: 0.6661945905334336
Epoch 267/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1341 - accuracy: 0.9790 - val_loss: 0.2378 - val_accuracy: 0.9478
[ 5.0166462e-34  0.0000000e+00 -2.8488002e-12 ... -3.6254507e-03
 -5.0614318e-03  4.6373978e-03]
Sparsity at: 0.6661945905334336
Epoch 268/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9787 - val_loss: 0.1973 - val_accuracy: 0.9614
[ 5.0166462e-34  0.0000000e+00 -2.6763981e-08 ... -1.2658463e-02
  1.3997826e-03  5.0675757e-03]
Sparsity at: 0.6661945905334336
Epoch 269/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9793 - val_loss: 0.2063 - val_accuracy: 0.9617
[ 5.0166462e-34  0.0000000e+00  2.7036560e-13 ... -1.2725029e-02
 -8.3470428e-03  6.7114620e-03]
Sparsity at: 0.6661945905334336
Epoch 270/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9790 - val_loss: 0.2041 - val_accuracy: 0.9571
[ 5.0166462e-34  0.0000000e+00  8.1289244e-08 ... -1.2470415e-02
 -1.1167065e-02  1.4550192e-03]
Sparsity at: 0.6661945905334336
Epoch 271/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9793 - val_loss: 0.2202 - val_accuracy: 0.9539
[ 5.0166462e-34  0.0000000e+00 -9.7513989e-13 ... -1.4343457e-02
 -5.7524391e-03  5.1763379e-03]
Sparsity at: 0.6661945905334336
Epoch 272/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.2114 - val_accuracy: 0.9582
[ 5.0166462e-34  0.0000000e+00 -6.7188330e-07 ... -1.0697252e-02
  1.8895425e-02 -8.6437650e-03]
Sparsity at: 0.6661945905334336
Epoch 273/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1354 - accuracy: 0.9790 - val_loss: 0.1966 - val_accuracy: 0.9638
[ 5.0166462e-34  0.0000000e+00 -4.6940229e-12 ... -2.1669054e-02
  1.2107472e-02 -3.6203754e-04]
Sparsity at: 0.6661945905334336
Epoch 274/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9792 - val_loss: 0.1871 - val_accuracy: 0.9634
[ 5.0166462e-34  0.0000000e+00  2.8189384e-06 ... -2.0712554e-02
  1.3943182e-03  9.4227316e-03]
Sparsity at: 0.6661945905334336
Epoch 275/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9794 - val_loss: 0.1942 - val_accuracy: 0.9635
[ 5.01664622e-34  0.00000000e+00  1.32951115e-11 ... -1.55939404e-02
 -2.08610989e-04  1.66784953e-02]
Sparsity at: 0.6661945905334336
Epoch 276/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9795 - val_loss: 0.2023 - val_accuracy: 0.9582
[ 5.0166462e-34  0.0000000e+00  4.8257098e-06 ...  8.2011968e-03
 -3.3854016e-03 -1.5099021e-04]
Sparsity at: 0.6661945905334336
Epoch 277/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9791 - val_loss: 0.2028 - val_accuracy: 0.9603
[ 5.0166462e-34  0.0000000e+00 -4.3871459e-11 ... -8.2591819e-03
  4.6813036e-03 -2.4894467e-03]
Sparsity at: 0.6661945905334336
Epoch 278/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1309 - accuracy: 0.9805 - val_loss: 0.2121 - val_accuracy: 0.9570
[ 5.0166462e-34  0.0000000e+00 -2.6101266e-05 ... -5.0516636e-03
  2.9433434e-04 -6.8966546e-03]
Sparsity at: 0.6661945905334336
Epoch 279/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9784 - val_loss: 0.1909 - val_accuracy: 0.9636
[ 5.0166462e-34  0.0000000e+00  1.7483674e-11 ... -2.7394076e-03
  5.3594005e-03 -1.7638268e-02]
Sparsity at: 0.6661945905334336
Epoch 280/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9797 - val_loss: 0.2015 - val_accuracy: 0.9612: 0.1332 - 
[ 5.0166462e-34  0.0000000e+00  5.0985022e-05 ... -3.3831631e-03
  2.7725506e-03 -1.7784374e-02]
Sparsity at: 0.6661945905334336
Epoch 281/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1358 - accuracy: 0.9790 - val_loss: 0.2136 - val_accuracy: 0.9588
[ 5.0166462e-34  0.0000000e+00  2.5149888e-10 ...  3.0595744e-03
  3.5595153e-03 -1.2285816e-02]
Sparsity at: 0.6661945905334336
Epoch 282/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.1904 - val_accuracy: 0.9633
[ 5.0166462e-34  0.0000000e+00 -8.8632194e-05 ...  2.2162162e-03
  3.3864758e-03 -2.2696542e-02]
Sparsity at: 0.6661945905334336
Epoch 283/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9787 - val_loss: 0.2203 - val_accuracy: 0.9538
[ 5.0166462e-34  0.0000000e+00 -2.5969096e-10 ...  3.0518125e-03
  3.7076464e-03 -4.7855619e-03]
Sparsity at: 0.6661945905334336
Epoch 284/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1313 - accuracy: 0.9798 - val_loss: 0.1959 - val_accuracy: 0.9625s - loss: 0.1321 
[ 5.016646e-34  0.000000e+00  1.280261e-09 ... -7.025521e-03  2.736197e-03
 -6.886986e-03]
Sparsity at: 0.6661945905334336
Epoch 285/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1356 - accuracy: 0.9787 - val_loss: 0.2091 - val_accuracy: 0.9595
[ 5.0166462e-34  0.0000000e+00 -5.4844018e-09 ... -3.6379043e-03
  1.3934745e-02 -1.5794719e-02]
Sparsity at: 0.6661945905334336
Epoch 286/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1311 - accuracy: 0.9797 - val_loss: 0.2239 - val_accuracy: 0.9526
[ 5.01664622e-34  0.00000000e+00  6.99845862e-14 ... -1.54469395e-02
  6.91636419e-03 -9.79533698e-03]
Sparsity at: 0.6661945905334336
Epoch 287/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9799 - val_loss: 0.1943 - val_accuracy: 0.9618
[ 5.0166462e-34  0.0000000e+00  8.4234443e-08 ... -1.9879712e-02
  1.1739079e-02 -1.1992269e-02]
Sparsity at: 0.6661945905334336
Epoch 288/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9791 - val_loss: 0.1884 - val_accuracy: 0.9626
[ 5.0166462e-34  0.0000000e+00  1.1066945e-12 ... -5.8060437e-03
  7.9021035e-03 -1.0213419e-02]
Sparsity at: 0.6661945905334336
Epoch 289/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9783 - val_loss: 0.2043 - val_accuracy: 0.9592
[ 5.0166462e-34  0.0000000e+00 -3.3072440e-06 ... -1.1217592e-02
  5.5181463e-03 -1.1749640e-02]
Sparsity at: 0.6661945905334336
Epoch 290/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9795 - val_loss: 0.2013 - val_accuracy: 0.9617
[ 5.0166462e-34  0.0000000e+00  1.8652187e-11 ... -1.5522042e-02
  1.5066974e-02 -1.4702387e-02]
Sparsity at: 0.6661945905334336
Epoch 291/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1329 - accuracy: 0.9792 - val_loss: 0.2118 - val_accuracy: 0.9577
[ 5.0166462e-34  0.0000000e+00 -3.2374588e-05 ... -1.1716704e-02
  1.4093393e-02 -1.0436050e-02]
Sparsity at: 0.6661945905334336
Epoch 292/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9793 - val_loss: 0.2150 - val_accuracy: 0.9570
[ 5.0166462e-34  0.0000000e+00 -1.9133001e-11 ... -1.5228307e-02
  7.2818608e-03  1.3726990e-03]
Sparsity at: 0.6661945905334336
Epoch 293/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9790 - val_loss: 0.2225 - val_accuracy: 0.9516
[ 5.01664622e-34  0.00000000e+00  1.21524696e-04 ... -1.05045643e-02
  1.19396467e-02 -3.99276614e-03]
Sparsity at: 0.6661945905334336
Epoch 294/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9799 - val_loss: 0.2292 - val_accuracy: 0.9514
[ 5.0166462e-34  0.0000000e+00  1.0207988e-09 ... -1.2707563e-02
  1.5120127e-02 -4.1666930e-03]
Sparsity at: 0.6661945905334336
Epoch 295/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1341 - accuracy: 0.9789 - val_loss: 0.2195 - val_accuracy: 0.9576
[ 5.0166462e-34  0.0000000e+00  1.6728348e-09 ... -8.4953010e-03
  1.1073551e-02  5.9924703e-03]
Sparsity at: 0.6661945905334336
Epoch 296/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9789 - val_loss: 0.2403 - val_accuracy: 0.9507
[ 5.016646e-34  0.000000e+00  3.779241e-09 ... -9.129332e-03  6.536076e-03
  5.635218e-03]
Sparsity at: 0.6661945905334336
Epoch 297/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9796 - val_loss: 0.2002 - val_accuracy: 0.9583
[ 5.01664622e-34  0.00000000e+00 -3.94146684e-13 ... -1.18225627e-02
  1.12283705e-02 -5.90698421e-03]
Sparsity at: 0.6661945905334336
Epoch 298/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9792 - val_loss: 0.2207 - val_accuracy: 0.9555
[ 5.0166462e-34  0.0000000e+00  6.2766681e-08 ... -1.5015728e-02
  1.5950866e-02 -1.0268219e-02]
Sparsity at: 0.6661945905334336
Epoch 299/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9791 - val_loss: 0.2237 - val_accuracy: 0.9549
[ 5.0166462e-34  0.0000000e+00 -4.4277928e-13 ... -2.1599604e-02
  1.1957290e-02 -1.2891023e-02]
Sparsity at: 0.6661945905334336
Epoch 300/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1332 - accuracy: 0.9793 - val_loss: 0.1796 - val_accuracy: 0.9648
[ 5.0166462e-34  0.0000000e+00  7.6373283e-08 ... -2.4667840e-02
  2.3287600e-02 -1.1523353e-02]
Sparsity at: 0.6661945905334336
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.011396164229506844
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [1. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 1. 1. 0.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.01809298950022642
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.84183335
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.0786105740883043
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.179
tf.Tensor(
[[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 285s 16ms/step - loss: 0.1324 - accuracy: 0.9792 - val_loss: 0.2089 - val_accuracy: 0.9574
[ 5.0166462e-34  0.0000000e+00 -2.3702320e-12 ... -1.8242219e-02
  2.2528322e-02 -2.3960277e-02]
Sparsity at: 0.6661945905334336
Epoch 302/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1321 - accuracy: 0.9798 - val_loss: 0.2126 - val_accuracy: 0.9558
[ 5.0166462e-34  0.0000000e+00  6.1803826e-07 ... -1.4370861e-02
  2.2725252e-02 -2.3305086e-02]
Sparsity at: 0.6661945905334336
Epoch 303/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1336 - accuracy: 0.9790 - val_loss: 0.2051 - val_accuracy: 0.9579
[ 5.0166462e-34  0.0000000e+00 -9.1631407e-12 ... -1.2030389e-02
  2.0712299e-02 -1.7406259e-02]
Sparsity at: 0.6661945905334336
Epoch 304/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1343 - accuracy: 0.9794 - val_loss: 0.2236 - val_accuracy: 0.9562
[ 5.0166462e-34  0.0000000e+00 -9.8235587e-06 ... -6.9225444e-03
  2.2390695e-02 -2.0534968e-02]
Sparsity at: 0.6661945905334336
Epoch 305/500
235/235 [==============================] - 5s 19ms/step - loss: 0.1322 - accuracy: 0.9797 - val_loss: 0.2087 - val_accuracy: 0.9581
[ 5.0166462e-34  0.0000000e+00  4.8186344e-11 ... -8.1087612e-03
  1.4207467e-02 -2.3832196e-02]
Sparsity at: 0.6661945905334336
Epoch 306/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1314 - accuracy: 0.9799 - val_loss: 0.1922 - val_accuracy: 0.9632
[ 5.0166462e-34  0.0000000e+00 -1.7762761e-05 ... -1.7818516e-02
  1.3395157e-02 -1.7064344e-02]
Sparsity at: 0.6661945905334336
Epoch 307/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9791 - val_loss: 0.1915 - val_accuracy: 0.9647
[ 5.0166462e-34  0.0000000e+00  6.2061280e-11 ... -1.5127133e-02
 -1.8796418e-06 -2.1614496e-02]
Sparsity at: 0.6661945905334336
Epoch 308/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1333 - accuracy: 0.9786 - val_loss: 0.2179 - val_accuracy: 0.9520
[ 5.0166462e-34  0.0000000e+00 -1.3301493e-05 ... -2.7700020e-02
  1.0548029e-02 -1.7826401e-02]
Sparsity at: 0.6661945905334336
Epoch 309/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1354 - accuracy: 0.9789 - val_loss: 0.1897 - val_accuracy: 0.9638
[ 5.0166462e-34  0.0000000e+00  8.5978347e-10 ... -1.8092519e-02
  9.2711914e-03 -1.6787298e-02]
Sparsity at: 0.6661945905334336
Epoch 310/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1336 - accuracy: 0.9796 - val_loss: 0.2108 - val_accuracy: 0.9590
[ 5.0166462e-34  0.0000000e+00 -4.6183862e-11 ... -2.2075607e-02
  1.5041044e-03 -2.1821933e-02]
Sparsity at: 0.6661945905334336
Epoch 311/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9797 - val_loss: 0.2215 - val_accuracy: 0.9553
[ 5.0166462e-34  0.0000000e+00 -3.9882391e-09 ... -1.5371803e-02
  1.5222040e-02 -1.1591384e-02]
Sparsity at: 0.6661945905334336
Epoch 312/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9787 - val_loss: 0.2160 - val_accuracy: 0.9573
[ 5.01664622e-34  0.00000000e+00  1.06812196e-13 ... -1.46025969e-02
  7.06102187e-03 -1.72168072e-02]
Sparsity at: 0.6661945905334336
Epoch 313/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1356 - accuracy: 0.9791 - val_loss: 0.2053 - val_accuracy: 0.9600
[ 5.0166462e-34  0.0000000e+00 -3.1783111e-07 ... -2.0769602e-02
  1.8100586e-02 -1.4454367e-02]
Sparsity at: 0.6661945905334336
Epoch 314/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9804 - val_loss: 0.2311 - val_accuracy: 0.9509
[ 5.0166462e-34  0.0000000e+00 -1.9262818e-12 ... -1.1952524e-02
  1.1275795e-02 -1.2317302e-02]
Sparsity at: 0.6661945905334336
Epoch 315/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1304 - accuracy: 0.9803 - val_loss: 0.2079 - val_accuracy: 0.9607
[ 5.0166462e-34  0.0000000e+00 -1.0100735e-05 ... -8.5355360e-03
 -2.4386854e-03 -1.3146252e-02]
Sparsity at: 0.6661945905334336
Epoch 316/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9793 - val_loss: 0.2237 - val_accuracy: 0.9552
[ 5.0166462e-34  0.0000000e+00 -5.3484647e-12 ... -3.9367643e-03
  8.4361220e-03 -2.6458047e-02]
Sparsity at: 0.6661945905334336
Epoch 317/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9797 - val_loss: 0.1890 - val_accuracy: 0.9647
[ 5.0166462e-34  0.0000000e+00  4.5105378e-05 ... -1.1967882e-02
  1.4722745e-02 -2.1711055e-02]
Sparsity at: 0.6661945905334336
Epoch 318/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9803 - val_loss: 0.2164 - val_accuracy: 0.9566
[ 5.0166462e-34  0.0000000e+00 -1.1884742e-09 ...  5.2370979e-03
  1.4491105e-02 -1.4545539e-02]
Sparsity at: 0.6661945905334336
Epoch 319/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1310 - accuracy: 0.9805 - val_loss: 0.1903 - val_accuracy: 0.9655
[ 5.0166462e-34  0.0000000e+00  1.1941486e-12 ... -9.0121105e-03
  2.4271395e-02 -1.3893281e-02]
Sparsity at: 0.6661945905334336
Epoch 320/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9801 - val_loss: 0.2259 - val_accuracy: 0.9549
[ 5.0166462e-34  0.0000000e+00 -1.8264657e-08 ... -1.6260551e-02
  8.4577557e-03 -1.8699598e-02]
Sparsity at: 0.6661945905334336
Epoch 321/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.2077 - val_accuracy: 0.9589
[ 5.0166462e-34  0.0000000e+00  1.3092449e-13 ... -2.1273766e-02
  1.6674172e-02 -1.3141829e-02]
Sparsity at: 0.6661945905334336
Epoch 322/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9793 - val_loss: 0.2228 - val_accuracy: 0.9563
[ 5.0166462e-34  0.0000000e+00  6.2886647e-07 ... -1.6637189e-02
  2.2465816e-02 -8.5178306e-03]
Sparsity at: 0.6661945905334336
Epoch 323/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9794 - val_loss: 0.1928 - val_accuracy: 0.9651
[ 5.0166462e-34  0.0000000e+00 -1.3348803e-11 ... -2.3974145e-02
  3.2854218e-02 -2.9167530e-04]
Sparsity at: 0.6661945905334336
Epoch 324/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1293 - accuracy: 0.9801 - val_loss: 0.2229 - val_accuracy: 0.9548
[ 5.0166462e-34  0.0000000e+00 -1.9049575e-04 ... -1.9254422e-02
  2.5088148e-02 -1.4602958e-02]
Sparsity at: 0.6661945905334336
Epoch 325/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9789 - val_loss: 0.1919 - val_accuracy: 0.9633
[ 5.0166462e-34  0.0000000e+00  4.7352333e-10 ... -1.4094747e-02
  2.1239776e-02 -5.9801517e-03]
Sparsity at: 0.6661945905334336
Epoch 326/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1305 - accuracy: 0.9801 - val_loss: 0.2142 - val_accuracy: 0.9561
[ 5.01664622e-34  0.00000000e+00 -5.37947551e-12 ... -1.17453495e-02
  3.03752674e-03 -1.45679880e-02]
Sparsity at: 0.6661945905334336
Epoch 327/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9791 - val_loss: 0.2294 - val_accuracy: 0.9546
[ 5.0166462e-34  0.0000000e+00  1.8562138e-08 ... -1.2465824e-02
  1.2475618e-02 -7.5133797e-03]
Sparsity at: 0.6661945905334336
Epoch 328/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1303 - accuracy: 0.9801 - val_loss: 0.2050 - val_accuracy: 0.9592
[ 5.0166462e-34  0.0000000e+00 -9.1033768e-14 ... -1.4011636e-02
  1.2803468e-02 -6.8804398e-03]
Sparsity at: 0.6661945905334336
Epoch 329/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1316 - accuracy: 0.9796 - val_loss: 0.2063 - val_accuracy: 0.9621
[ 5.0166462e-34  0.0000000e+00 -5.7358716e-07 ... -1.0516850e-02
  3.2680419e-03 -6.1329873e-03]
Sparsity at: 0.6661945905334336
Epoch 330/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1329 - accuracy: 0.9791 - val_loss: 0.2045 - val_accuracy: 0.9583
[ 5.0166462e-34  0.0000000e+00  3.4191283e-12 ... -2.0310344e-02
 -4.4605369e-03 -5.5692075e-03]
Sparsity at: 0.6661945905334336
Epoch 331/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9795 - val_loss: 0.2181 - val_accuracy: 0.9556
[ 5.0166462e-34  0.0000000e+00 -1.2942956e-05 ... -1.6684642e-02
 -4.1639176e-03 -5.8261766e-03]
Sparsity at: 0.6661945905334336
Epoch 332/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1312 - accuracy: 0.9798 - val_loss: 0.2107 - val_accuracy: 0.9583
[ 5.0166462e-34  0.0000000e+00  1.5556086e-10 ... -1.0945871e-02
 -1.0664497e-03 -4.7471542e-03]
Sparsity at: 0.6661945905334336
Epoch 333/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9789 - val_loss: 0.2073 - val_accuracy: 0.9590
[ 5.016646e-34  0.000000e+00  2.748752e-15 ... -2.041337e-02 -4.115544e-03
 -8.328827e-03]
Sparsity at: 0.6661945905334336
Epoch 334/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9803 - val_loss: 0.2079 - val_accuracy: 0.9572
[ 5.0166462e-34  0.0000000e+00  6.0321099e-09 ... -1.4147059e-02
  1.0227805e-02 -1.7437987e-02]
Sparsity at: 0.6661945905334336
Epoch 335/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.1818 - val_accuracy: 0.9663
[ 5.0166462e-34  0.0000000e+00  4.8706904e-13 ... -6.2247445e-03
  9.5995078e-03 -2.0691784e-02]
Sparsity at: 0.6661945905334336
Epoch 336/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1308 - accuracy: 0.9797 - val_loss: 0.1946 - val_accuracy: 0.9620
[ 5.0166462e-34  0.0000000e+00  2.0230744e-05 ... -4.7127861e-03
  8.1583625e-03  9.2168443e-04]
Sparsity at: 0.6661945905334336
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9797 - val_loss: 0.2124 - val_accuracy: 0.9556
[ 5.0166462e-34  0.0000000e+00 -1.2016507e-10 ... -9.3236389e-03
 -1.8080815e-03 -4.0031327e-03]
Sparsity at: 0.6661945905334336
Epoch 338/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9792 - val_loss: 0.2202 - val_accuracy: 0.9547
[ 5.0166462e-34  0.0000000e+00  8.8193949e-12 ... -1.1737032e-02
  3.8471487e-03  1.0326997e-02]
Sparsity at: 0.6661945905334336
Epoch 339/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9800 - val_loss: 0.2434 - val_accuracy: 0.9468
[ 5.01664622e-34  0.00000000e+00  1.63267266e-09 ... -1.06334975e-02
  5.82451420e-03 -1.63395831e-03]
Sparsity at: 0.6661945905334336
Epoch 340/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9791 - val_loss: 0.2107 - val_accuracy: 0.9562
[ 5.0166462e-34  0.0000000e+00  4.0483086e-14 ... -1.1281181e-02
  1.0191390e-03 -6.1182515e-03]
Sparsity at: 0.6661945905334336
Epoch 341/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9798 - val_loss: 0.2077 - val_accuracy: 0.9566
[ 5.0166462e-34  0.0000000e+00  6.2784312e-07 ... -1.2749154e-02
  1.9580455e-02 -9.2525557e-03]
Sparsity at: 0.6661945905334336
Epoch 342/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1335 - accuracy: 0.9794 - val_loss: 0.2046 - val_accuracy: 0.9598
[ 5.0166462e-34  0.0000000e+00 -2.9631467e-12 ... -1.9894438e-02
  1.7226735e-02 -3.8211532e-03]
Sparsity at: 0.6661945905334336
Epoch 343/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1336 - accuracy: 0.9790 - val_loss: 0.2066 - val_accuracy: 0.9595
[ 5.0166462e-34  0.0000000e+00  2.1980832e-06 ... -2.3388274e-02
  1.4149473e-02 -1.8189080e-02]
Sparsity at: 0.6661945905334336
Epoch 344/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9801 - val_loss: 0.2154 - val_accuracy: 0.9568
[ 5.0166462e-34  0.0000000e+00  8.5287624e-11 ... -1.5402656e-02
  5.0745727e-03 -1.6026553e-02]
Sparsity at: 0.6661945905334336
Epoch 345/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1289 - accuracy: 0.9803 - val_loss: 0.2084 - val_accuracy: 0.9583
[ 5.0166462e-34  0.0000000e+00 -3.5860998e-05 ... -1.4443289e-02
  9.3781678e-03  8.0649846e-04]
Sparsity at: 0.6661945905334336
Epoch 346/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1343 - accuracy: 0.9788 - val_loss: 0.2032 - val_accuracy: 0.9595
[ 5.0166462e-34  0.0000000e+00 -1.0291356e-09 ... -2.2654267e-02
  9.6237659e-03 -3.1983352e-03]
Sparsity at: 0.6661945905334336
Epoch 347/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9798 - val_loss: 0.2355 - val_accuracy: 0.9526
[ 5.0166462e-34  0.0000000e+00  9.3713183e-11 ... -2.3953097e-02
  3.4885462e-03  1.0725718e-03]
Sparsity at: 0.6661945905334336
Epoch 348/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9794 - val_loss: 0.2520 - val_accuracy: 0.9485
[ 5.0166462e-34  0.0000000e+00  8.6070635e-09 ... -5.0405944e-03
 -8.2615390e-03 -2.4087301e-02]
Sparsity at: 0.6661945905334336
Epoch 349/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9791 - val_loss: 0.2037 - val_accuracy: 0.9601
[ 5.0166462e-34  0.0000000e+00  1.4061285e-13 ...  2.3777741e-03
 -5.0382805e-03 -2.5125226e-02]
Sparsity at: 0.6661945905334336
Epoch 350/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1287 - accuracy: 0.9803 - val_loss: 0.2136 - val_accuracy: 0.9564
[ 5.0166462e-34  0.0000000e+00  4.4193747e-08 ...  2.8114193e-03
 -6.3742017e-03 -3.5064004e-02]
Sparsity at: 0.6661945905334336
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.017434749236987512
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [1. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 1. 1. 0.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.029788859901648035
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.84183335
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.08050235161440078
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.179
tf.Tensor(
[[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 335s 16ms/step - loss: 0.1345 - accuracy: 0.9793 - val_loss: 0.2046 - val_accuracy: 0.9600
[ 5.0166462e-34  0.0000000e+00  5.8957743e-13 ...  1.2488515e-02
 -1.5884835e-02 -5.1940709e-02]
Sparsity at: 0.6661945905334336
Epoch 352/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1326 - accuracy: 0.9792 - val_loss: 0.2187 - val_accuracy: 0.9576
[ 5.0166462e-34  0.0000000e+00 -4.2192119e-07 ...  1.1118654e-02
 -1.2221551e-02 -4.7764443e-02]
Sparsity at: 0.6661945905334336
Epoch 353/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1295 - accuracy: 0.9804 - val_loss: 0.2183 - val_accuracy: 0.9581
[ 5.0166462e-34  0.0000000e+00 -4.4247110e-12 ...  1.6902167e-02
  4.4779978e-03 -4.6188347e-02]
Sparsity at: 0.6661945905334336
Epoch 354/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1315 - accuracy: 0.9798 - val_loss: 0.2129 - val_accuracy: 0.9581
[ 5.0166462e-34  0.0000000e+00 -5.6141289e-07 ...  2.8519338e-02
 -9.2720296e-03 -4.7764119e-02]
Sparsity at: 0.6661945905334336
Epoch 355/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1303 - accuracy: 0.9804 - val_loss: 0.2162 - val_accuracy: 0.9555uracy: 
[ 5.0166462e-34  0.0000000e+00 -1.7933197e-11 ...  2.0556360e-02
  9.2330696e-03 -4.1158084e-02]
Sparsity at: 0.6661945905334336
Epoch 356/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1300 - accuracy: 0.9802 - val_loss: 0.1953 - val_accuracy: 0.9625
[ 5.0166462e-34  0.0000000e+00 -1.7770592e-05 ...  3.0165171e-02
 -6.2958631e-03 -5.7087801e-02]
Sparsity at: 0.6661945905334336
Epoch 357/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1292 - accuracy: 0.9797 - val_loss: 0.2357 - val_accuracy: 0.9491
[ 5.0166462e-34  0.0000000e+00  2.1001957e-10 ...  2.8506894e-02
 -1.7188465e-03 -3.7222117e-02]
Sparsity at: 0.6661945905334336
Epoch 358/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1274 - accuracy: 0.9803 - val_loss: 0.2251 - val_accuracy: 0.9526
[ 5.0166462e-34  0.0000000e+00 -1.4999537e-05 ...  3.8802572e-02
  1.3308596e-02 -3.1848133e-02]
Sparsity at: 0.6661945905334336
Epoch 359/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1336 - accuracy: 0.9795 - val_loss: 0.2048 - val_accuracy: 0.9604
[ 5.0166462e-34  0.0000000e+00 -1.2536223e-09 ...  2.7177701e-02
  6.6147419e-03 -2.9729892e-02]
Sparsity at: 0.6661945905334336
Epoch 360/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1308 - accuracy: 0.9794 - val_loss: 0.2029 - val_accuracy: 0.9600
[ 5.0166462e-34  0.0000000e+00 -2.0987207e-11 ...  3.2178987e-02
  1.5719826e-03 -4.3029077e-02]
Sparsity at: 0.6661945905334336
Epoch 361/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1307 - accuracy: 0.9798 - val_loss: 0.1983 - val_accuracy: 0.9634
[ 5.0166462e-34  0.0000000e+00 -1.4190608e-08 ...  3.6039576e-02
 -6.4102947e-03 -4.1300118e-02]
Sparsity at: 0.6661945905334336
Epoch 362/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9789 - val_loss: 0.2088 - val_accuracy: 0.9583
[ 5.0166462e-34  0.0000000e+00  3.1177207e-13 ...  2.9584855e-02
 -3.0890966e-04 -3.7495367e-02]
Sparsity at: 0.6661945905334336
Epoch 363/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1301 - accuracy: 0.9797 - val_loss: 0.2169 - val_accuracy: 0.9557
[ 5.0166462e-34  0.0000000e+00 -2.9521702e-08 ...  3.5900716e-02
 -8.2435217e-03 -3.2542981e-02]
Sparsity at: 0.6661945905334336
Epoch 364/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1293 - accuracy: 0.9800 - val_loss: 0.2134 - val_accuracy: 0.9582
[ 5.0166462e-34  0.0000000e+00  8.2353986e-13 ...  3.2868866e-02
 -1.6639662e-03 -3.7886836e-02]
Sparsity at: 0.6661945905334336
Epoch 365/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1336 - accuracy: 0.9789 - val_loss: 0.2110 - val_accuracy: 0.9573
[ 5.0166462e-34  0.0000000e+00  3.2103361e-07 ...  2.3626272e-02
 -2.0054677e-03 -3.0642426e-02]
Sparsity at: 0.6661945905334336
Epoch 366/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1311 - accuracy: 0.9799 - val_loss: 0.2080 - val_accuracy: 0.9588
[ 5.0166462e-34  0.0000000e+00  2.5757100e-12 ...  3.0733455e-02
 -2.4378446e-03 -3.7532512e-02]
Sparsity at: 0.6661945905334336
Epoch 367/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1331 - accuracy: 0.9791 - val_loss: 0.1872 - val_accuracy: 0.9646
[ 5.0166462e-34  0.0000000e+00  1.5192638e-06 ...  1.9180356e-02
 -9.5219770e-04 -3.6745586e-02]
Sparsity at: 0.6661945905334336
Epoch 368/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1321 - accuracy: 0.9795 - val_loss: 0.2196 - val_accuracy: 0.9538
[ 5.0166462e-34  0.0000000e+00 -7.1119768e-12 ...  2.4660589e-02
 -9.5603373e-03 -2.3976712e-02]
Sparsity at: 0.6661945905334336
Epoch 369/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1328 - accuracy: 0.9792 - val_loss: 0.2332 - val_accuracy: 0.9519
[ 5.0166462e-34  0.0000000e+00  9.3108074e-06 ...  2.5102228e-02
 -2.3864459e-03 -3.9766040e-02]
Sparsity at: 0.6661945905334336
Epoch 370/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1325 - accuracy: 0.9797 - val_loss: 0.1936 - val_accuracy: 0.9640
[ 5.0166462e-34  0.0000000e+00 -1.5575041e-13 ...  2.1633491e-02
 -5.6903646e-03 -3.9527357e-02]
Sparsity at: 0.6661945905334336
Epoch 371/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1308 - accuracy: 0.9793 - val_loss: 0.2386 - val_accuracy: 0.9504
[ 5.0166462e-34  0.0000000e+00  6.4366206e-05 ...  3.4552068e-02
 -4.5697121e-03 -4.3708708e-02]
Sparsity at: 0.6661945905334336
Epoch 372/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1332 - accuracy: 0.9796 - val_loss: 0.1865 - val_accuracy: 0.9659
[ 5.0166462e-34  0.0000000e+00  3.9390574e-12 ...  3.2431535e-02
 -9.9957585e-03 -4.5200132e-02]
Sparsity at: 0.6661945905334336
Epoch 373/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1335 - accuracy: 0.9789 - val_loss: 0.2095 - val_accuracy: 0.9589
[ 5.0166462e-34  0.0000000e+00  6.9456894e-07 ...  2.8762830e-02
 -6.9462038e-03 -4.9897589e-02]
Sparsity at: 0.6661945905334336
Epoch 374/500
235/235 [==============================] - 4s 19ms/step - loss: 0.1292 - accuracy: 0.9803 - val_loss: 0.2353 - val_accuracy: 0.9518
[ 5.0166462e-34  0.0000000e+00  1.6167983e-09 ...  2.7011273e-02
 -1.9643442e-03 -4.2815819e-02]
Sparsity at: 0.6661945905334336
Epoch 375/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1295 - accuracy: 0.9801 - val_loss: 0.1946 - val_accuracy: 0.9630
[ 5.016646e-34  0.000000e+00 -4.129045e-12 ...  3.013569e-02 -6.010564e-03
 -3.324266e-02]
Sparsity at: 0.6661945905334336
Epoch 376/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1323 - accuracy: 0.9793 - val_loss: 0.1907 - val_accuracy: 0.9637
[ 5.0166462e-34  0.0000000e+00  2.7074265e-08 ...  3.5031065e-02
 -7.4370415e-03 -5.2227963e-02]
Sparsity at: 0.6661945905334336
Epoch 377/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1319 - accuracy: 0.9800 - val_loss: 0.1949 - val_accuracy: 0.9632
[ 5.0166462e-34  0.0000000e+00 -2.6356814e-13 ...  2.9390654e-02
 -8.1426336e-04 -4.7859687e-02]
Sparsity at: 0.6661945905334336
Epoch 378/500
235/235 [==============================] - 5s 20ms/step - loss: 0.1319 - accuracy: 0.9797 - val_loss: 0.2322 - val_accuracy: 0.9522
[ 5.0166462e-34  0.0000000e+00 -2.4443636e-07 ...  3.1448398e-02
  8.7784640e-03 -4.9626831e-02]
Sparsity at: 0.6661945905334336
Epoch 379/500
235/235 [==============================] - 5s 19ms/step - loss: 0.1307 - accuracy: 0.9801 - val_loss: 0.2583 - val_accuracy: 0.9426
[ 5.0166462e-34  0.0000000e+00  9.6504212e-14 ...  2.9293986e-02
  5.7697417e-03 -3.0985681e-02]
Sparsity at: 0.6661945905334336
Epoch 380/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1303 - accuracy: 0.9797 - val_loss: 0.2206 - val_accuracy: 0.9553
[ 5.0166462e-34  0.0000000e+00 -1.8575988e-06 ...  2.7057441e-02
  3.7859948e-03 -3.7359085e-02]
Sparsity at: 0.6661945905334336
Epoch 381/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1362 - accuracy: 0.9783 - val_loss: 0.2112 - val_accuracy: 0.9578
[ 5.0166462e-34  0.0000000e+00 -2.0278206e-12 ...  3.1557865e-02
  4.8949770e-03 -3.2245442e-02]
Sparsity at: 0.6661945905334336
Epoch 382/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1288 - accuracy: 0.9803 - val_loss: 0.2087 - val_accuracy: 0.9596
[ 5.0166462e-34  0.0000000e+00 -1.8182627e-05 ...  3.7171446e-02
  9.7972937e-03 -3.4056179e-02]
Sparsity at: 0.6661945905334336
Epoch 383/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9793 - val_loss: 0.2213 - val_accuracy: 0.9546
[ 5.0166462e-34  0.0000000e+00  1.2706924e-10 ...  3.4090966e-02
  6.0086604e-03 -3.5175726e-02]
Sparsity at: 0.6661945905334336
Epoch 384/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1318 - accuracy: 0.9793 - val_loss: 0.2203 - val_accuracy: 0.9583
[ 5.0166462e-34  0.0000000e+00  1.9386724e-05 ...  2.8913746e-02
  1.3827384e-02 -3.2566305e-02]
Sparsity at: 0.6661945905334336
Epoch 385/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9801 - val_loss: 0.2151 - val_accuracy: 0.9568
[ 5.0166462e-34  0.0000000e+00 -9.3321373e-10 ...  3.6412727e-02
  9.3722353e-03 -4.1933380e-02]
Sparsity at: 0.6661945905334336
Epoch 386/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1305 - accuracy: 0.9802 - val_loss: 0.1981 - val_accuracy: 0.9607
[ 5.0166462e-34  0.0000000e+00 -6.7631056e-10 ...  3.7425280e-02
  1.4755691e-03 -4.2886496e-02]
Sparsity at: 0.6661945905334336
Epoch 387/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9796 - val_loss: 0.2020 - val_accuracy: 0.9589
[ 5.0166462e-34  0.0000000e+00 -1.3555002e-09 ...  4.1914202e-02
  3.4431075e-03 -4.8598185e-02]
Sparsity at: 0.6661945905334336
Epoch 388/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9800 - val_loss: 0.2125 - val_accuracy: 0.9591
[ 5.0166462e-34  0.0000000e+00  3.1771972e-12 ...  3.5622969e-02
 -1.2308236e-02 -4.2843826e-02]
Sparsity at: 0.6661945905334336
Epoch 389/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9791 - val_loss: 0.2233 - val_accuracy: 0.9554
[ 5.0166462e-34  0.0000000e+00 -3.4145387e-08 ...  4.2909775e-02
 -7.8834957e-03 -4.6983235e-02]
Sparsity at: 0.6661945905334336
Epoch 390/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9789 - val_loss: 0.2492 - val_accuracy: 0.9502
[ 5.0166462e-34  0.0000000e+00  2.7271244e-13 ...  3.8265847e-02
 -7.6803095e-03 -3.9133497e-02]
Sparsity at: 0.6661945905334336
Epoch 391/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1290 - accuracy: 0.9812 - val_loss: 0.2085 - val_accuracy: 0.9590
[ 5.0166462e-34  0.0000000e+00 -4.5084761e-08 ...  3.2480195e-02
 -1.5979197e-03 -4.9847659e-02]
Sparsity at: 0.6661945905334336
Epoch 392/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9796 - val_loss: 0.2060 - val_accuracy: 0.9608
[ 5.0166462e-34  0.0000000e+00 -2.1740975e-13 ...  3.0273179e-02
 -1.3435634e-02 -3.9513748e-02]
Sparsity at: 0.6661945905334336
Epoch 393/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1294 - accuracy: 0.9802 - val_loss: 0.2093 - val_accuracy: 0.9579
[ 5.0166462e-34  0.0000000e+00 -1.1434133e-07 ...  3.0503336e-02
 -1.5596343e-03 -3.2199610e-02]
Sparsity at: 0.6661945905334336
Epoch 394/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1279 - accuracy: 0.9800 - val_loss: 0.2032 - val_accuracy: 0.9602
[ 5.0166462e-34  0.0000000e+00 -3.1662888e-13 ...  2.8297052e-02
  4.1530184e-03 -3.3677880e-02]
Sparsity at: 0.6661945905334336
Epoch 395/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1313 - accuracy: 0.9793 - val_loss: 0.1967 - val_accuracy: 0.9624
[ 5.0166462e-34  0.0000000e+00  6.6424332e-07 ...  2.9361097e-02
 -1.8165674e-03 -3.7136026e-02]
Sparsity at: 0.6661945905334336
Epoch 396/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9791 - val_loss: 0.2190 - val_accuracy: 0.9551
[ 5.0166462e-34  0.0000000e+00  6.1607100e-13 ...  3.7772931e-02
  1.3082376e-03 -3.5317603e-02]
Sparsity at: 0.6661945905334336
Epoch 397/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9794 - val_loss: 0.2298 - val_accuracy: 0.9537
[ 5.0166462e-34  0.0000000e+00  7.3241245e-07 ...  3.0229187e-02
  5.1592146e-03 -3.7146766e-02]
Sparsity at: 0.6661945905334336
Epoch 398/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1329 - accuracy: 0.9793 - val_loss: 0.2396 - val_accuracy: 0.9509
[ 5.0166462e-34  0.0000000e+00  4.2851715e-11 ...  2.9686911e-02
  1.2720291e-02 -4.0892307e-02]
Sparsity at: 0.6661945905334336
Epoch 399/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9796 - val_loss: 0.1918 - val_accuracy: 0.9629
[ 5.0166462e-34  0.0000000e+00 -3.1581243e-05 ...  2.5934486e-02
  8.5540358e-03 -3.4263596e-02]
Sparsity at: 0.6661945905334336
Epoch 400/500
235/235 [==============================] - 5s 19ms/step - loss: 0.1301 - accuracy: 0.9806 - val_loss: 0.2160 - val_accuracy: 0.9603
[ 5.0166462e-34  0.0000000e+00 -2.2723745e-10 ...  3.8275316e-02
  7.3109306e-03 -3.3438545e-02]
Sparsity at: 0.6661945905334336
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.021286794171819112
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [1. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 1. 1. 0.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.035991625182131504
Thresholhold -0.004473910667002201
Using suggest threshold.
Applying new mask
Percentage zeros 0.84183335
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.0847288159094921
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.179
tf.Tensor(
[[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 314s 16ms/step - loss: 0.1366 - accuracy: 0.9788 - val_loss: 0.2161 - val_accuracy: 0.9585
[ 5.0166462e-34  0.0000000e+00 -1.7030098e-04 ...  3.3695348e-02
  7.3106256e-03 -2.9767919e-02]
Sparsity at: 0.6661945905334336
Epoch 402/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9797 - val_loss: 0.2234 - val_accuracy: 0.9557
[ 5.0166462e-34  0.0000000e+00  5.3073029e-10 ...  2.5435541e-02
  8.9312075e-03 -3.8330525e-02]
Sparsity at: 0.6661945905334336
Epoch 403/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1303 - accuracy: 0.9797 - val_loss: 0.1993 - val_accuracy: 0.9600
[ 5.0166462e-34  0.0000000e+00  1.2903041e-10 ...  2.4581093e-02
  1.2220347e-02 -3.7538722e-02]
Sparsity at: 0.6661945905334336
Epoch 404/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9799 - val_loss: 0.2319 - val_accuracy: 0.9542
[ 5.0166462e-34  0.0000000e+00  1.8479582e-09 ...  2.1131694e-02
  1.1529819e-02 -3.1216592e-02]
Sparsity at: 0.6661945905334336
Epoch 405/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1305 - accuracy: 0.9806 - val_loss: 0.2282 - val_accuracy: 0.9525
[ 5.0166462e-34  0.0000000e+00 -5.9908601e-14 ...  2.7480183e-02
  2.0195769e-02 -2.8177053e-02]
Sparsity at: 0.6661945905334336
Epoch 406/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1324 - accuracy: 0.9793 - val_loss: 0.2170 - val_accuracy: 0.9577
[ 5.0166462e-34  0.0000000e+00 -2.3530706e-07 ...  2.2083312e-02
  1.2195705e-02 -3.0541109e-02]
Sparsity at: 0.6661945905334336
Epoch 407/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9796 - val_loss: 0.2121 - val_accuracy: 0.9588
[ 5.0166462e-34  0.0000000e+00 -1.3948997e-12 ...  3.2646816e-02
  2.6080599e-03 -3.1332266e-02]
Sparsity at: 0.6661945905334336
Epoch 408/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9806 - val_loss: 0.2102 - val_accuracy: 0.9615
[ 5.0166462e-34  0.0000000e+00 -3.7684127e-05 ...  2.4762336e-02
  5.1115514e-03 -2.7669128e-02]
Sparsity at: 0.6661945905334336
Epoch 409/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1315 - accuracy: 0.9792 - val_loss: 0.2155 - val_accuracy: 0.9559
[ 5.0166462e-34  0.0000000e+00  3.3662428e-10 ...  2.3849143e-02
  8.2674921e-03 -2.3807436e-02]
Sparsity at: 0.6661945905334336
Epoch 410/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9800 - val_loss: 0.2350 - val_accuracy: 0.9496
[ 5.0166462e-34  0.0000000e+00  8.3640816e-11 ...  2.4263062e-02
 -2.2368226e-03 -2.6655937e-02]
Sparsity at: 0.6661945905334336
Epoch 411/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1306 - accuracy: 0.9796 - val_loss: 0.2035 - val_accuracy: 0.9596
[ 5.0166462e-34  0.0000000e+00  5.7226401e-09 ...  2.9896941e-02
 -1.1668382e-02 -3.1332888e-02]
Sparsity at: 0.6661945905334336
Epoch 412/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1315 - accuracy: 0.9796 - val_loss: 0.2301 - val_accuracy: 0.9528
[ 5.0166462e-34  0.0000000e+00  1.1491273e-13 ...  3.1510808e-02
 -1.2416903e-02 -3.8731392e-02]
Sparsity at: 0.6661945905334336
Epoch 413/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1324 - accuracy: 0.9798 - val_loss: 0.2176 - val_accuracy: 0.9556
[ 5.0166462e-34  0.0000000e+00  1.5143132e-07 ...  3.6174856e-02
 -4.7017857e-03 -2.5497245e-02]
Sparsity at: 0.6661945905334336
Epoch 414/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1291 - accuracy: 0.9805 - val_loss: 0.2172 - val_accuracy: 0.9545
[ 5.0166462e-34  0.0000000e+00 -7.7384843e-13 ...  4.0978163e-02
  4.9593374e-03 -3.3247415e-02]
Sparsity at: 0.6661945905334336
Epoch 415/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1286 - accuracy: 0.9803 - val_loss: 0.2290 - val_accuracy: 0.9512
[ 5.0166462e-34  0.0000000e+00  8.9124205e-07 ...  3.4150086e-02
  3.6105814e-03 -3.4573276e-02]
Sparsity at: 0.6661945905334336
Epoch 416/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9790 - val_loss: 0.1943 - val_accuracy: 0.9632
[ 5.0166462e-34  0.0000000e+00 -3.3500824e-12 ...  2.8278327e-02
  9.3308287e-03 -3.7592702e-02]
Sparsity at: 0.6661945905334336
Epoch 417/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9797 - val_loss: 0.2230 - val_accuracy: 0.9550
[ 5.0166462e-34  0.0000000e+00  1.4363301e-05 ...  2.3932341e-02
  4.2434921e-03 -3.6306161e-02]
Sparsity at: 0.6661945905334336
Epoch 418/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1284 - accuracy: 0.9805 - val_loss: 0.2209 - val_accuracy: 0.9545
[ 5.0166462e-34  0.0000000e+00  1.2187328e-11 ...  2.3486931e-02
  6.7101177e-03 -3.8759548e-02]
Sparsity at: 0.6661945905334336
Epoch 419/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9786 - val_loss: 0.2074 - val_accuracy: 0.9620
[ 5.0166462e-34  0.0000000e+00  5.2370451e-05 ...  1.7969867e-02
  1.7991617e-02 -3.6398351e-02]
Sparsity at: 0.6661945905334336
Epoch 420/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9801 - val_loss: 0.2119 - val_accuracy: 0.9597
[ 5.0166462e-34  0.0000000e+00  1.0719575e-10 ...  1.5772834e-02
  9.8205805e-03 -2.9567460e-02]
Sparsity at: 0.6661945905334336
Epoch 421/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9804 - val_loss: 0.2106 - val_accuracy: 0.9581
[ 5.0166462e-34  0.0000000e+00 -8.0907668e-08 ...  2.3440348e-02
  1.5317601e-02 -3.3446498e-02]
Sparsity at: 0.6661945905334336
Epoch 422/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1272 - accuracy: 0.9808 - val_loss: 0.1988 - val_accuracy: 0.9620
[ 5.0166462e-34  0.0000000e+00  2.0843607e-09 ...  2.6289063e-02
  2.6920129e-02 -3.7764721e-02]
Sparsity at: 0.6661945905334336
Epoch 423/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9793 - val_loss: 0.1882 - val_accuracy: 0.9662
[ 5.0166462e-34  0.0000000e+00 -1.6385374e-12 ...  3.0213658e-02
  2.2953881e-02 -3.5196871e-02]
Sparsity at: 0.6661945905334336
Epoch 424/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1305 - accuracy: 0.9801 - val_loss: 0.2050 - val_accuracy: 0.9586
[ 5.0166462e-34  0.0000000e+00 -1.1898731e-08 ...  2.8032901e-02
  1.9446323e-02 -3.4592714e-02]
Sparsity at: 0.6661945905334336
Epoch 425/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9794 - val_loss: 0.1998 - val_accuracy: 0.9623
[ 5.01664622e-34  0.00000000e+00  1.00221684e-13 ...  2.14962792e-02
  1.65227633e-02 -3.66929844e-02]
Sparsity at: 0.6661945905334336
Epoch 426/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1305 - accuracy: 0.9805 - val_loss: 0.1872 - val_accuracy: 0.9642
[ 5.0166462e-34  0.0000000e+00  3.6113369e-07 ...  3.3168435e-02
  4.1375863e-03 -3.8761869e-02]
Sparsity at: 0.6661945905334336
Epoch 427/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9793 - val_loss: 0.2099 - val_accuracy: 0.9594
[ 5.0166462e-34  0.0000000e+00 -1.6369941e-12 ...  2.4910981e-02
  1.5841726e-02 -3.9719313e-02]
Sparsity at: 0.6661945905334336
Epoch 428/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1332 - accuracy: 0.9792 - val_loss: 0.1906 - val_accuracy: 0.9638
[ 5.0166462e-34  0.0000000e+00  5.0748622e-07 ...  1.7773718e-02
  1.4031833e-02 -4.1157886e-02]
Sparsity at: 0.6661945905334336
Epoch 429/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1299 - accuracy: 0.9798 - val_loss: 0.2107 - val_accuracy: 0.9573
[ 5.0166462e-34  0.0000000e+00 -1.7289691e-11 ...  2.3442393e-02
  2.3262611e-02 -4.2894352e-02]
Sparsity at: 0.6661945905334336
Epoch 430/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1295 - accuracy: 0.9797 - val_loss: 0.2383 - val_accuracy: 0.9493
[ 5.0166462e-34  0.0000000e+00 -1.3089324e-05 ...  2.6219543e-02
  1.0280359e-02 -3.9027151e-02]
Sparsity at: 0.6661945905334336
Epoch 431/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9792 - val_loss: 0.1891 - val_accuracy: 0.9651
[ 5.0166462e-34  0.0000000e+00  5.7543192e-11 ...  3.0424522e-02
  6.9643944e-03 -4.2140618e-02]
Sparsity at: 0.6661945905334336
Epoch 432/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1293 - accuracy: 0.9804 - val_loss: 0.1991 - val_accuracy: 0.9616
[ 5.0166462e-34  0.0000000e+00 -1.3232585e-04 ...  3.5531569e-02
  3.3531720e-03 -3.3410549e-02]
Sparsity at: 0.6661945905334336
Epoch 433/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.2159 - val_accuracy: 0.9560
[ 5.0166462e-34  0.0000000e+00 -5.3958515e-10 ...  3.5795450e-02
  7.2374404e-03 -4.1890942e-02]
Sparsity at: 0.6661945905334336
Epoch 434/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1275 - accuracy: 0.9808 - val_loss: 0.2245 - val_accuracy: 0.9551
[ 5.0166462e-34  0.0000000e+00 -2.9143706e-07 ...  3.2696735e-02
  1.1144673e-02 -4.0690720e-02]
Sparsity at: 0.6661945905334336
Epoch 435/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9792 - val_loss: 0.2023 - val_accuracy: 0.9604
[ 5.0166462e-34  0.0000000e+00 -3.3629433e-11 ...  3.2253627e-02
  4.1438697e-04 -3.7762336e-02]
Sparsity at: 0.6661945905334336
Epoch 436/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9791 - val_loss: 0.1851 - val_accuracy: 0.9642
[ 5.0166462e-34  0.0000000e+00  6.3243008e-11 ...  3.3667326e-02
  1.7573556e-03 -3.5908993e-02]
Sparsity at: 0.6661945905334336
Epoch 437/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1375 - accuracy: 0.9782 - val_loss: 0.2054 - val_accuracy: 0.9602
[ 5.0166462e-34  0.0000000e+00 -7.9449318e-09 ...  3.5927340e-02
 -6.3959445e-04 -3.5168685e-02]
Sparsity at: 0.6661945905334336
Epoch 438/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1274 - accuracy: 0.9804 - val_loss: 0.1935 - val_accuracy: 0.9627
[ 5.0166462e-34  0.0000000e+00 -1.5558700e-13 ...  3.3379830e-02
  1.4684042e-02 -3.6536660e-02]
Sparsity at: 0.6661945905334336
Epoch 439/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9793 - val_loss: 0.2006 - val_accuracy: 0.9614
[ 5.0166462e-34  0.0000000e+00 -1.4287522e-09 ...  3.5465494e-02
  2.1416308e-04 -3.0830063e-02]
Sparsity at: 0.6661945905334336
Epoch 440/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1302 - accuracy: 0.9794 - val_loss: 0.1858 - val_accuracy: 0.9661
[ 5.0166462e-34  0.0000000e+00  1.2060875e-13 ...  3.5312630e-02
  1.5592229e-02 -3.0844605e-02]
Sparsity at: 0.6661945905334336
Epoch 441/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1278 - accuracy: 0.9811 - val_loss: 0.1921 - val_accuracy: 0.9629
[ 5.0166462e-34  0.0000000e+00 -2.6459077e-07 ...  3.5115194e-02
  1.0440097e-02 -3.1698763e-02]
Sparsity at: 0.6661945905334336
Epoch 442/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1312 - accuracy: 0.9800 - val_loss: 0.2173 - val_accuracy: 0.9584
[ 5.0166462e-34  0.0000000e+00  1.8957847e-12 ...  3.2708019e-02
  1.1413266e-02 -3.1284310e-02]
Sparsity at: 0.6661945905334336
Epoch 443/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9797 - val_loss: 0.2401 - val_accuracy: 0.9490
[ 5.0166462e-34  0.0000000e+00 -7.1751507e-05 ...  2.4949687e-02
  1.2319994e-02 -2.3613293e-02]
Sparsity at: 0.6661945905334336
Epoch 444/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9788 - val_loss: 0.2352 - val_accuracy: 0.9513
[ 5.0166462e-34  0.0000000e+00  4.8981014e-11 ...  3.4620065e-02
  8.7027783e-03 -2.7650103e-02]
Sparsity at: 0.6661945905334336
Epoch 445/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.1894 - val_accuracy: 0.9648
[ 5.0166462e-34  0.0000000e+00 -1.8864559e-14 ...  3.2831721e-02
  1.4507332e-02 -2.8606158e-02]
Sparsity at: 0.6661945905334336
Epoch 446/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9801 - val_loss: 0.2441 - val_accuracy: 0.9504
[ 5.0166462e-34  0.0000000e+00 -5.5716079e-08 ...  3.4155052e-02
  1.8863833e-02 -3.0358726e-02]
Sparsity at: 0.6661945905334336
Epoch 447/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1303 - accuracy: 0.9794 - val_loss: 0.2155 - val_accuracy: 0.9572
[ 5.0166462e-34  0.0000000e+00  6.3468653e-13 ...  2.7182460e-02
  2.1889007e-02 -3.4409065e-02]
Sparsity at: 0.6661945905334336
Epoch 448/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1302 - accuracy: 0.9803 - val_loss: 0.2138 - val_accuracy: 0.9568
[ 5.0166462e-34  0.0000000e+00  8.7546359e-06 ...  2.8081672e-02
  1.7427117e-02 -3.2683324e-02]
Sparsity at: 0.6661945905334336
Epoch 449/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1307 - accuracy: 0.9793 - val_loss: 0.1926 - val_accuracy: 0.9628
[ 5.0166462e-34  0.0000000e+00 -4.2037596e-11 ...  2.3931349e-02
  2.1367043e-02 -3.7568647e-02]
Sparsity at: 0.6661945905334336
Epoch 450/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1291 - accuracy: 0.9801 - val_loss: 0.2041 - val_accuracy: 0.9610
[ 5.0166462e-34  0.0000000e+00  1.2971173e-06 ...  2.4462642e-02
  1.9424165e-02 -2.3107791e-02]
Sparsity at: 0.6661945905334336
Epoch 451/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9794 - val_loss: 0.1908 - val_accuracy: 0.9645
[ 5.0166462e-34  0.0000000e+00 -2.1308819e-09 ...  2.5378020e-02
  1.5213143e-02 -3.0225599e-02]
Sparsity at: 0.6661945905334336
Epoch 452/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9796 - val_loss: 0.2178 - val_accuracy: 0.9536
[ 5.01664622e-34  0.00000000e+00 -4.86081376e-14 ...  2.47786175e-02
  1.28195835e-02 -2.88934782e-02]
Sparsity at: 0.6661945905334336
Epoch 453/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9790 - val_loss: 0.2295 - val_accuracy: 0.9527
[ 5.0166462e-34  0.0000000e+00 -7.9636571e-08 ...  3.0350950e-02
  1.8294608e-02 -2.0713219e-02]
Sparsity at: 0.6661945905334336
Epoch 454/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1308 - accuracy: 0.9796 - val_loss: 0.2023 - val_accuracy: 0.9630
[ 5.0166462e-34  0.0000000e+00  7.3057998e-13 ...  3.3765789e-02
  5.9622084e-03 -2.7223842e-02]
Sparsity at: 0.6661945905334336
Epoch 455/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9803 - val_loss: 0.1946 - val_accuracy: 0.9623
[ 5.0166462e-34  0.0000000e+00 -2.6681580e-06 ...  2.9829403e-02
  2.0242143e-02 -2.7752785e-02]
Sparsity at: 0.6661945905334336
Epoch 456/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9803 - val_loss: 0.1893 - val_accuracy: 0.9621
[ 5.0166462e-34  0.0000000e+00  4.9105065e-12 ...  3.1063760e-02
  1.9509818e-02 -3.3856992e-02]
Sparsity at: 0.6661945905334336
Epoch 457/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1285 - accuracy: 0.9799 - val_loss: 0.1859 - val_accuracy: 0.9662
[ 5.0166462e-34  0.0000000e+00 -2.3863926e-05 ...  3.1564310e-02
  1.5154132e-02 -3.7474949e-02]
Sparsity at: 0.6661945905334336
Epoch 458/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1324 - accuracy: 0.9793 - val_loss: 0.2127 - val_accuracy: 0.9561
[ 5.0166462e-34  0.0000000e+00 -9.2739538e-11 ...  2.7012378e-02
  2.2031531e-02 -3.2125648e-02]
Sparsity at: 0.6661945905334336
Epoch 459/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9791 - val_loss: 0.2447 - val_accuracy: 0.9485
[ 5.0166462e-34  0.0000000e+00  7.0777824e-05 ...  2.5135579e-02
  2.4749571e-02 -3.7654240e-02]
Sparsity at: 0.6661945905334336
Epoch 460/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1313 - accuracy: 0.9797 - val_loss: 0.2012 - val_accuracy: 0.9603
[ 5.0166462e-34  0.0000000e+00  1.1374727e-09 ...  2.4070501e-02
  2.1299662e-02 -3.5719249e-02]
Sparsity at: 0.6661945905334336
Epoch 461/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1286 - accuracy: 0.9805 - val_loss: 0.2112 - val_accuracy: 0.9590
[ 5.0166462e-34  0.0000000e+00 -1.0053436e-09 ...  2.2825822e-02
  1.8411534e-02 -3.7973076e-02]
Sparsity at: 0.6661945905334336
Epoch 462/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9789 - val_loss: 0.2039 - val_accuracy: 0.9593
[ 5.0166462e-34  0.0000000e+00  7.8198195e-09 ...  2.5847569e-02
  1.8466167e-02 -3.6668569e-02]
Sparsity at: 0.6661945905334336
Epoch 463/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9791 - val_loss: 0.2105 - val_accuracy: 0.9603
[ 5.0166462e-34  0.0000000e+00 -2.2292281e-12 ...  2.6206233e-02
  1.8838052e-02 -2.7534315e-02]
Sparsity at: 0.6661945905334336
Epoch 464/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1296 - accuracy: 0.9798 - val_loss: 0.1912 - val_accuracy: 0.9644
[ 5.0166462e-34  0.0000000e+00  1.8013564e-08 ...  2.1012601e-02
  2.8967986e-02 -2.8848035e-02]
Sparsity at: 0.6661945905334336
Epoch 465/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1308 - accuracy: 0.9797 - val_loss: 0.1980 - val_accuracy: 0.9617
[ 5.0166462e-34  0.0000000e+00 -5.3496454e-13 ...  3.3694621e-02
  1.5965614e-02 -3.0848363e-02]
Sparsity at: 0.6661945905334336
Epoch 466/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1302 - accuracy: 0.9802 - val_loss: 0.2768 - val_accuracy: 0.9386
[ 5.01664622e-34  0.00000000e+00 -1.13023560e-07 ...  2.48287749e-02
  1.33100245e-02 -3.13817635e-02]
Sparsity at: 0.6661945905334336
Epoch 467/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9786 - val_loss: 0.1950 - val_accuracy: 0.9614
[ 5.0166462e-34  0.0000000e+00 -4.3734634e-13 ...  2.5379071e-02
  1.8889066e-02 -2.5580255e-02]
Sparsity at: 0.6661945905334336
Epoch 468/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9792 - val_loss: 0.1930 - val_accuracy: 0.9631
[ 5.0166462e-34  0.0000000e+00  3.0134677e-07 ...  2.9281765e-02
  1.5817739e-02 -1.9219046e-02]
Sparsity at: 0.6661945905334336
Epoch 469/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1284 - accuracy: 0.9807 - val_loss: 0.2010 - val_accuracy: 0.9603
[ 5.0166462e-34  0.0000000e+00 -2.1538910e-12 ...  3.1510673e-02
  1.4559312e-02 -2.7425151e-02]
Sparsity at: 0.6661945905334336
Epoch 470/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1282 - accuracy: 0.9811 - val_loss: 0.2058 - val_accuracy: 0.9582
[ 5.0166462e-34  0.0000000e+00 -7.7651214e-07 ...  3.4084164e-02
  2.4510140e-02 -2.7484257e-02]
Sparsity at: 0.6661945905334336
Epoch 471/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9793 - val_loss: 0.2073 - val_accuracy: 0.9587
[ 5.0166462e-34  0.0000000e+00  4.6306578e-12 ...  3.2265905e-02
  1.0921558e-02 -3.0408163e-02]
Sparsity at: 0.6661945905334336
Epoch 472/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9793 - val_loss: 0.1934 - val_accuracy: 0.9664
[ 5.0166462e-34  0.0000000e+00 -2.7766364e-06 ...  3.5363849e-02
  1.4309766e-02 -3.1650126e-02]
Sparsity at: 0.6661945905334336
Epoch 473/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9800 - val_loss: 0.2345 - val_accuracy: 0.9527
[ 5.01664622e-34  0.00000000e+00  1.18122144e-11 ...  3.35260108e-02
  1.12362215e-02 -3.58981527e-02]
Sparsity at: 0.6661945905334336
Epoch 474/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1301 - accuracy: 0.9796 - val_loss: 0.2037 - val_accuracy: 0.9602
[ 5.0166462e-34  0.0000000e+00 -2.7544767e-05 ...  4.0611811e-02
  7.1950620e-03 -2.9724510e-02]
Sparsity at: 0.6661945905334336
Epoch 475/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9785 - val_loss: 0.2283 - val_accuracy: 0.9572
[ 5.0166462e-34  0.0000000e+00 -7.6409157e-11 ...  3.3445243e-02
  9.2513142e-03 -4.0036768e-02]
Sparsity at: 0.6661945905334336
Epoch 476/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1272 - accuracy: 0.9808 - val_loss: 0.2193 - val_accuracy: 0.9549
[ 5.0166462e-34  0.0000000e+00  2.8041622e-08 ...  4.3839898e-02
  2.0105566e-03 -3.9353095e-02]
Sparsity at: 0.6661945905334336
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9794 - val_loss: 0.2217 - val_accuracy: 0.9562
[ 5.0166462e-34  0.0000000e+00  4.1147978e-09 ...  4.0250488e-02
  7.9912217e-03 -4.0568639e-02]
Sparsity at: 0.6661945905334336
Epoch 478/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1286 - accuracy: 0.9807 - val_loss: 0.1977 - val_accuracy: 0.9616
[ 5.0166462e-34  0.0000000e+00  1.7125869e-12 ...  3.4014031e-02
  1.1212647e-02 -3.6969546e-02]
Sparsity at: 0.6661945905334336
Epoch 479/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1298 - accuracy: 0.9799 - val_loss: 0.1956 - val_accuracy: 0.9641
[ 5.0166462e-34  0.0000000e+00  1.2998083e-08 ...  2.7157564e-02
  8.5445177e-03 -3.2730255e-02]
Sparsity at: 0.6661945905334336
Epoch 480/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9796 - val_loss: 0.1936 - val_accuracy: 0.9661
[ 5.0166462e-34  0.0000000e+00 -2.9026044e-13 ...  3.6168572e-02
  1.3901040e-02 -4.1138522e-02]
Sparsity at: 0.6661945905334336
Epoch 481/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1293 - accuracy: 0.9801 - val_loss: 0.2027 - val_accuracy: 0.9608
[ 5.0166462e-34  0.0000000e+00 -1.0953354e-07 ...  2.5413845e-02
  1.0120479e-02 -4.3176826e-02]
Sparsity at: 0.6661945905334336
Epoch 482/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9805 - val_loss: 0.2330 - val_accuracy: 0.9528
[ 5.0166462e-34  0.0000000e+00 -6.3144336e-13 ...  3.4757722e-02
  1.5925772e-02 -3.8130615e-02]
Sparsity at: 0.6661945905334336
Epoch 483/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1313 - accuracy: 0.9798 - val_loss: 0.2210 - val_accuracy: 0.9569
[ 5.0166462e-34  0.0000000e+00 -4.8368372e-07 ...  3.7440643e-02
  1.1787804e-02 -3.8699858e-02]
Sparsity at: 0.6661945905334336
Epoch 484/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1295 - accuracy: 0.9803 - val_loss: 0.2069 - val_accuracy: 0.9578
[ 5.01664622e-34  0.00000000e+00 -4.73472607e-12 ...  3.59379053e-02
  1.11894915e-02 -3.30985300e-02]
Sparsity at: 0.6661945905334336
Epoch 485/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9797 - val_loss: 0.1977 - val_accuracy: 0.9589
[ 5.0166462e-34  0.0000000e+00  3.1105365e-06 ...  2.3438873e-02
  9.5222406e-03 -2.9412445e-02]
Sparsity at: 0.6661945905334336
Epoch 486/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1292 - accuracy: 0.9799 - val_loss: 0.1886 - val_accuracy: 0.9643
[ 5.0166462e-34  0.0000000e+00  2.8096695e-11 ...  2.7605167e-02
  2.8741575e-05 -2.3558874e-02]
Sparsity at: 0.6661945905334336
Epoch 487/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1324 - accuracy: 0.9789 - val_loss: 0.2108 - val_accuracy: 0.9598
[ 5.0166462e-34  0.0000000e+00 -1.5028096e-05 ...  2.4362903e-02
  8.0653653e-03 -3.0866563e-02]
Sparsity at: 0.6661945905334336
Epoch 488/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1309 - accuracy: 0.9800 - val_loss: 0.1972 - val_accuracy: 0.9625
[ 5.0166462e-34  0.0000000e+00 -8.5109100e-11 ...  3.1887610e-02
  4.0479116e-03 -2.8064299e-02]
Sparsity at: 0.6661945905334336
Epoch 489/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1276 - accuracy: 0.9810 - val_loss: 0.2254 - val_accuracy: 0.9536
[ 5.0166462e-34  0.0000000e+00  4.0138926e-05 ...  3.4619462e-02
  3.2950668e-03 -2.7101122e-02]
Sparsity at: 0.6661945905334336
Epoch 490/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9793 - val_loss: 0.1910 - val_accuracy: 0.9623
[ 5.0166462e-34  0.0000000e+00 -2.1473656e-10 ...  2.6551168e-02
  9.4030909e-03 -2.6809609e-02]
Sparsity at: 0.6661945905334336
Epoch 491/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.2136 - val_accuracy: 0.9559
[ 5.0166462e-34  0.0000000e+00 -7.2793264e-05 ...  1.5244971e-02
  1.8277496e-02 -3.0468663e-02]
Sparsity at: 0.6661945905334336
Epoch 492/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1276 - accuracy: 0.9814 - val_loss: 0.2019 - val_accuracy: 0.9600
[ 5.0166462e-34  0.0000000e+00 -3.8658907e-10 ...  2.2117740e-02
  1.8707333e-02 -3.7650708e-02]
Sparsity at: 0.6661945905334336
Epoch 493/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9798 - val_loss: 0.2094 - val_accuracy: 0.9587
[ 5.0166462e-34  0.0000000e+00  7.1694376e-07 ...  2.9080246e-02
  1.3878999e-02 -3.3167481e-02]
Sparsity at: 0.6661945905334336
Epoch 494/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9784 - val_loss: 0.2339 - val_accuracy: 0.9496
[ 5.0166462e-34  0.0000000e+00 -2.1057092e-10 ...  3.3845954e-02
  1.5826404e-02 -3.2074660e-02]
Sparsity at: 0.6661945905334336
Epoch 495/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1304 - accuracy: 0.9805 - val_loss: 0.2047 - val_accuracy: 0.9584
[ 5.0166462e-34  0.0000000e+00 -3.0252900e-06 ...  3.0809687e-02
  9.0651698e-03 -3.5765842e-02]
Sparsity at: 0.6661945905334336
Epoch 496/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9790 - val_loss: 0.2109 - val_accuracy: 0.9588
[ 5.0166462e-34  0.0000000e+00 -7.0761619e-10 ...  3.3090234e-02
  1.4748472e-02 -3.4498975e-02]
Sparsity at: 0.6661945905334336
Epoch 497/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1311 - accuracy: 0.9794 - val_loss: 0.2120 - val_accuracy: 0.9549
[ 5.0166462e-34  0.0000000e+00  1.6825118e-09 ...  2.2164147e-02
  1.5568255e-02 -3.2713860e-02]
Sparsity at: 0.6661945905334336
Epoch 498/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9797 - val_loss: 0.2291 - val_accuracy: 0.9539
[ 5.0166462e-34  0.0000000e+00 -2.9893639e-09 ...  2.4329815e-02
  1.3032331e-02 -3.8005240e-02]
Sparsity at: 0.6661945905334336
Epoch 499/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1273 - accuracy: 0.9804 - val_loss: 0.2185 - val_accuracy: 0.9570
[ 5.0166462e-34  0.0000000e+00  9.8324500e-12 ...  2.6388383e-02
  8.3673280e-03 -2.9870940e-02]
Sparsity at: 0.6661945905334336
Epoch 500/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9791 - val_loss: 0.1973 - val_accuracy: 0.9607
[ 5.0166462e-34  0.0000000e+00 -1.7728471e-08 ...  2.7445143e-02
  1.5431383e-02 -3.0578464e-02]
Sparsity at: 0.6661945905334336
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.037218498066067696
Thresholhold -0.04982715845108032
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.0609181709587574
Thresholhold 0.0024757087230682373
Using suggest threshold.
Applying new mask
Percentage zeros 0.020033333
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.11689147353172302
Thresholhold 0.15380525588989258
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
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  5/235 [..............................] - ETA: 2s - loss: 1.7690 - accuracy: 0.4336     WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0120s vs `on_train_batch_begin` time: 11.4425s). Check your callbacks.
235/235 [==============================] - 73s 16ms/step - loss: 0.2540 - accuracy: 0.9258 - val_loss: 0.2206 - val_accuracy: 0.9571
[-0.04982716  0.05973877  0.01948842 ...  0.          0.20478557
  0.2136623 ]
Sparsity at: 0.0041359879789631855
Epoch 2/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0886 - accuracy: 0.9749 - val_loss: 0.0972 - val_accuracy: 0.9694
[-0.04982716  0.05973877  0.01948842 ... -0.          0.21181315
  0.21732321]
Sparsity at: 0.0041359879789631855
Epoch 3/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0502 - accuracy: 0.9861 - val_loss: 0.0897 - val_accuracy: 0.9717
[-0.04982716  0.05973877  0.01948842 ...  0.          0.22302365
  0.22485492]
Sparsity at: 0.0041359879789631855
Epoch 4/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0303 - accuracy: 0.9923 - val_loss: 0.0910 - val_accuracy: 0.9734
[-0.04982716  0.05973877  0.01948842 ...  0.          0.2341781
  0.23953493]
Sparsity at: 0.0041359879789631855
Epoch 5/500
235/235 [==============================] - 4s 18ms/step - loss: 0.0199 - accuracy: 0.9950 - val_loss: 0.0895 - val_accuracy: 0.9720
[-0.04982716  0.05973877  0.01948842 ... -0.          0.24328542
  0.24719958]
Sparsity at: 0.0041359879789631855
Epoch 6/500
235/235 [==============================] - 4s 18ms/step - loss: 0.0129 - accuracy: 0.9972 - val_loss: 0.0928 - val_accuracy: 0.9737
[-0.04982716  0.05973877  0.01948842 ...  0.          0.25285584
  0.25656718]
Sparsity at: 0.0041359879789631855
Epoch 7/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0111 - accuracy: 0.9976 - val_loss: 0.1011 - val_accuracy: 0.9710
[-0.04982716  0.05973877  0.01948842 ...  0.          0.2586816
  0.263335  ]
Sparsity at: 0.0041359879789631855
Epoch 8/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0133 - accuracy: 0.9963 - val_loss: 0.0988 - val_accuracy: 0.9717
[-0.04982716  0.05973877  0.01948842 ...  0.          0.26232496
  0.25895464]
Sparsity at: 0.0041359879789631855
Epoch 9/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0106 - accuracy: 0.9969 - val_loss: 0.1065 - val_accuracy: 0.9710
[-0.04982716  0.05973877  0.01948842 ...  0.          0.27342716
  0.26351595]
Sparsity at: 0.0041359879789631855
Epoch 10/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0127 - accuracy: 0.9963 - val_loss: 0.0846 - val_accuracy: 0.9759
[-0.04982716  0.05973877  0.01948842 ...  0.          0.27977377
  0.26159462]
Sparsity at: 0.0041359879789631855
Epoch 11/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0101 - accuracy: 0.9973 - val_loss: 0.0875 - val_accuracy: 0.9783
[-0.04982716  0.05973877  0.01948842 ...  0.          0.28169194
  0.27414232]
Sparsity at: 0.0041359879789631855
Epoch 12/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0073 - accuracy: 0.9979 - val_loss: 0.0934 - val_accuracy: 0.9768
[-0.04982716  0.05973877  0.01948842 ...  0.          0.28030452
  0.27433112]
Sparsity at: 0.0041359879789631855
Epoch 13/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0074 - accuracy: 0.9978 - val_loss: 0.0872 - val_accuracy: 0.9770
[-0.04982716  0.05973877  0.01948842 ...  0.          0.28139576
  0.2773061 ]
Sparsity at: 0.0041359879789631855
Epoch 14/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0080 - accuracy: 0.9974 - val_loss: 0.0901 - val_accuracy: 0.9763
[-0.04982716  0.05973877  0.01948842 ...  0.          0.2809943
  0.2672443 ]
Sparsity at: 0.0041359879789631855
Epoch 15/500
235/235 [==============================] - 4s 17ms/step - loss: 0.0086 - accuracy: 0.9973 - val_loss: 0.0997 - val_accuracy: 0.9754
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28502947
  0.25800997]
Sparsity at: 0.0041359879789631855
Epoch 16/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0070 - accuracy: 0.9978 - val_loss: 0.0830 - val_accuracy: 0.9786
[-0.04982716  0.05973877  0.01948842 ...  0.          0.29293737
  0.2527194 ]
Sparsity at: 0.0041359879789631855
Epoch 17/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.0898 - val_accuracy: 0.9786
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29453862
  0.25558212]
Sparsity at: 0.0041359879789631855
Epoch 18/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.0866 - val_accuracy: 0.9783
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29659212
  0.26113114]
Sparsity at: 0.0041359879789631855
Epoch 19/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 0.0837 - val_accuracy: 0.9803
[-0.04982716  0.05973877  0.01948842 ...  0.          0.2895051
  0.26165482]
Sparsity at: 0.0041359879789631855
Epoch 20/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.0975 - val_accuracy: 0.9763
[-0.04982716  0.05973877  0.01948842 ...  0.          0.293849
  0.26251072]
Sparsity at: 0.0041359879789631855
Epoch 21/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0071 - accuracy: 0.9977 - val_loss: 0.1012 - val_accuracy: 0.9769
[-0.04982716  0.05973877  0.01948842 ... -0.          0.2922375
  0.26159057]
Sparsity at: 0.0041359879789631855
Epoch 22/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.0896 - val_accuracy: 0.9789
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29632998
  0.2636737 ]
Sparsity at: 0.0041359879789631855
Epoch 23/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0861 - val_accuracy: 0.9812
[-0.04982716  0.05973877  0.01948842 ...  0.          0.29133672
  0.26979598]
Sparsity at: 0.0041359879789631855
Epoch 24/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.0862 - val_accuracy: 0.9794
[-0.04982716  0.05973877  0.01948842 ... -0.          0.2752739
  0.27974817]
Sparsity at: 0.0041359879789631855
Epoch 25/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.0826 - val_accuracy: 0.9814
[-0.04982716  0.05973877  0.01948842 ...  0.          0.2806628
  0.28145903]
Sparsity at: 0.0041359879789631855
Epoch 26/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0801 - val_accuracy: 0.9813
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28829682
  0.276715  ]
Sparsity at: 0.0041359879789631855
Epoch 27/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1113 - val_accuracy: 0.9772
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29849505
  0.26939705]
Sparsity at: 0.0041359879789631855
Epoch 28/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.1010 - val_accuracy: 0.9785
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3061583
  0.26269224]
Sparsity at: 0.0041359879789631855
Epoch 29/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.0944 - val_accuracy: 0.9809
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30132788
  0.27088454]
Sparsity at: 0.0041359879789631855
Epoch 30/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.0898 - val_accuracy: 0.9798
[-0.04982716  0.05973877  0.01948842 ... -0.          0.2958858
  0.2749377 ]
Sparsity at: 0.0041359879789631855
Epoch 31/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0799 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29212517
  0.27903095]
Sparsity at: 0.0041359879789631855
Epoch 32/500
235/235 [==============================] - 4s 15ms/step - loss: 7.3785e-04 - accuracy: 0.9998 - val_loss: 0.0859 - val_accuracy: 0.9814
[-0.04982716  0.05973877  0.01948842 ...  0.          0.29303262
  0.28101605]
Sparsity at: 0.0041359879789631855
Epoch 33/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 0.1185 - val_accuracy: 0.9748
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28040862
  0.264212  ]
Sparsity at: 0.0041359879789631855
Epoch 34/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0106 - accuracy: 0.9968 - val_loss: 0.1170 - val_accuracy: 0.9738
[-0.04982716  0.05973877  0.01948842 ...  0.          0.29225263
  0.26447278]
Sparsity at: 0.0041359879789631855
Epoch 35/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.0874 - val_accuracy: 0.9810
[-0.04982716  0.05973877  0.01948842 ... -0.          0.292528
  0.27109116]
Sparsity at: 0.0041359879789631855
Epoch 36/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.0815 - val_accuracy: 0.9824
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29903105
  0.26698968]
Sparsity at: 0.0041359879789631855
Epoch 37/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0864 - val_accuracy: 0.9823
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29839352
  0.26889652]
Sparsity at: 0.0041359879789631855
Epoch 38/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0800 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29959574
  0.2672393 ]
Sparsity at: 0.0041359879789631855
Epoch 39/500
235/235 [==============================] - 4s 15ms/step - loss: 8.1996e-04 - accuracy: 0.9998 - val_loss: 0.0814 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29619095
  0.26595417]
Sparsity at: 0.0041359879789631855
Epoch 40/500
235/235 [==============================] - 4s 15ms/step - loss: 6.0527e-04 - accuracy: 0.9998 - val_loss: 0.0753 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3004466
  0.26851308]
Sparsity at: 0.0041359879789631855
Epoch 41/500
235/235 [==============================] - 4s 15ms/step - loss: 2.3943e-04 - accuracy: 1.0000 - val_loss: 0.0759 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30373895
  0.26961058]
Sparsity at: 0.0041359879789631855
Epoch 42/500
235/235 [==============================] - 4s 15ms/step - loss: 4.7950e-04 - accuracy: 0.9999 - val_loss: 0.0776 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30561522
  0.26942354]
Sparsity at: 0.0041359879789631855
Epoch 43/500
235/235 [==============================] - 4s 15ms/step - loss: 3.6180e-04 - accuracy: 0.9999 - val_loss: 0.0771 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ...  0.          0.30141103
  0.27318367]
Sparsity at: 0.0041359879789631855
Epoch 44/500
235/235 [==============================] - 4s 15ms/step - loss: 8.7666e-05 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9850
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3025155
  0.2748859 ]
Sparsity at: 0.0041359879789631855
Epoch 45/500
235/235 [==============================] - 4s 16ms/step - loss: 5.2639e-05 - accuracy: 1.0000 - val_loss: 0.0760 - val_accuracy: 0.9855
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3030059
  0.27575946]
Sparsity at: 0.0041359879789631855
Epoch 46/500
235/235 [==============================] - 4s 15ms/step - loss: 4.5326e-05 - accuracy: 1.0000 - val_loss: 0.0763 - val_accuracy: 0.9857
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30317962
  0.27659866]
Sparsity at: 0.0041359879789631855
Epoch 47/500
235/235 [==============================] - 4s 15ms/step - loss: 4.2967e-05 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9852
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30354983
  0.27665097]
Sparsity at: 0.0041359879789631855
Epoch 48/500
235/235 [==============================] - 4s 15ms/step - loss: 3.8486e-05 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9854
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30382994
  0.2779988 ]
Sparsity at: 0.0041359879789631855
Epoch 49/500
235/235 [==============================] - 4s 15ms/step - loss: 3.2782e-05 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9850
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30410594
  0.27929664]
Sparsity at: 0.0041359879789631855
Epoch 50/500
235/235 [==============================] - 4s 15ms/step - loss: 2.7584e-05 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9852
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3046591
  0.28008497]
Sparsity at: 0.0041359879789631855
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.07416100226778077
Thresholhold -0.04982715845108032
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.10183334236959318
Thresholhold 0.07005202770233154
Using suggest threshold.
Applying new mask
Percentage zeros 0.47423333
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 ...
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.30993456551384924
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 116s 15ms/step - loss: 4.6706e-05 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 0.9853
[-0.04982716  0.05973877  0.01948842 ... -0.          0.31088376
  0.2802743 ]
Sparsity at: 0.05532306536438768
Epoch 52/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0134 - accuracy: 0.9959 - val_loss: 0.1981 - val_accuracy: 0.9608
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3168721
  0.27842847]
Sparsity at: 0.05532306536438768
Epoch 53/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0213 - accuracy: 0.9932 - val_loss: 0.0947 - val_accuracy: 0.9797
[-0.04982716  0.05973877  0.01948842 ...  0.          0.28807792
  0.26549312]
Sparsity at: 0.05532306536438768
Epoch 54/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.0747 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ...  0.          0.29878637
  0.2553946 ]
Sparsity at: 0.05532306536438768
Epoch 55/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0755 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29739475
  0.26077503]
Sparsity at: 0.05532306536438768
Epoch 56/500
235/235 [==============================] - 4s 15ms/step - loss: 5.4174e-04 - accuracy: 0.9999 - val_loss: 0.0720 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.2994943
  0.26392555]
Sparsity at: 0.05532306536438768
Epoch 57/500
235/235 [==============================] - 4s 16ms/step - loss: 3.9185e-04 - accuracy: 0.9999 - val_loss: 0.0735 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29759264
  0.26670554]
Sparsity at: 0.05532306536438768
Epoch 58/500
235/235 [==============================] - 4s 15ms/step - loss: 2.2422e-04 - accuracy: 1.0000 - val_loss: 0.0708 - val_accuracy: 0.9855
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29884848
  0.2688403 ]
Sparsity at: 0.05532306536438768
Epoch 59/500
235/235 [==============================] - 4s 16ms/step - loss: 1.4396e-04 - accuracy: 1.0000 - val_loss: 0.0711 - val_accuracy: 0.9853
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29952547
  0.27041975]
Sparsity at: 0.05532306536438768
Epoch 60/500
235/235 [==============================] - 4s 18ms/step - loss: 1.1313e-04 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9856
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29970995
  0.27185225]
Sparsity at: 0.05532306536438768
Epoch 61/500
235/235 [==============================] - 4s 16ms/step - loss: 9.1381e-05 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9856
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30020404
  0.27265635]
Sparsity at: 0.05532306536438768
Epoch 62/500
235/235 [==============================] - 4s 15ms/step - loss: 8.6439e-05 - accuracy: 1.0000 - val_loss: 0.0713 - val_accuracy: 0.9859
[-0.04982716  0.05973877  0.01948842 ...  0.          0.30439514
  0.2744069 ]
Sparsity at: 0.05532306536438768
Epoch 63/500
235/235 [==============================] - 4s 15ms/step - loss: 2.3999e-04 - accuracy: 0.9999 - val_loss: 0.0714 - val_accuracy: 0.9862
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3036872
  0.27507365]
Sparsity at: 0.05532306536438768
Epoch 64/500
235/235 [==============================] - 4s 16ms/step - loss: 1.7575e-04 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9855
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30160758
  0.27633995]
Sparsity at: 0.05532306536438768
Epoch 65/500
235/235 [==============================] - 4s 18ms/step - loss: 1.0677e-04 - accuracy: 1.0000 - val_loss: 0.0723 - val_accuracy: 0.9863
[-0.04982716  0.05973877  0.01948842 ...  0.          0.30355382
  0.27835494]
Sparsity at: 0.05532306536438768
Epoch 66/500
235/235 [==============================] - 4s 18ms/step - loss: 9.6458e-05 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9862
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3034424
  0.2777555 ]
Sparsity at: 0.05532306536438768
Epoch 67/500
235/235 [==============================] - 4s 17ms/step - loss: 5.6933e-05 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9866
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30382723
  0.27944645]
Sparsity at: 0.05532306536438768
Epoch 68/500
235/235 [==============================] - 4s 18ms/step - loss: 5.5485e-05 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9861
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3043384
  0.28074074]
Sparsity at: 0.05532306536438768
Epoch 69/500
235/235 [==============================] - 4s 17ms/step - loss: 0.0163 - accuracy: 0.9952 - val_loss: 0.1197 - val_accuracy: 0.9760
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30768427
  0.27113882]
Sparsity at: 0.05532306536438768
Epoch 70/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0075 - accuracy: 0.9975 - val_loss: 0.0810 - val_accuracy: 0.9816
[-0.04982716  0.05973877  0.01948842 ...  0.          0.30423674
  0.2582061 ]
Sparsity at: 0.05532306536438768
Epoch 71/500
235/235 [==============================] - 4s 18ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0847 - val_accuracy: 0.9830
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3021722
  0.25187626]
Sparsity at: 0.05532306536438768
Epoch 72/500
235/235 [==============================] - 4s 16ms/step - loss: 8.0413e-04 - accuracy: 0.9998 - val_loss: 0.0788 - val_accuracy: 0.9829
[-0.04982716  0.05973877  0.01948842 ... -0.          0.294386
  0.25542942]
Sparsity at: 0.05532306536438768
Epoch 73/500
235/235 [==============================] - 4s 15ms/step - loss: 3.3529e-04 - accuracy: 1.0000 - val_loss: 0.0775 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29657426
  0.25942993]
Sparsity at: 0.05532306536438768
Epoch 74/500
235/235 [==============================] - 4s 15ms/step - loss: 1.8030e-04 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.2966725
  0.2619392 ]
Sparsity at: 0.05532306536438768
Epoch 75/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1608e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ...  0.          0.29728922
  0.26399213]
Sparsity at: 0.05532306536438768
Epoch 76/500
235/235 [==============================] - 4s 16ms/step - loss: 2.1749e-04 - accuracy: 0.9999 - val_loss: 0.0783 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29840246
  0.2631213 ]
Sparsity at: 0.05532306536438768
Epoch 77/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0352e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29999495
  0.263344  ]
Sparsity at: 0.05532306536438768
Epoch 78/500
235/235 [==============================] - 4s 15ms/step - loss: 6.7196e-05 - accuracy: 1.0000 - val_loss: 0.0785 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30081528
  0.2646451 ]
Sparsity at: 0.05532306536438768
Epoch 79/500
235/235 [==============================] - 4s 16ms/step - loss: 6.1105e-05 - accuracy: 1.0000 - val_loss: 0.0784 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3016633
  0.26523677]
Sparsity at: 0.05532306536438768
Epoch 80/500
235/235 [==============================] - 4s 16ms/step - loss: 5.2155e-05 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30205697
  0.26662493]
Sparsity at: 0.05532306536438768
Epoch 81/500
235/235 [==============================] - 4s 16ms/step - loss: 5.2636e-05 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3037289
  0.2675096 ]
Sparsity at: 0.05532306536438768
Epoch 82/500
235/235 [==============================] - 4s 15ms/step - loss: 4.2220e-05 - accuracy: 1.0000 - val_loss: 0.0791 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3037151
  0.26866376]
Sparsity at: 0.05532306536438768
Epoch 83/500
235/235 [==============================] - 4s 16ms/step - loss: 3.8581e-05 - accuracy: 1.0000 - val_loss: 0.0781 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30511752
  0.26987624]
Sparsity at: 0.05532306536438768
Epoch 84/500
235/235 [==============================] - 4s 16ms/step - loss: 3.1091e-05 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3059143
  0.27018484]
Sparsity at: 0.05532306536438768
Epoch 85/500
235/235 [==============================] - 4s 15ms/step - loss: 3.8367e-05 - accuracy: 1.0000 - val_loss: 0.0794 - val_accuracy: 0.9850
[-0.04982716  0.05973877  0.01948842 ...  0.          0.30613846
  0.27019313]
Sparsity at: 0.05532306536438768
Epoch 86/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0148 - accuracy: 0.9953 - val_loss: 0.1003 - val_accuracy: 0.9810
[-0.04982716  0.05973877  0.01948842 ... -0.          0.27227536
  0.25412798]
Sparsity at: 0.05532306536438768
Epoch 87/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0066 - accuracy: 0.9980 - val_loss: 0.0844 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28151733
  0.25203684]
Sparsity at: 0.05532306536438768
Epoch 88/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.0834 - val_accuracy: 0.9814
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28999248
  0.24562049]
Sparsity at: 0.05532306536438768
Epoch 89/500
235/235 [==============================] - 4s 15ms/step - loss: 5.1121e-04 - accuracy: 0.9999 - val_loss: 0.0746 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28745735
  0.24837331]
Sparsity at: 0.05532306536438768
Epoch 90/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5512e-04 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28735048
  0.25034374]
Sparsity at: 0.05532306536438768
Epoch 91/500
235/235 [==============================] - 4s 15ms/step - loss: 1.2494e-04 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9852
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28635707
  0.2514869 ]
Sparsity at: 0.05532306536438768
Epoch 92/500
235/235 [==============================] - 4s 15ms/step - loss: 9.1560e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9852
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28741822
  0.25149155]
Sparsity at: 0.05532306536438768
Epoch 93/500
235/235 [==============================] - 4s 15ms/step - loss: 7.9539e-05 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ... -0.          0.28841567
  0.25202757]
Sparsity at: 0.05532306536438768
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 6.6189e-05 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9854
[-0.04982716  0.05973877  0.01948842 ...  0.          0.28934282
  0.25184563]
Sparsity at: 0.05532306536438768
Epoch 95/500
235/235 [==============================] - 4s 15ms/step - loss: 5.8065e-05 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29017863
  0.25285733]
Sparsity at: 0.05532306536438768
Epoch 96/500
235/235 [==============================] - 4s 15ms/step - loss: 3.0534e-04 - accuracy: 0.9999 - val_loss: 0.0772 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29200765
  0.25385278]
Sparsity at: 0.05532306536438768
Epoch 97/500
235/235 [==============================] - 4s 15ms/step - loss: 8.5834e-05 - accuracy: 1.0000 - val_loss: 0.0761 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ...  0.          0.2929127
  0.25472513]
Sparsity at: 0.05532306536438768
Epoch 98/500
235/235 [==============================] - 4s 15ms/step - loss: 5.3179e-05 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29375505
  0.2557747 ]
Sparsity at: 0.05532306536438768
Epoch 99/500
235/235 [==============================] - 4s 15ms/step - loss: 4.6621e-05 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.294817
  0.25398254]
Sparsity at: 0.05532306536438768
Epoch 100/500
235/235 [==============================] - 4s 15ms/step - loss: 3.3563e-05 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ...  0.          0.2952224
  0.25539938]
Sparsity at: 0.05532306536438768
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.1353847068942109
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.15325765617603082
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.47423333
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 ...
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.41499605027482644
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 205s 15ms/step - loss: 3.3060e-05 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29604897
  0.25505355]
Sparsity at: 0.05532306536438768
Epoch 102/500
235/235 [==============================] - 4s 15ms/step - loss: 2.6682e-05 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29714984
  0.2561345 ]
Sparsity at: 0.05532306536438768
Epoch 103/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2090e-05 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29778838
  0.25723168]
Sparsity at: 0.05532306536438768
Epoch 104/500
235/235 [==============================] - 4s 15ms/step - loss: 2.2387e-05 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9853
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29841363
  0.26070467]
Sparsity at: 0.05532306536438768
Epoch 105/500
235/235 [==============================] - 4s 15ms/step - loss: 1.6907e-05 - accuracy: 1.0000 - val_loss: 0.0773 - val_accuracy: 0.9854
[-0.04982716  0.05973877  0.01948842 ...  0.          0.2991446
  0.26118207]
Sparsity at: 0.05532306536438768
Epoch 106/500
235/235 [==============================] - 4s 15ms/step - loss: 1.7917e-05 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9854
[-0.04982716  0.05973877  0.01948842 ... -0.          0.29982653
  0.2604146 ]
Sparsity at: 0.05532306536438768
Epoch 107/500
235/235 [==============================] - 4s 15ms/step - loss: 9.4394e-04 - accuracy: 0.9998 - val_loss: 0.2249 - val_accuracy: 0.9613
[-0.04982716  0.05973877  0.01948842 ... -0.          0.27953577
  0.2598036 ]
Sparsity at: 0.05532306536438768
Epoch 108/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0176 - accuracy: 0.9948 - val_loss: 0.1053 - val_accuracy: 0.9797
[-0.04982716  0.05973877  0.01948842 ... -0.          0.2948608
  0.24591452]
Sparsity at: 0.05532306536438768
Epoch 109/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.0865 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30597484
  0.25668395]
Sparsity at: 0.05532306536438768
Epoch 110/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0916 - val_accuracy: 0.9822
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30971584
  0.25334167]
Sparsity at: 0.05532306536438768
Epoch 111/500
235/235 [==============================] - 4s 15ms/step - loss: 2.9436e-04 - accuracy: 1.0000 - val_loss: 0.0828 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.31344947
  0.2542721 ]
Sparsity at: 0.05532306536438768
Epoch 112/500
235/235 [==============================] - 4s 15ms/step - loss: 1.8015e-04 - accuracy: 1.0000 - val_loss: 0.0822 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ...  0.          0.31344506
  0.25459245]
Sparsity at: 0.05532306536438768
Epoch 113/500
235/235 [==============================] - 4s 15ms/step - loss: 1.2760e-04 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ...  0.          0.31242907
  0.25747836]
Sparsity at: 0.05532306536438768
Epoch 114/500
235/235 [==============================] - 4s 15ms/step - loss: 8.4124e-05 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ... -0.          0.312877
  0.258229  ]
Sparsity at: 0.05532306536438768
Epoch 115/500
235/235 [==============================] - 4s 16ms/step - loss: 6.2730e-05 - accuracy: 1.0000 - val_loss: 0.0799 - val_accuracy: 0.9853
[-0.04982716  0.05973877  0.01948842 ... -0.          0.31335565
  0.2594165 ]
Sparsity at: 0.05532306536438768
Epoch 116/500
235/235 [==============================] - 4s 15ms/step - loss: 5.3580e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ...  0.          0.31421694
  0.26035887]
Sparsity at: 0.05532306536438768
Epoch 117/500
235/235 [==============================] - 4s 15ms/step - loss: 5.1654e-05 - accuracy: 1.0000 - val_loss: 0.0797 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.31515834
  0.26139054]
Sparsity at: 0.05532306536438768
Epoch 118/500
235/235 [==============================] - 4s 15ms/step - loss: 4.4575e-05 - accuracy: 1.0000 - val_loss: 0.0798 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3148601
  0.26247984]
Sparsity at: 0.05532306536438768
Epoch 119/500
235/235 [==============================] - 4s 15ms/step - loss: 4.0983e-05 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3154066
  0.26289272]
Sparsity at: 0.05532306536438768
Epoch 120/500
235/235 [==============================] - 4s 15ms/step - loss: 3.4594e-05 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.31530052
  0.26418483]
Sparsity at: 0.05532306536438768
Epoch 121/500
235/235 [==============================] - 4s 15ms/step - loss: 3.3877e-05 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3159717
  0.26484454]
Sparsity at: 0.05532306536438768
Epoch 122/500
235/235 [==============================] - 4s 16ms/step - loss: 4.0860e-05 - accuracy: 1.0000 - val_loss: 0.0817 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.31623426
  0.26547432]
Sparsity at: 0.05532306536438768
Epoch 123/500
235/235 [==============================] - 4s 16ms/step - loss: 3.6856e-05 - accuracy: 1.0000 - val_loss: 0.0827 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ...  0.          0.31848082
  0.26667124]
Sparsity at: 0.05532306536438768
Epoch 124/500
235/235 [==============================] - 4s 15ms/step - loss: 8.8720e-04 - accuracy: 0.9997 - val_loss: 0.1055 - val_accuracy: 0.9812
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3087373
  0.27352598]
Sparsity at: 0.05532306536438768
Epoch 125/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0093 - accuracy: 0.9969 - val_loss: 0.1398 - val_accuracy: 0.9768
[-0.04982716  0.05973877  0.01948842 ...  0.          0.31090182
  0.26080692]
Sparsity at: 0.05532306536438768
Epoch 126/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0056 - accuracy: 0.9980 - val_loss: 0.0997 - val_accuracy: 0.9820
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30002645
  0.26113218]
Sparsity at: 0.05532306536438768
Epoch 127/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.0997 - val_accuracy: 0.9820
[-0.04982716  0.05973877  0.01948842 ...  0.          0.30606556
  0.2573662 ]
Sparsity at: 0.05532306536438768
Epoch 128/500
235/235 [==============================] - 4s 15ms/step - loss: 4.8395e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ...  0.          0.29911128
  0.25909916]
Sparsity at: 0.05532306536438768
Epoch 129/500
235/235 [==============================] - 4s 16ms/step - loss: 1.4308e-04 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ...  0.          0.30228415
  0.2627522 ]
Sparsity at: 0.05532306536438768
Epoch 130/500
235/235 [==============================] - 4s 15ms/step - loss: 9.6640e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3032974
  0.26235455]
Sparsity at: 0.05532306536438768
Epoch 131/500
235/235 [==============================] - 4s 16ms/step - loss: 7.9228e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.30604923
  0.264399  ]
Sparsity at: 0.05532306536438768
Epoch 132/500
235/235 [==============================] - 4s 15ms/step - loss: 7.4731e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3071766
  0.26380146]
Sparsity at: 0.05532306536438768
Epoch 133/500
235/235 [==============================] - 4s 15ms/step - loss: 4.6172e-05 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.30834436
  0.26445392]
Sparsity at: 0.05532306536438768
Epoch 134/500
235/235 [==============================] - 4s 15ms/step - loss: 4.1116e-04 - accuracy: 0.9999 - val_loss: 0.0939 - val_accuracy: 0.9832
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3127295
  0.2628851 ]
Sparsity at: 0.05532306536438768
Epoch 135/500
235/235 [==============================] - 4s 15ms/step - loss: 3.3876e-04 - accuracy: 0.9999 - val_loss: 0.0986 - val_accuracy: 0.9826
[-0.04982716  0.05973877  0.01948842 ... -0.          0.31954125
  0.2631654 ]
Sparsity at: 0.05532306536438768
Epoch 136/500
235/235 [==============================] - 4s 15ms/step - loss: 6.0937e-04 - accuracy: 0.9999 - val_loss: 0.0995 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3188961
  0.2612582 ]
Sparsity at: 0.05532306536438768
Epoch 137/500
235/235 [==============================] - 4s 15ms/step - loss: 5.9838e-04 - accuracy: 0.9999 - val_loss: 0.0984 - val_accuracy: 0.9829
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3173903
  0.249751  ]
Sparsity at: 0.05532306536438768
Epoch 138/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1206 - val_accuracy: 0.9786
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3238519
  0.25141045]
Sparsity at: 0.05532306536438768
Epoch 139/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.1198 - val_accuracy: 0.9786
[-0.04982716  0.05973877  0.01948842 ...  0.          0.37034503
  0.2302945 ]
Sparsity at: 0.05532306536438768
Epoch 140/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0964 - val_accuracy: 0.9828
[-0.04982716  0.05973877  0.01948842 ... -0.          0.37223265
  0.23050274]
Sparsity at: 0.05532306536438768
Epoch 141/500
235/235 [==============================] - 4s 15ms/step - loss: 8.1190e-04 - accuracy: 0.9997 - val_loss: 0.0952 - val_accuracy: 0.9824
[-0.04982716  0.05973877  0.01948842 ... -0.          0.37210748
  0.2355605 ]
Sparsity at: 0.05532306536438768
Epoch 142/500
235/235 [==============================] - 4s 15ms/step - loss: 1.9571e-04 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38293058
  0.23387823]
Sparsity at: 0.05532306536438768
Epoch 143/500
235/235 [==============================] - 4s 15ms/step - loss: 1.3818e-04 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38309458
  0.23547126]
Sparsity at: 0.05532306536438768
Epoch 144/500
235/235 [==============================] - 4s 15ms/step - loss: 6.2643e-05 - accuracy: 1.0000 - val_loss: 0.0928 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38287032
  0.23657654]
Sparsity at: 0.05532306536438768
Epoch 145/500
235/235 [==============================] - 4s 15ms/step - loss: 9.8550e-05 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38239568
  0.2371617 ]
Sparsity at: 0.05532306536438768
Epoch 146/500
235/235 [==============================] - 4s 16ms/step - loss: 5.1021e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38436702
  0.23820715]
Sparsity at: 0.05532306536438768
Epoch 147/500
235/235 [==============================] - 4s 15ms/step - loss: 2.6544e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38565645
  0.2392176 ]
Sparsity at: 0.05532306536438768
Epoch 148/500
235/235 [==============================] - 4s 15ms/step - loss: 6.6766e-04 - accuracy: 0.9998 - val_loss: 0.0973 - val_accuracy: 0.9826
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3862009
  0.24069567]
Sparsity at: 0.05532306536438768
Epoch 149/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.1213 - val_accuracy: 0.9813
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40024272
  0.25369614]
Sparsity at: 0.05532306536438768
Epoch 150/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.1078 - val_accuracy: 0.9810
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3984509
  0.24945013]
Sparsity at: 0.05532306536438768
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.2141127703646868
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.2219345703124782
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.47423333
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 ...
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.5212721049450231
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 193s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0935 - val_accuracy: 0.9823
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40363902
  0.24689984]
Sparsity at: 0.05532306536438768
Epoch 152/500
235/235 [==============================] - 4s 16ms/step - loss: 4.0806e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40155357
  0.25519785]
Sparsity at: 0.05532306536438768
Epoch 153/500
235/235 [==============================] - 4s 15ms/step - loss: 2.9562e-04 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4007723
  0.26052234]
Sparsity at: 0.05532306536438768
Epoch 154/500
235/235 [==============================] - 3s 15ms/step - loss: 6.1025e-05 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9832
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4000809
  0.25932118]
Sparsity at: 0.05532306536438768
Epoch 155/500
235/235 [==============================] - 3s 15ms/step - loss: 7.0068e-05 - accuracy: 1.0000 - val_loss: 0.0908 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39975688
  0.25887883]
Sparsity at: 0.05532306536438768
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8888e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4030536
  0.25711098]
Sparsity at: 0.05532306536438768
Epoch 157/500
235/235 [==============================] - 3s 15ms/step - loss: 3.8447e-05 - accuracy: 1.0000 - val_loss: 0.0913 - val_accuracy: 0.9852
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40303853
  0.25842872]
Sparsity at: 0.05532306536438768
Epoch 158/500
235/235 [==============================] - 4s 15ms/step - loss: 3.4097e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40190303
  0.2582449 ]
Sparsity at: 0.05532306536438768
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1831e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9850
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40180627
  0.2596528 ]
Sparsity at: 0.05532306536438768
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6071e-04 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40058398
  0.2606617 ]
Sparsity at: 0.05532306536438768
Epoch 161/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1227 - val_accuracy: 0.9794
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4027032
  0.2532185 ]
Sparsity at: 0.05532306536438768
Epoch 162/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.1106 - val_accuracy: 0.9817
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38838977
  0.22324349]
Sparsity at: 0.05532306536438768
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1117 - val_accuracy: 0.9827
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39431074
  0.22829254]
Sparsity at: 0.05532306536438768
Epoch 164/500
235/235 [==============================] - 3s 15ms/step - loss: 9.7425e-04 - accuracy: 0.9997 - val_loss: 0.1024 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39974904
  0.23598598]
Sparsity at: 0.05532306536438768
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0981e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4088891
  0.23181362]
Sparsity at: 0.05532306536438768
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6918e-04 - accuracy: 0.9999 - val_loss: 0.1000 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40933576
  0.2326822 ]
Sparsity at: 0.05532306536438768
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5070e-04 - accuracy: 0.9999 - val_loss: 0.0999 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40693745
  0.23271139]
Sparsity at: 0.05532306536438768
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9241e-04 - accuracy: 0.9999 - val_loss: 0.0980 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40815088
  0.23293655]
Sparsity at: 0.05532306536438768
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7054e-04 - accuracy: 0.9999 - val_loss: 0.1020 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40589342
  0.23813158]
Sparsity at: 0.05532306536438768
Epoch 170/500
235/235 [==============================] - 3s 15ms/step - loss: 6.5825e-05 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9850
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40560204
  0.23832662]
Sparsity at: 0.05532306536438768
Epoch 171/500
235/235 [==============================] - 3s 15ms/step - loss: 5.2292e-04 - accuracy: 0.9998 - val_loss: 0.1216 - val_accuracy: 0.9808
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40568253
  0.23503608]
Sparsity at: 0.05532306536438768
Epoch 172/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1083 - val_accuracy: 0.9816
[-0.04982716  0.05973877  0.01948842 ... -0.          0.42796567
  0.24256563]
Sparsity at: 0.05532306536438768
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1011 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40078318
  0.24065906]
Sparsity at: 0.05532306536438768
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7311e-04 - accuracy: 0.9998 - val_loss: 0.0978 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3993944
  0.24321577]
Sparsity at: 0.05532306536438768
Epoch 175/500
235/235 [==============================] - 4s 15ms/step - loss: 3.7274e-04 - accuracy: 0.9999 - val_loss: 0.0946 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38737833
  0.24664868]
Sparsity at: 0.05532306536438768
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 8.9576e-05 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38738838
  0.24869552]
Sparsity at: 0.05532306536438768
Epoch 177/500
235/235 [==============================] - 3s 15ms/step - loss: 8.0263e-04 - accuracy: 0.9998 - val_loss: 0.1023 - val_accuracy: 0.9825
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3889975
  0.25259748]
Sparsity at: 0.05532306536438768
Epoch 178/500
235/235 [==============================] - 3s 15ms/step - loss: 6.3145e-04 - accuracy: 0.9998 - val_loss: 0.1016 - val_accuracy: 0.9832
[-0.04982716  0.05973877  0.01948842 ... -0.          0.377169
  0.2521522 ]
Sparsity at: 0.05532306536438768
Epoch 179/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5752e-04 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40121153
  0.25371543]
Sparsity at: 0.05532306536438768
Epoch 180/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5374e-04 - accuracy: 0.9999 - val_loss: 0.0974 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40489456
  0.252211  ]
Sparsity at: 0.05532306536438768
Epoch 181/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2759e-04 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4038098
  0.25829002]
Sparsity at: 0.05532306536438768
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3021e-04 - accuracy: 0.9999 - val_loss: 0.1013 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40118086
  0.2590216 ]
Sparsity at: 0.05532306536438768
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1240 - val_accuracy: 0.9806
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39369828
  0.26102462]
Sparsity at: 0.05532306536438768
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1121 - val_accuracy: 0.9823
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3938333
  0.25673178]
Sparsity at: 0.05532306536438768
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.1175 - val_accuracy: 0.9822
[-0.04982716  0.05973877  0.01948842 ... -0.          0.395111
  0.2591486 ]
Sparsity at: 0.05532306536438768
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4228e-04 - accuracy: 0.9998 - val_loss: 0.0982 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40660065
  0.26025626]
Sparsity at: 0.05532306536438768
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7526e-04 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40353236
  0.26038867]
Sparsity at: 0.05532306536438768
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4260e-05 - accuracy: 1.0000 - val_loss: 0.0974 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4036507
  0.26138842]
Sparsity at: 0.05532306536438768
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6241e-05 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40348572
  0.26239958]
Sparsity at: 0.05532306536438768
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4873e-05 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4027253
  0.26282182]
Sparsity at: 0.05532306536438768
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6596e-05 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40249202
  0.26214966]
Sparsity at: 0.05532306536438768
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0795e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39514512
  0.26335058]
Sparsity at: 0.05532306536438768
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2491e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3944439
  0.26202565]
Sparsity at: 0.05532306536438768
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1752e-05 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39411375
  0.26209912]
Sparsity at: 0.05532306536438768
Epoch 195/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6732e-05 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39444962
  0.26277745]
Sparsity at: 0.05532306536438768
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4727e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9831
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39495662
  0.26660532]
Sparsity at: 0.05532306536438768
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1241 - val_accuracy: 0.9808
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3924187
  0.28874403]
Sparsity at: 0.05532306536438768
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.1129 - val_accuracy: 0.9817
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38623077
  0.27304542]
Sparsity at: 0.05532306536438768
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1182 - val_accuracy: 0.9827
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38901502
  0.25762653]
Sparsity at: 0.05532306536438768
Epoch 200/500
235/235 [==============================] - 4s 16ms/step - loss: 4.8139e-04 - accuracy: 0.9998 - val_loss: 0.1056 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38711238
  0.27013484]
Sparsity at: 0.05532306536438768
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.3013106641342773
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.2964759516939637
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.47423333
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 ...
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.6115250208721079
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 184s 12ms/step - loss: 2.6372e-04 - accuracy: 0.9999 - val_loss: 0.1035 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39125538
  0.2720272 ]
Sparsity at: 0.05532306536438768
Epoch 202/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2033e-04 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4086597
  0.27226263]
Sparsity at: 0.05532306536438768
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4562e-04 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.41928494
  0.27078995]
Sparsity at: 0.05532306536438768
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6892e-04 - accuracy: 0.9999 - val_loss: 0.1085 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40136835
  0.27203184]
Sparsity at: 0.05532306536438768
Epoch 205/500
235/235 [==============================] - 4s 16ms/step - loss: 8.6147e-05 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3950483
  0.27371612]
Sparsity at: 0.05532306536438768
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5287e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3970903
  0.27386713]
Sparsity at: 0.05532306536438768
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3581e-05 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39831993
  0.27222818]
Sparsity at: 0.05532306536438768
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0380e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.398459
  0.2725772 ]
Sparsity at: 0.05532306536438768
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9609e-05 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3987309
  0.273546  ]
Sparsity at: 0.05532306536438768
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4726e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39928967
  0.2738743 ]
Sparsity at: 0.05532306536438768
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 9.6240e-06 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39993745
  0.27403527]
Sparsity at: 0.05532306536438768
Epoch 212/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7635e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.40073967
  0.27470973]
Sparsity at: 0.05532306536438768
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2949e-04 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9827
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39315197
  0.27361724]
Sparsity at: 0.05532306536438768
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1298 - val_accuracy: 0.9812
[-0.04982716  0.05973877  0.01948842 ... -0.          0.37000448
  0.27693266]
Sparsity at: 0.05532306536438768
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0053 - accuracy: 0.9983 - val_loss: 0.1220 - val_accuracy: 0.9822
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38308087
  0.24401376]
Sparsity at: 0.05532306536438768
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1975e-04 - accuracy: 0.9998 - val_loss: 0.1140 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38869387
  0.2620652 ]
Sparsity at: 0.05532306536438768
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9901e-04 - accuracy: 0.9999 - val_loss: 0.1104 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39033696
  0.2618823 ]
Sparsity at: 0.05532306536438768
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8180e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3913286
  0.26662627]
Sparsity at: 0.05532306536438768
Epoch 219/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7902e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3908382
  0.26734078]
Sparsity at: 0.05532306536438768
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2300e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39184713
  0.26754943]
Sparsity at: 0.05532306536438768
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0248e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39319795
  0.26747864]
Sparsity at: 0.05532306536438768
Epoch 222/500
235/235 [==============================] - 3s 15ms/step - loss: 2.6220e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39990935
  0.26688474]
Sparsity at: 0.05532306536438768
Epoch 223/500
235/235 [==============================] - 3s 15ms/step - loss: 2.0096e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39938045
  0.26859707]
Sparsity at: 0.05532306536438768
Epoch 224/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7357e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39890456
  0.26935485]
Sparsity at: 0.05532306536438768
Epoch 225/500
235/235 [==============================] - 3s 15ms/step - loss: 2.6362e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39875814
  0.27026972]
Sparsity at: 0.05532306536438768
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5584e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39913246
  0.27012575]
Sparsity at: 0.05532306536438768
Epoch 227/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0367e-05 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39883775
  0.27087516]
Sparsity at: 0.05532306536438768
Epoch 228/500
235/235 [==============================] - 3s 15ms/step - loss: 1.4848e-05 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3988056
  0.2725325 ]
Sparsity at: 0.05532306536438768
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1392 - val_accuracy: 0.9797
[-0.04982716  0.05973877  0.01948842 ...  0.          0.40842462
  0.23887268]
Sparsity at: 0.05532306536438768
Epoch 230/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0048 - accuracy: 0.9984 - val_loss: 0.1409 - val_accuracy: 0.9784
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38386512
  0.25505808]
Sparsity at: 0.05532306536438768
Epoch 231/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1305 - val_accuracy: 0.9827
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38836068
  0.23818332]
Sparsity at: 0.05532306536438768
Epoch 232/500
235/235 [==============================] - 3s 15ms/step - loss: 5.1274e-04 - accuracy: 0.9998 - val_loss: 0.1177 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3843187
  0.2410608 ]
Sparsity at: 0.05532306536438768
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6390e-04 - accuracy: 0.9999 - val_loss: 0.1106 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.38970822
  0.23837051]
Sparsity at: 0.05532306536438768
Epoch 234/500
235/235 [==============================] - 4s 16ms/step - loss: 1.2974e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.38986206
  0.23669155]
Sparsity at: 0.05532306536438768
Epoch 235/500
235/235 [==============================] - 4s 16ms/step - loss: 3.1203e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3903161
  0.2366217 ]
Sparsity at: 0.05532306536438768
Epoch 236/500
235/235 [==============================] - 4s 15ms/step - loss: 2.5502e-05 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.390672
  0.23684685]
Sparsity at: 0.05532306536438768
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4492e-05 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39097905
  0.23810863]
Sparsity at: 0.05532306536438768
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9914e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3902535
  0.23874174]
Sparsity at: 0.05532306536438768
Epoch 239/500
235/235 [==============================] - 4s 16ms/step - loss: 2.0800e-05 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39045194
  0.23896268]
Sparsity at: 0.05532306536438768
Epoch 240/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6603e-05 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39058223
  0.23966385]
Sparsity at: 0.05532306536438768
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2503e-05 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39109987
  0.24005397]
Sparsity at: 0.05532306536438768
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 9.9101e-06 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.391521
  0.24002573]
Sparsity at: 0.05532306536438768
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 9.2949e-06 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3917925
  0.2402741 ]
Sparsity at: 0.05532306536438768
Epoch 244/500
235/235 [==============================] - 3s 13ms/step - loss: 9.7633e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39200884
  0.24112676]
Sparsity at: 0.05532306536438768
Epoch 245/500
235/235 [==============================] - 3s 15ms/step - loss: 8.6581e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39197296
  0.24104488]
Sparsity at: 0.05532306536438768
Epoch 246/500
235/235 [==============================] - 3s 15ms/step - loss: 8.3628e-06 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39195555
  0.2424246 ]
Sparsity at: 0.05532306536438768
Epoch 247/500
235/235 [==============================] - 3s 15ms/step - loss: 7.1205e-06 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39227766
  0.24267031]
Sparsity at: 0.05532306536438768
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2559e-06 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39278734
  0.2428052 ]
Sparsity at: 0.05532306536438768
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1828e-06 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ...  0.          0.392932
  0.24333303]
Sparsity at: 0.05532306536438768
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6370e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39355507
  0.24397428]
Sparsity at: 0.05532306536438768
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.38901842718566115
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.3690081494183204
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.47423333
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 ...
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.704682643034964
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 184s 12ms/step - loss: 4.6624e-06 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39399922
  0.24433331]
Sparsity at: 0.05532306536438768
Epoch 252/500
235/235 [==============================] - 4s 15ms/step - loss: 4.5225e-06 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39438352
  0.24483493]
Sparsity at: 0.05532306536438768
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6172e-06 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39470148
  0.24563989]
Sparsity at: 0.05532306536438768
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9525e-06 - accuracy: 1.0000 - val_loss: 0.1121 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.39506933
  0.24761824]
Sparsity at: 0.05532306536438768
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0964e-06 - accuracy: 1.0000 - val_loss: 0.1117 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39580423
  0.24818167]
Sparsity at: 0.05532306536438768
Epoch 256/500
235/235 [==============================] - 3s 15ms/step - loss: 3.3780e-06 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39745253
  0.2483204 ]
Sparsity at: 0.05532306536438768
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0328e-06 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ...  0.          0.3989004
  0.24767922]
Sparsity at: 0.05532306536438768
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5808e-06 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.39935505
  0.248646  ]
Sparsity at: 0.05532306536438768
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1451e-06 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.3994727
  0.24877688]
Sparsity at: 0.05532306536438768
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.1474 - val_accuracy: 0.9775
[-0.04982716  0.05973877  0.01948842 ...  0.          0.43925628
  0.23038983]
Sparsity at: 0.05532306536438768
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0064 - accuracy: 0.9979 - val_loss: 0.1386 - val_accuracy: 0.9816
[-0.04982716  0.05973877  0.01948842 ...  0.          0.43241113
  0.22370207]
Sparsity at: 0.05532306536438768
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1231 - val_accuracy: 0.9831
[-0.04982716  0.05973877  0.01948842 ...  0.          0.44124004
  0.22493237]
Sparsity at: 0.05532306536438768
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4441e-04 - accuracy: 0.9999 - val_loss: 0.1180 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.44021973
  0.22509414]
Sparsity at: 0.05532306536438768
Epoch 264/500
235/235 [==============================] - 3s 15ms/step - loss: 6.4876e-05 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4388117
  0.22675472]
Sparsity at: 0.05532306536438768
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2562e-05 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4385144
  0.2272301 ]
Sparsity at: 0.05532306536438768
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8112e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.44100788
  0.22737163]
Sparsity at: 0.05532306536438768
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3427e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44065437
  0.22735251]
Sparsity at: 0.05532306536438768
Epoch 268/500
235/235 [==============================] - 4s 15ms/step - loss: 2.2499e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4401549
  0.22795591]
Sparsity at: 0.05532306536438768
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0745e-05 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43582156
  0.2311895 ]
Sparsity at: 0.05532306536438768
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4265e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44216588
  0.22676004]
Sparsity at: 0.05532306536438768
Epoch 271/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7078e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ...  0.          0.44173884
  0.22685778]
Sparsity at: 0.05532306536438768
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3408e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.441773
  0.22733301]
Sparsity at: 0.05532306536438768
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1131e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44132373
  0.22780071]
Sparsity at: 0.05532306536438768
Epoch 274/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2240e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44188026
  0.22799905]
Sparsity at: 0.05532306536438768
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5328e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ...  0.          0.43608895
  0.22751404]
Sparsity at: 0.05532306536438768
Epoch 276/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1311 - val_accuracy: 0.9810
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45520017
  0.22810377]
Sparsity at: 0.05532306536438768
Epoch 277/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 0.1329 - val_accuracy: 0.9821
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4388881
  0.24626863]
Sparsity at: 0.05532306536438768
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 9.0893e-04 - accuracy: 0.9998 - val_loss: 0.1237 - val_accuracy: 0.9829
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4383832
  0.25822568]
Sparsity at: 0.05532306536438768
Epoch 279/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1183e-04 - accuracy: 0.9999 - val_loss: 0.1168 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4312693
  0.25402737]
Sparsity at: 0.05532306536438768
Epoch 280/500
235/235 [==============================] - 3s 15ms/step - loss: 2.0138e-04 - accuracy: 0.9999 - val_loss: 0.1157 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43283874
  0.25281906]
Sparsity at: 0.05532306536438768
Epoch 281/500
235/235 [==============================] - 4s 15ms/step - loss: 5.6276e-05 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.433847
  0.251294  ]
Sparsity at: 0.05532306536438768
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9593e-05 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43413007
  0.25291407]
Sparsity at: 0.05532306536438768
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2759e-05 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43340915
  0.25460523]
Sparsity at: 0.05532306536438768
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7539e-04 - accuracy: 0.9999 - val_loss: 0.1310 - val_accuracy: 0.9816
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43619737
  0.2634769 ]
Sparsity at: 0.05532306536438768
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 7.0379e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43192607
  0.25996777]
Sparsity at: 0.05532306536438768
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1066e-05 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4352768
  0.2639697 ]
Sparsity at: 0.05532306536438768
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9368e-04 - accuracy: 0.9999 - val_loss: 0.1156 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43396473
  0.2695453 ]
Sparsity at: 0.05532306536438768
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8665e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43616894
  0.2690207 ]
Sparsity at: 0.05532306536438768
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2537e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9853
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43618777
  0.26778606]
Sparsity at: 0.05532306536438768
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0318e-04 - accuracy: 0.9999 - val_loss: 0.1200 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.43182656
  0.2724257 ]
Sparsity at: 0.05532306536438768
Epoch 291/500
235/235 [==============================] - 3s 15ms/step - loss: 2.8614e-04 - accuracy: 0.9999 - val_loss: 0.1269 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4494583
  0.25073293]
Sparsity at: 0.05532306536438768
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1412 - val_accuracy: 0.9799
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44460893
  0.25590864]
Sparsity at: 0.05532306536438768
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1391 - val_accuracy: 0.9798
[-0.04982716  0.05973877  0.01948842 ... -0.          0.42628253
  0.24971262]
Sparsity at: 0.05532306536438768
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5822e-04 - accuracy: 0.9998 - val_loss: 0.1276 - val_accuracy: 0.9827
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4492327
  0.24540307]
Sparsity at: 0.05532306536438768
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2023e-04 - accuracy: 0.9999 - val_loss: 0.1212 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ...  0.          0.45012707
  0.2438707 ]
Sparsity at: 0.05532306536438768
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5284e-05 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4508162
  0.24332595]
Sparsity at: 0.05532306536438768
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3450e-05 - accuracy: 1.0000 - val_loss: 0.1201 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45131227
  0.2430325 ]
Sparsity at: 0.05532306536438768
Epoch 298/500
235/235 [==============================] - 3s 15ms/step - loss: 2.3861e-05 - accuracy: 1.0000 - val_loss: 0.1193 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ...  0.          0.45095432
  0.24257272]
Sparsity at: 0.05532306536438768
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7440e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45129728
  0.24261521]
Sparsity at: 0.05532306536438768
Epoch 300/500
235/235 [==============================] - 3s 13ms/step - loss: 4.6134e-04 - accuracy: 0.9999 - val_loss: 0.1268 - val_accuracy: 0.9819
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45140854
  0.24249601]
Sparsity at: 0.05532306536438768
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.5008987904229016
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.46366963504290837
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.47423333
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 ...
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.8045685335823194
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 180s 12ms/step - loss: 2.9841e-04 - accuracy: 0.9999 - val_loss: 0.1252 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ...  0.          0.46742654
  0.23531465]
Sparsity at: 0.05532306536438768
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5936e-04 - accuracy: 0.9999 - val_loss: 0.1265 - val_accuracy: 0.9822
[-0.04982716  0.05973877  0.01948842 ...  0.          0.46432424
  0.23241413]
Sparsity at: 0.05532306536438768
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5776e-04 - accuracy: 0.9997 - val_loss: 0.1266 - val_accuracy: 0.9826
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4590296
  0.22740059]
Sparsity at: 0.05532306536438768
Epoch 304/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1302 - val_accuracy: 0.9823
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4367955
  0.22090368]
Sparsity at: 0.05532306536438768
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6629e-04 - accuracy: 0.9999 - val_loss: 0.1303 - val_accuracy: 0.9829
[-0.04982716  0.05973877  0.01948842 ...  0.          0.44480368
  0.20039834]
Sparsity at: 0.05532306536438768
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8771e-04 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45374176
  0.19618611]
Sparsity at: 0.05532306536438768
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6920e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44415548
  0.19838066]
Sparsity at: 0.05532306536438768
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8158e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4427757
  0.19979377]
Sparsity at: 0.05532306536438768
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2974e-04 - accuracy: 1.0000 - val_loss: 0.1235 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ...  0.          0.44225204
  0.19922069]
Sparsity at: 0.05532306536438768
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7099e-05 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44080323
  0.20220506]
Sparsity at: 0.05532306536438768
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3144e-05 - accuracy: 1.0000 - val_loss: 0.1266 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4400816
  0.20287384]
Sparsity at: 0.05532306536438768
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4191e-04 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9832
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44017732
  0.23184298]
Sparsity at: 0.05532306536438768
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3923e-05 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43265793
  0.23424512]
Sparsity at: 0.05532306536438768
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2402e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43256992
  0.2350495 ]
Sparsity at: 0.05532306536438768
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3194e-06 - accuracy: 1.0000 - val_loss: 0.1250 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43308738
  0.23507847]
Sparsity at: 0.05532306536438768
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7944e-06 - accuracy: 1.0000 - val_loss: 0.1247 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43320674
  0.23811181]
Sparsity at: 0.05532306536438768
Epoch 317/500
235/235 [==============================] - 3s 15ms/step - loss: 5.6418e-06 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4334326
  0.23700659]
Sparsity at: 0.05532306536438768
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5521e-06 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43340346
  0.23649652]
Sparsity at: 0.05532306536438768
Epoch 319/500
235/235 [==============================] - 3s 15ms/step - loss: 4.4834e-06 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43361148
  0.23708822]
Sparsity at: 0.05532306536438768
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7622e-06 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.43667713
  0.23680735]
Sparsity at: 0.05532306536438768
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3420e-06 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.43666345
  0.23772842]
Sparsity at: 0.05532306536438768
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1520 - val_accuracy: 0.9801
[-0.04982716  0.05973877  0.01948842 ... -0.          0.46464515
  0.2563095 ]
Sparsity at: 0.05532306536438768
Epoch 323/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1294 - val_accuracy: 0.9817
[-0.04982716  0.05973877  0.01948842 ... -0.          0.46302938
  0.25890985]
Sparsity at: 0.05532306536438768
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2691e-04 - accuracy: 0.9997 - val_loss: 0.1244 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.46912757
  0.26121917]
Sparsity at: 0.05532306536438768
Epoch 325/500
235/235 [==============================] - 3s 15ms/step - loss: 2.5424e-04 - accuracy: 0.9999 - val_loss: 0.1192 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47171718
  0.26136354]
Sparsity at: 0.05532306536438768
Epoch 326/500
235/235 [==============================] - 3s 15ms/step - loss: 7.6093e-05 - accuracy: 1.0000 - val_loss: 0.1212 - val_accuracy: 0.9832
[-0.04982716  0.05973877  0.01948842 ...  0.          0.47282887
  0.26285452]
Sparsity at: 0.05532306536438768
Epoch 327/500
235/235 [==============================] - 3s 15ms/step - loss: 3.9379e-05 - accuracy: 1.0000 - val_loss: 0.1196 - val_accuracy: 0.9832
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47229168
  0.2630963 ]
Sparsity at: 0.05532306536438768
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4873e-05 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47178632
  0.2631623 ]
Sparsity at: 0.05532306536438768
Epoch 329/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7219e-05 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47163317
  0.26386172]
Sparsity at: 0.05532306536438768
Epoch 330/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1338e-05 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47158778
  0.26396286]
Sparsity at: 0.05532306536438768
Epoch 331/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0947e-05 - accuracy: 1.0000 - val_loss: 0.1179 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47172984
  0.2656687 ]
Sparsity at: 0.05532306536438768
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3600e-06 - accuracy: 1.0000 - val_loss: 0.1176 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4716729
  0.26599285]
Sparsity at: 0.05532306536438768
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5895e-06 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47172114
  0.26574817]
Sparsity at: 0.05532306536438768
Epoch 334/500
235/235 [==============================] - 4s 15ms/step - loss: 7.2983e-06 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47200662
  0.26659006]
Sparsity at: 0.05532306536438768
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 8.7673e-06 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47216564
  0.26749355]
Sparsity at: 0.05532306536438768
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4191e-06 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47236577
  0.26906198]
Sparsity at: 0.05532306536438768
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2357e-06 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47219855
  0.26897925]
Sparsity at: 0.05532306536438768
Epoch 338/500
235/235 [==============================] - 4s 15ms/step - loss: 9.5693e-06 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.47209817
  0.26926926]
Sparsity at: 0.05532306536438768
Epoch 339/500
235/235 [==============================] - 3s 12ms/step - loss: 5.9020e-04 - accuracy: 0.9999 - val_loss: 0.1299 - val_accuracy: 0.9825
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4634504
  0.2685126 ]
Sparsity at: 0.05532306536438768
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6404e-04 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9828
[-0.04982716  0.05973877  0.01948842 ... -0.          0.46114507
  0.26692095]
Sparsity at: 0.05532306536438768
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1286 - val_accuracy: 0.9819
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4559907
  0.2572591 ]
Sparsity at: 0.05532306536438768
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.1319 - val_accuracy: 0.9820
[-0.04982716  0.05973877  0.01948842 ... -0.          0.44147778
  0.281688  ]
Sparsity at: 0.05532306536438768
Epoch 343/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1218 - val_accuracy: 0.9832
[-0.04982716  0.05973877  0.01948842 ...  0.          0.45822462
  0.27933848]
Sparsity at: 0.05532306536438768
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7428e-04 - accuracy: 0.9998 - val_loss: 0.1268 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45594788
  0.26766077]
Sparsity at: 0.05532306536438768
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1717e-04 - accuracy: 0.9999 - val_loss: 0.1226 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45915067
  0.2869062 ]
Sparsity at: 0.05532306536438768
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5478e-05 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4590872
  0.2841896 ]
Sparsity at: 0.05532306536438768
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2858e-05 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4596594
  0.279463  ]
Sparsity at: 0.05532306536438768
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6759e-05 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45698085
  0.28273842]
Sparsity at: 0.05532306536438768
Epoch 349/500
235/235 [==============================] - 4s 15ms/step - loss: 1.2350e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4580125
  0.2816334 ]
Sparsity at: 0.05532306536438768
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0951e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ...  0.          0.45822996
  0.28127468]
Sparsity at: 0.05532306536438768
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.6140129645016401
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.5528144922561111
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.47423333
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 ...
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.8750882307586423
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 187s 11ms/step - loss: 7.6440e-06 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4581577
  0.2817292 ]
Sparsity at: 0.05532306536438768
Epoch 352/500
235/235 [==============================] - 3s 13ms/step - loss: 6.7103e-06 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9853
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45839173
  0.2817931 ]
Sparsity at: 0.05532306536438768
Epoch 353/500
235/235 [==============================] - 3s 13ms/step - loss: 7.5068e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9853
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45917845
  0.2820227 ]
Sparsity at: 0.05532306536438768
Epoch 354/500
235/235 [==============================] - 3s 13ms/step - loss: 5.0055e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9854
[-0.04982716  0.05973877  0.01948842 ...  0.          0.45933667
  0.28271312]
Sparsity at: 0.05532306536438768
Epoch 355/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3440e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9855
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4591993
  0.2824572 ]
Sparsity at: 0.05532306536438768
Epoch 356/500
235/235 [==============================] - 3s 13ms/step - loss: 4.5487e-06 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9854
[-0.04982716  0.05973877  0.01948842 ...  0.          0.4592792
  0.28230175]
Sparsity at: 0.05532306536438768
Epoch 357/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3086e-06 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9852
[-0.04982716  0.05973877  0.01948842 ...  0.          0.45821822
  0.28283468]
Sparsity at: 0.05532306536438768
Epoch 358/500
235/235 [==============================] - 3s 13ms/step - loss: 4.1505e-06 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45851034
  0.28274634]
Sparsity at: 0.05532306536438768
Epoch 359/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7418e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9850
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4581587
  0.28342044]
Sparsity at: 0.05532306536438768
Epoch 360/500
235/235 [==============================] - 3s 13ms/step - loss: 4.5167e-06 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9852
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4584856
  0.28279388]
Sparsity at: 0.05532306536438768
Epoch 361/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0606e-06 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9856
[-0.04982716  0.05973877  0.01948842 ...  0.          0.458797
  0.2833478 ]
Sparsity at: 0.05532306536438768
Epoch 362/500
235/235 [==============================] - 3s 13ms/step - loss: 8.5018e-06 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.45989436
  0.28209025]
Sparsity at: 0.05532306536438768
Epoch 363/500
235/235 [==============================] - 3s 13ms/step - loss: 5.9720e-04 - accuracy: 0.9998 - val_loss: 0.1418 - val_accuracy: 0.9811
[-0.04982716  0.05973877  0.01948842 ... -0.          0.4657365
  0.28327924]
Sparsity at: 0.05532306536438768
Epoch 364/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 0.1416 - val_accuracy: 0.9818
[-0.04982716  0.05973877  0.01948842 ...  0.          0.473192
  0.3003992 ]
Sparsity at: 0.05532306536438768
Epoch 365/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1171 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5007538
  0.30398205]
Sparsity at: 0.05532306536438768
Epoch 366/500
235/235 [==============================] - 3s 13ms/step - loss: 9.0517e-04 - accuracy: 0.9997 - val_loss: 0.1256 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.49749655
  0.29741535]
Sparsity at: 0.05532306536438768
Epoch 367/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5740e-04 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5010914
  0.29638526]
Sparsity at: 0.05532306536438768
Epoch 368/500
235/235 [==============================] - 3s 13ms/step - loss: 4.5423e-05 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5061297
  0.29758725]
Sparsity at: 0.05532306536438768
Epoch 369/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1312e-05 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50753206
  0.30142626]
Sparsity at: 0.05532306536438768
Epoch 370/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1123e-05 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50824636
  0.30123538]
Sparsity at: 0.05532306536438768
Epoch 371/500
235/235 [==============================] - 3s 13ms/step - loss: 5.5512e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5053327
  0.30245307]
Sparsity at: 0.05532306536438768
Epoch 372/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9210e-05 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5054767
  0.30247843]
Sparsity at: 0.05532306536438768
Epoch 373/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2359e-05 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9850
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50523615
  0.30327407]
Sparsity at: 0.05532306536438768
Epoch 374/500
235/235 [==============================] - 3s 13ms/step - loss: 7.7801e-06 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ...  0.          0.50510925
  0.30375057]
Sparsity at: 0.05532306536438768
Epoch 375/500
235/235 [==============================] - 3s 13ms/step - loss: 7.8674e-06 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9851
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50459
  0.30456388]
Sparsity at: 0.05532306536438768
Epoch 376/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4866e-04 - accuracy: 0.9999 - val_loss: 0.1212 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50153035
  0.31111473]
Sparsity at: 0.05532306536438768
Epoch 377/500
235/235 [==============================] - 3s 13ms/step - loss: 7.5106e-05 - accuracy: 1.0000 - val_loss: 0.1203 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5016039
  0.310885  ]
Sparsity at: 0.05532306536438768
Epoch 378/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9823e-05 - accuracy: 1.0000 - val_loss: 0.1192 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ...  0.          0.50245667
  0.31265646]
Sparsity at: 0.05532306536438768
Epoch 379/500
235/235 [==============================] - 3s 13ms/step - loss: 9.4999e-06 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5032469
  0.31338224]
Sparsity at: 0.05532306536438768
Epoch 380/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5604e-04 - accuracy: 0.9999 - val_loss: 0.1204 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5014787
  0.31252626]
Sparsity at: 0.05532306536438768
Epoch 381/500
235/235 [==============================] - 3s 13ms/step - loss: 9.1546e-05 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50098723
  0.3185105 ]
Sparsity at: 0.05532306536438768
Epoch 382/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3154e-05 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5006329
  0.31674966]
Sparsity at: 0.05532306536438768
Epoch 383/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0362e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5003671
  0.31977242]
Sparsity at: 0.05532306536438768
Epoch 384/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8784e-06 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5009117
  0.31957304]
Sparsity at: 0.05532306536438768
Epoch 385/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4874e-06 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5014185
  0.3194861 ]
Sparsity at: 0.05532306536438768
Epoch 386/500
235/235 [==============================] - 3s 13ms/step - loss: 4.5017e-06 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.501621
  0.31993163]
Sparsity at: 0.05532306536438768
Epoch 387/500
235/235 [==============================] - 3s 13ms/step - loss: 7.4749e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9823
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5023707
  0.3161628 ]
Sparsity at: 0.05532306536438768
Epoch 388/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9989 - val_loss: 0.1376 - val_accuracy: 0.9818
[-0.04982716  0.05973877  0.01948842 ... -0.          0.52636707
  0.28956854]
Sparsity at: 0.05532306536438768
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1352 - val_accuracy: 0.9826
[-0.04982716  0.05973877  0.01948842 ... -0.          0.515818
  0.30215022]
Sparsity at: 0.05532306536438768
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2414e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ...  0.          0.51656497
  0.29993185]
Sparsity at: 0.05532306536438768
Epoch 391/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9249e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5186281
  0.30094177]
Sparsity at: 0.05532306536438768
Epoch 392/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1163e-05 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ...  0.          0.52019125
  0.30100995]
Sparsity at: 0.05532306536438768
Epoch 393/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5208e-05 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5201942
  0.30117047]
Sparsity at: 0.05532306536438768
Epoch 394/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2785e-05 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.51965505
  0.3012742 ]
Sparsity at: 0.05532306536438768
Epoch 395/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0934e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5202651
  0.3011564 ]
Sparsity at: 0.05532306536438768
Epoch 396/500
235/235 [==============================] - 3s 13ms/step - loss: 8.9156e-06 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.52051437
  0.30114996]
Sparsity at: 0.05532306536438768
Epoch 397/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1424e-05 - accuracy: 1.0000 - val_loss: 0.1232 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5202042
  0.3015433 ]
Sparsity at: 0.05532306536438768
Epoch 398/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2730e-05 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.521056
  0.30076516]
Sparsity at: 0.05532306536438768
Epoch 399/500
235/235 [==============================] - 3s 13ms/step - loss: 7.2268e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5210935
  0.29945865]
Sparsity at: 0.05532306536438768
Epoch 400/500
235/235 [==============================] - 3s 13ms/step - loss: 5.9194e-06 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ...  0.          0.52109504
  0.3002102 ]
Sparsity at: 0.05532306536438768
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.6955513260454822
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.6126025692631742
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.47423333
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 ...
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.9414327682117545
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 180s 11ms/step - loss: 4.2713e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5210336
  0.30053818]
Sparsity at: 0.05532306536438768
Epoch 402/500
235/235 [==============================] - 3s 12ms/step - loss: 3.5542e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5208555
  0.30074525]
Sparsity at: 0.05532306536438768
Epoch 403/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9147e-06 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5209104
  0.300975  ]
Sparsity at: 0.05532306536438768
Epoch 404/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4511e-06 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5207364
  0.30101952]
Sparsity at: 0.05532306536438768
Epoch 405/500
235/235 [==============================] - 3s 13ms/step - loss: 5.4820e-06 - accuracy: 1.0000 - val_loss: 0.1209 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5223767
  0.30045474]
Sparsity at: 0.05532306536438768
Epoch 406/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2645e-06 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.52232724
  0.30103946]
Sparsity at: 0.05532306536438768
Epoch 407/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9993 - val_loss: 0.1832 - val_accuracy: 0.9774
[-0.04982716  0.05973877  0.01948842 ... -0.          0.49645117
  0.29623982]
Sparsity at: 0.05532306536438768
Epoch 408/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.1512 - val_accuracy: 0.9810
[-0.04982716  0.05973877  0.01948842 ...  0.          0.527881
  0.2878974 ]
Sparsity at: 0.05532306536438768
Epoch 409/500
235/235 [==============================] - 3s 15ms/step - loss: 9.1773e-04 - accuracy: 0.9997 - val_loss: 0.1365 - val_accuracy: 0.9823
[-0.04982716  0.05973877  0.01948842 ... -0.          0.52309227
  0.2774441 ]
Sparsity at: 0.05532306536438768
Epoch 410/500
235/235 [==============================] - 4s 15ms/step - loss: 4.6863e-04 - accuracy: 0.9998 - val_loss: 0.1297 - val_accuracy: 0.9827
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5361131
  0.28076628]
Sparsity at: 0.05532306536438768
Epoch 411/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3569e-05 - accuracy: 1.0000 - val_loss: 0.1270 - val_accuracy: 0.9827
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5371284
  0.27949962]
Sparsity at: 0.05532306536438768
Epoch 412/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9570e-05 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9831
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53746456
  0.2795313 ]
Sparsity at: 0.05532306536438768
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1592e-05 - accuracy: 1.0000 - val_loss: 0.1276 - val_accuracy: 0.9832
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5386377
  0.27915117]
Sparsity at: 0.05532306536438768
Epoch 414/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4689e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5378048
  0.2790982 ]
Sparsity at: 0.05532306536438768
Epoch 415/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6818e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.53727275
  0.28277516]
Sparsity at: 0.05532306536438768
Epoch 416/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0559e-05 - accuracy: 1.0000 - val_loss: 0.1259 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5365682
  0.28414074]
Sparsity at: 0.05532306536438768
Epoch 417/500
235/235 [==============================] - 3s 13ms/step - loss: 9.7407e-06 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5363638
  0.2841104 ]
Sparsity at: 0.05532306536438768
Epoch 418/500
235/235 [==============================] - 3s 13ms/step - loss: 7.0180e-06 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.53626776
  0.28413   ]
Sparsity at: 0.05532306536438768
Epoch 419/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7505e-05 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53651994
  0.28355727]
Sparsity at: 0.05532306536438768
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1300e-04 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5191347
  0.26550815]
Sparsity at: 0.05532306536438768
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4271e-05 - accuracy: 1.0000 - val_loss: 0.1282 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ... -0.          0.52168643
  0.28079024]
Sparsity at: 0.05532306536438768
Epoch 422/500
235/235 [==============================] - 3s 13ms/step - loss: 3.5510e-04 - accuracy: 0.9999 - val_loss: 0.1283 - val_accuracy: 0.9826
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53663653
  0.26957372]
Sparsity at: 0.05532306536438768
Epoch 423/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3769e-04 - accuracy: 0.9999 - val_loss: 0.1248 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.52908003
  0.27156737]
Sparsity at: 0.05532306536438768
Epoch 424/500
235/235 [==============================] - 3s 13ms/step - loss: 5.0717e-04 - accuracy: 0.9998 - val_loss: 0.1443 - val_accuracy: 0.9829
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53554624
  0.29214096]
Sparsity at: 0.05532306536438768
Epoch 425/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1321 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5330179
  0.27747452]
Sparsity at: 0.05532306536438768
Epoch 426/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3595e-04 - accuracy: 0.9999 - val_loss: 0.1308 - val_accuracy: 0.9830
[-0.04982716  0.05973877  0.01948842 ...  0.          0.52952915
  0.2802243 ]
Sparsity at: 0.05532306536438768
Epoch 427/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1631e-04 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5309915
  0.28262174]
Sparsity at: 0.05532306536438768
Epoch 428/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3524e-05 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ...  0.          0.53200155
  0.28156304]
Sparsity at: 0.05532306536438768
Epoch 429/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1513e-05 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53230065
  0.2819927 ]
Sparsity at: 0.05532306536438768
Epoch 430/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3787e-05 - accuracy: 1.0000 - val_loss: 0.1250 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ...  0.          0.53197134
  0.28296068]
Sparsity at: 0.05532306536438768
Epoch 431/500
235/235 [==============================] - 3s 13ms/step - loss: 6.5912e-05 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5325488
  0.28088087]
Sparsity at: 0.05532306536438768
Epoch 432/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1970e-04 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.54094464
  0.28011298]
Sparsity at: 0.05532306536438768
Epoch 433/500
235/235 [==============================] - 3s 13ms/step - loss: 5.2602e-05 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5449595
  0.2799128 ]
Sparsity at: 0.05532306536438768
Epoch 434/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7330e-04 - accuracy: 0.9999 - val_loss: 0.1273 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53867936
  0.29310128]
Sparsity at: 0.05532306536438768
Epoch 435/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1486 - val_accuracy: 0.9815
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5525666
  0.28388616]
Sparsity at: 0.05532306536438768
Epoch 436/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1438 - val_accuracy: 0.9829
[-0.04982716  0.05973877  0.01948842 ... -0.          0.55337054
  0.3150877 ]
Sparsity at: 0.05532306536438768
Epoch 437/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2341e-04 - accuracy: 0.9999 - val_loss: 0.1327 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5263115
  0.30942366]
Sparsity at: 0.05532306536438768
Epoch 438/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5070e-04 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5289538
  0.3103089 ]
Sparsity at: 0.05532306536438768
Epoch 439/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4775e-05 - accuracy: 1.0000 - val_loss: 0.1365 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5324671
  0.3134772 ]
Sparsity at: 0.05532306536438768
Epoch 440/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8365e-05 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53326786
  0.31338337]
Sparsity at: 0.05532306536438768
Epoch 441/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4052e-05 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53548634
  0.3148533 ]
Sparsity at: 0.05532306536438768
Epoch 442/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1088e-05 - accuracy: 1.0000 - val_loss: 0.1361 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53562844
  0.31470722]
Sparsity at: 0.05532306536438768
Epoch 443/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3710e-05 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ... -0.          0.536015
  0.31658113]
Sparsity at: 0.05532306536438768
Epoch 444/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2991e-05 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5364563
  0.3167361 ]
Sparsity at: 0.05532306536438768
Epoch 445/500
235/235 [==============================] - 3s 13ms/step - loss: 7.2624e-06 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.53677535
  0.32057145]
Sparsity at: 0.05532306536438768
Epoch 446/500
235/235 [==============================] - 3s 13ms/step - loss: 5.4908e-06 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5383855
  0.31846514]
Sparsity at: 0.05532306536438768
Epoch 447/500
235/235 [==============================] - 3s 13ms/step - loss: 5.2670e-06 - accuracy: 1.0000 - val_loss: 0.1344 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5389112
  0.31941938]
Sparsity at: 0.05532306536438768
Epoch 448/500
235/235 [==============================] - 3s 13ms/step - loss: 4.3803e-06 - accuracy: 1.0000 - val_loss: 0.1339 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.53889704
  0.32050425]
Sparsity at: 0.05532306536438768
Epoch 449/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6598e-06 - accuracy: 1.0000 - val_loss: 0.1332 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5394477
  0.32096836]
Sparsity at: 0.05532306536438768
Epoch 450/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7682e-06 - accuracy: 1.0000 - val_loss: 0.1337 - val_accuracy: 0.9843
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5400168
  0.3253128 ]
Sparsity at: 0.05532306536438768
Epoch 451/500
235/235 [==============================] - 3s 13ms/step - loss: 5.2756e-06 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ... -0.          0.54224855
  0.3115583 ]
Sparsity at: 0.05532306536438768
Epoch 452/500
235/235 [==============================] - 3s 13ms/step - loss: 9.2170e-06 - accuracy: 1.0000 - val_loss: 0.1334 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ...  0.          0.54335266
  0.3139447 ]
Sparsity at: 0.05532306536438768
Epoch 453/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5040e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9844
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5430496
  0.31725064]
Sparsity at: 0.05532306536438768
Epoch 454/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8035e-06 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.54108584
  0.32385433]
Sparsity at: 0.05532306536438768
Epoch 455/500
235/235 [==============================] - 3s 13ms/step - loss: 4.3150e-06 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.53896636
  0.32035512]
Sparsity at: 0.05532306536438768
Epoch 456/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1707 - val_accuracy: 0.9791
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5504874
  0.24591713]
Sparsity at: 0.05532306536438768
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1444 - val_accuracy: 0.9831
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5229932
  0.2547202 ]
Sparsity at: 0.05532306536438768
Epoch 458/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7820e-04 - accuracy: 0.9999 - val_loss: 0.1378 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5266959
  0.2523252 ]
Sparsity at: 0.05532306536438768
Epoch 459/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8650e-04 - accuracy: 0.9999 - val_loss: 0.1396 - val_accuracy: 0.9840
[-0.04982716  0.05973877  0.01948842 ...  0.          0.52342343
  0.25432187]
Sparsity at: 0.05532306536438768
Epoch 460/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2779e-04 - accuracy: 1.0000 - val_loss: 0.1405 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ... -0.          0.52168137
  0.2558187 ]
Sparsity at: 0.05532306536438768
Epoch 461/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7131e-05 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5201269
  0.25625893]
Sparsity at: 0.05532306536438768
Epoch 462/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1591e-05 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9845
[-0.04982716  0.05973877  0.01948842 ...  0.          0.51794636
  0.25729594]
Sparsity at: 0.05532306536438768
Epoch 463/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1354e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5176448
  0.25785604]
Sparsity at: 0.05532306536438768
Epoch 464/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2794e-05 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5172574
  0.25917947]
Sparsity at: 0.05532306536438768
Epoch 465/500
235/235 [==============================] - 3s 13ms/step - loss: 7.7986e-06 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.51677996
  0.2597112 ]
Sparsity at: 0.05532306536438768
Epoch 466/500
235/235 [==============================] - 3s 13ms/step - loss: 6.3350e-06 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.516446
  0.26018885]
Sparsity at: 0.05532306536438768
Epoch 467/500
235/235 [==============================] - 3s 13ms/step - loss: 6.0108e-06 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5162901
  0.26076654]
Sparsity at: 0.05532306536438768
Epoch 468/500
235/235 [==============================] - 3s 13ms/step - loss: 9.6696e-06 - accuracy: 1.0000 - val_loss: 0.1365 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ...  0.          0.51587224
  0.26094478]
Sparsity at: 0.05532306536438768
Epoch 469/500
235/235 [==============================] - 3s 13ms/step - loss: 6.6151e-06 - accuracy: 1.0000 - val_loss: 0.1350 - val_accuracy: 0.9849
[-0.04982716  0.05973877  0.01948842 ... -0.          0.51515514
  0.2612386 ]
Sparsity at: 0.05532306536438768
Epoch 470/500
235/235 [==============================] - 3s 13ms/step - loss: 5.0970e-06 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5150388
  0.26139995]
Sparsity at: 0.05532306536438768
Epoch 471/500
235/235 [==============================] - 3s 15ms/step - loss: 4.7767e-06 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.51389974
  0.2622641 ]
Sparsity at: 0.05532306536438768
Epoch 472/500
235/235 [==============================] - 3s 13ms/step - loss: 4.2047e-06 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9846
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5135113
  0.26244745]
Sparsity at: 0.05532306536438768
Epoch 473/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6117e-06 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5135251
  0.263105  ]
Sparsity at: 0.05532306536438768
Epoch 474/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6434e-06 - accuracy: 1.0000 - val_loss: 0.1361 - val_accuracy: 0.9847
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5134599
  0.26328033]
Sparsity at: 0.05532306536438768
Epoch 475/500
235/235 [==============================] - 3s 13ms/step - loss: 2.8866e-05 - accuracy: 1.0000 - val_loss: 0.1377 - val_accuracy: 0.9848
[-0.04982716  0.05973877  0.01948842 ... -0.          0.51327163
  0.26956156]
Sparsity at: 0.05532306536438768
Epoch 476/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.1549 - val_accuracy: 0.9814
[-0.04982716  0.05973877  0.01948842 ...  0.          0.48847535
  0.29634196]
Sparsity at: 0.05532306536438768
Epoch 477/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1510 - val_accuracy: 0.9810
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5041807
  0.30275723]
Sparsity at: 0.05532306536438768
Epoch 478/500
235/235 [==============================] - 3s 15ms/step - loss: 3.6720e-04 - accuracy: 0.9999 - val_loss: 0.1445 - val_accuracy: 0.9828
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5039699
  0.3014864 ]
Sparsity at: 0.05532306536438768
Epoch 479/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5922e-04 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5081401
  0.30228072]
Sparsity at: 0.05532306536438768
Epoch 480/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5361e-05 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ...  0.          0.50764865
  0.30287766]
Sparsity at: 0.05532306536438768
Epoch 481/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7885e-05 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5087834
  0.30331257]
Sparsity at: 0.05532306536438768
Epoch 482/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7546e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5050485
  0.3044693 ]
Sparsity at: 0.05532306536438768
Epoch 483/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2830e-05 - accuracy: 1.0000 - val_loss: 0.1356 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.5028577
  0.30476004]
Sparsity at: 0.05532306536438768
Epoch 484/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1952e-05 - accuracy: 1.0000 - val_loss: 0.1352 - val_accuracy: 0.9835
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5025818
  0.3050833 ]
Sparsity at: 0.05532306536438768
Epoch 485/500
235/235 [==============================] - 3s 13ms/step - loss: 8.4024e-06 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5017011
  0.30694985]
Sparsity at: 0.05532306536438768
Epoch 486/500
235/235 [==============================] - 3s 13ms/step - loss: 8.2682e-06 - accuracy: 1.0000 - val_loss: 0.1362 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5011062
  0.3066643 ]
Sparsity at: 0.05532306536438768
Epoch 487/500
235/235 [==============================] - 3s 13ms/step - loss: 5.6268e-06 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50109154
  0.30688888]
Sparsity at: 0.05532306536438768
Epoch 488/500
235/235 [==============================] - 3s 13ms/step - loss: 4.6665e-06 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ...  0.          0.50150394
  0.30690894]
Sparsity at: 0.05532306536438768
Epoch 489/500
235/235 [==============================] - 3s 13ms/step - loss: 6.8122e-06 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9837
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5013247
  0.30714166]
Sparsity at: 0.05532306536438768
Epoch 490/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4690e-06 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9836
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50161153
  0.30729237]
Sparsity at: 0.05532306536438768
Epoch 491/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9907e-06 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9838
[-0.04982716  0.05973877  0.01948842 ...  0.          0.501716
  0.30763265]
Sparsity at: 0.05532306536438768
Epoch 492/500
235/235 [==============================] - 3s 13ms/step - loss: 4.1219e-06 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ...  0.          0.502364
  0.30610272]
Sparsity at: 0.05532306536438768
Epoch 493/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3964e-06 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9842
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5022471
  0.30661505]
Sparsity at: 0.05532306536438768
Epoch 494/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7587e-06 - accuracy: 1.0000 - val_loss: 0.1363 - val_accuracy: 0.9839
[-0.04982716  0.05973877  0.01948842 ...  0.          0.50213057
  0.306776  ]
Sparsity at: 0.05532306536438768
Epoch 495/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1154e-05 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 0.9841
[-0.04982716  0.05973877  0.01948842 ...  0.          0.50217587
  0.288102  ]
Sparsity at: 0.05532306536438768
Epoch 496/500
235/235 [==============================] - 3s 13ms/step - loss: 4.6088e-04 - accuracy: 0.9999 - val_loss: 0.1504 - val_accuracy: 0.9815
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5296241
  0.24158844]
Sparsity at: 0.05532306536438768
Epoch 497/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1696 - val_accuracy: 0.9794
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5090498
  0.27930385]
Sparsity at: 0.05532306536438768
Epoch 498/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1422 - val_accuracy: 0.9821
[-0.04982716  0.05973877  0.01948842 ... -0.          0.503968
  0.2755768 ]
Sparsity at: 0.05532306536438768
Epoch 499/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4543e-04 - accuracy: 0.9999 - val_loss: 0.1371 - val_accuracy: 0.9833
[-0.04982716  0.05973877  0.01948842 ... -0.          0.50573874
  0.27428147]
Sparsity at: 0.05532306536438768
Epoch 500/500
235/235 [==============================] - 3s 13ms/step - loss: 8.2261e-05 - accuracy: 1.0000 - val_loss: 0.1370 - val_accuracy: 0.9834
[-0.04982716  0.05973877  0.01948842 ...  0.          0.5055063
  0.2736778 ]
Sparsity at: 0.05532306536438768
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.042179541662335396
Thresholhold -0.05633559077978134
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.08939405530691147
Thresholhold 0.003573372960090637
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10492659732699394
Thresholhold 0.13731814920902252
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 59:26 - loss: 4.5594 - accuracy: 0.0469WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0061s vs `on_train_batch_begin` time: 2.4660s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 1.5780 - accuracy: 0.8525 - val_loss: 0.9437 - val_accuracy: 0.9044
[ 4.5475988e-07  9.0963016e-07 -1.0694450e-07 ... -2.1114853e-01
 -1.6127768e-01  1.4526787e-01]
Sparsity at: 0.013428782188841202
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9004 - accuracy: 0.8984 - val_loss: 0.8537 - val_accuracy: 0.9061
[-2.8473083e-12  1.1483228e-11  5.0856444e-13 ... -1.6856050e-01
 -1.4945585e-01  1.6102970e-01]
Sparsity at: 0.013428782188841202
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8625 - accuracy: 0.8997 - val_loss: 0.8396 - val_accuracy: 0.9054
[ 1.3545059e-17  4.3443245e-17  1.4456606e-18 ... -1.4215761e-01
 -1.4187299e-01  1.8113463e-01]
Sparsity at: 0.013428782188841202
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8521 - accuracy: 0.9007 - val_loss: 0.8325 - val_accuracy: 0.9053
[ 4.2891570e-23 -6.4231276e-23 -2.1155007e-24 ... -1.2928811e-01
 -1.3472477e-01  1.9978335e-01]
Sparsity at: 0.013428782188841202
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8463 - accuracy: 0.9006 - val_loss: 0.8277 - val_accuracy: 0.9054
[-9.5993928e-29  6.4666834e-28 -4.4591661e-29 ... -1.2403209e-01
 -1.2691663e-01  2.1329701e-01]
Sparsity at: 0.013428782188841202
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8420 - accuracy: 0.9012 - val_loss: 0.8252 - val_accuracy: 0.9046
[-2.4919617e-34 -3.3479611e-33 -4.8786629e-34 ... -1.2253988e-01
 -1.1949231e-01  2.2268082e-01]
Sparsity at: 0.013428782188841202
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9010 - val_loss: 0.8226 - val_accuracy: 0.9044
[ 3.17312738e-34  5.28513877e-34 -4.87866293e-34 ... -1.22799225e-01
 -1.12141855e-01  2.28833780e-01]
Sparsity at: 0.013428782188841202
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8371 - accuracy: 0.9009 - val_loss: 0.8204 - val_accuracy: 0.9040
[ 3.17312738e-34  5.28513877e-34 -4.87866293e-34 ... -1.24042794e-01
 -1.04983211e-01  2.32911736e-01]
Sparsity at: 0.013428782188841202
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8355 - accuracy: 0.9010 - val_loss: 0.8189 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.2496045e-01
 -9.8333962e-02  2.3376772e-01]
Sparsity at: 0.013428782188841202
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8344 - accuracy: 0.9009 - val_loss: 0.8182 - val_accuracy: 0.9032
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.2592028e-01
 -9.2154019e-02  2.3430060e-01]
Sparsity at: 0.013428782188841202
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8335 - accuracy: 0.9009 - val_loss: 0.8166 - val_accuracy: 0.9035
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.2658608e-01
 -8.6029150e-02  2.3419960e-01]
Sparsity at: 0.013428782188841202
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8325 - accuracy: 0.9006 - val_loss: 0.8169 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.2716629e-01
 -7.9783775e-02  2.3338556e-01]
Sparsity at: 0.013428782188841202
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8320 - accuracy: 0.9006 - val_loss: 0.8157 - val_accuracy: 0.9033
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.2827858e-01
 -7.2897233e-02  2.3223698e-01]
Sparsity at: 0.013428782188841202
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8313 - accuracy: 0.9009 - val_loss: 0.8164 - val_accuracy: 0.9031
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.2870763e-01
 -6.6217788e-02  2.2989169e-01]
Sparsity at: 0.013428782188841202
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8308 - accuracy: 0.9010 - val_loss: 0.8156 - val_accuracy: 0.9035
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.2947418e-01
 -5.9880666e-02  2.2718878e-01]
Sparsity at: 0.013428782188841202
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8303 - accuracy: 0.9007 - val_loss: 0.8161 - val_accuracy: 0.9030
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.3072118e-01
 -5.3231589e-02  2.2447559e-01]
Sparsity at: 0.013428782188841202
Epoch 17/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8302 - accuracy: 0.9006 - val_loss: 0.8147 - val_accuracy: 0.9033
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.3205472e-01
 -4.6203740e-02  2.2203453e-01]
Sparsity at: 0.013428782188841202
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8294 - accuracy: 0.9008 - val_loss: 0.8140 - val_accuracy: 0.9029
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.3396858e-01
 -3.8817868e-02  2.1891777e-01]
Sparsity at: 0.013428782188841202
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8293 - accuracy: 0.9010 - val_loss: 0.8139 - val_accuracy: 0.9030
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.3609716e-01
 -3.0903092e-02  2.1660043e-01]
Sparsity at: 0.013428782188841202
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8287 - accuracy: 0.9013 - val_loss: 0.8130 - val_accuracy: 0.9034
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.3891685e-01
 -2.1367926e-02  2.1418606e-01]
Sparsity at: 0.013428782188841202
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8284 - accuracy: 0.9012 - val_loss: 0.8139 - val_accuracy: 0.9024
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.4271159e-01
 -1.0610447e-02  2.1245191e-01]
Sparsity at: 0.013428782188841202
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8283 - accuracy: 0.9010 - val_loss: 0.8124 - val_accuracy: 0.9031
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.4768597e-01
  2.4324600e-03  2.1122523e-01]
Sparsity at: 0.013428782188841202
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8281 - accuracy: 0.9009 - val_loss: 0.8129 - val_accuracy: 0.9034
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.5270925e-01
  1.6758425e-02  2.0936862e-01]
Sparsity at: 0.013428782188841202
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8281 - accuracy: 0.9011 - val_loss: 0.8121 - val_accuracy: 0.9027
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.5732294e-01
  3.2971688e-02  2.0744301e-01]
Sparsity at: 0.013428782188841202
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8273 - accuracy: 0.9017 - val_loss: 0.8124 - val_accuracy: 0.9034
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.6303615e-01
  5.0393268e-02  2.0540491e-01]
Sparsity at: 0.013428782188841202
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8276 - accuracy: 0.9012 - val_loss: 0.8124 - val_accuracy: 0.9033
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.6887848e-01
  6.8029106e-02  2.0382623e-01]
Sparsity at: 0.013428782188841202
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8273 - accuracy: 0.9014 - val_loss: 0.8119 - val_accuracy: 0.9035
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.7474389e-01
  8.4843218e-02  2.0244543e-01]
Sparsity at: 0.013428782188841202
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8272 - accuracy: 0.9012 - val_loss: 0.8125 - val_accuracy: 0.9029
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.7915966e-01
  9.9111311e-02  2.0087935e-01]
Sparsity at: 0.013428782188841202
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8273 - accuracy: 0.9013 - val_loss: 0.8120 - val_accuracy: 0.9036
[ 3.17312738e-34  5.28513877e-34 -4.87866293e-34 ... -1.83177650e-01
  1.11306235e-01  1.99301392e-01]
Sparsity at: 0.013428782188841202
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8273 - accuracy: 0.9009 - val_loss: 0.8113 - val_accuracy: 0.9030
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.8661909e-01
  1.2168070e-01  1.9879262e-01]
Sparsity at: 0.013428782188841202
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8271 - accuracy: 0.9017 - val_loss: 0.8115 - val_accuracy: 0.9031
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.8911327e-01
  1.3033628e-01  1.9865364e-01]
Sparsity at: 0.013428782188841202
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8269 - accuracy: 0.9015 - val_loss: 0.8113 - val_accuracy: 0.9030
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.9211307e-01
  1.3732308e-01  1.9895643e-01]
Sparsity at: 0.013428782188841202
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8268 - accuracy: 0.9014 - val_loss: 0.8122 - val_accuracy: 0.9034
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.9425830e-01
  1.4281784e-01  1.9968069e-01]
Sparsity at: 0.013428782188841202
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8268 - accuracy: 0.9010 - val_loss: 0.8117 - val_accuracy: 0.9034
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.9578844e-01
  1.4711940e-01  1.9983456e-01]
Sparsity at: 0.013428782188841202
Epoch 35/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8267 - accuracy: 0.9014 - val_loss: 0.8119 - val_accuracy: 0.9033
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.9742593e-01
  1.5133455e-01  2.0035335e-01]
Sparsity at: 0.013428782188841202
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8270 - accuracy: 0.9014 - val_loss: 0.8122 - val_accuracy: 0.9035
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.9811359e-01
  1.5420264e-01  2.0088133e-01]
Sparsity at: 0.013428782188841202
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8265 - accuracy: 0.9016 - val_loss: 0.8111 - val_accuracy: 0.9034
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -1.9940473e-01
  1.5691824e-01  2.0143208e-01]
Sparsity at: 0.013428782188841202
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8265 - accuracy: 0.9013 - val_loss: 0.8115 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0084132e-01
  1.5904133e-01  2.0193990e-01]
Sparsity at: 0.013428782188841202
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8267 - accuracy: 0.9014 - val_loss: 0.8118 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0176548e-01
  1.6157974e-01  2.0300451e-01]
Sparsity at: 0.013428782188841202
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8266 - accuracy: 0.9014 - val_loss: 0.8115 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0229991e-01
  1.6367090e-01  2.0333448e-01]
Sparsity at: 0.013428782188841202
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8265 - accuracy: 0.9013 - val_loss: 0.8105 - val_accuracy: 0.9031
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0316249e-01
  1.6504906e-01  2.0427863e-01]
Sparsity at: 0.013428782188841202
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8262 - accuracy: 0.9015 - val_loss: 0.8118 - val_accuracy: 0.9032
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0392489e-01
  1.6690196e-01  2.0502971e-01]
Sparsity at: 0.013428782188841202
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8263 - accuracy: 0.9011 - val_loss: 0.8106 - val_accuracy: 0.9032
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0507868e-01
  1.6848043e-01  2.0541219e-01]
Sparsity at: 0.013428782188841202
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8264 - accuracy: 0.9015 - val_loss: 0.8110 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0562387e-01
  1.6960602e-01  2.0620069e-01]
Sparsity at: 0.013428782188841202
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8264 - accuracy: 0.9014 - val_loss: 0.8110 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0618463e-01
  1.7072159e-01  2.0682956e-01]
Sparsity at: 0.013428782188841202
Epoch 46/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8262 - accuracy: 0.9021 - val_loss: 0.8110 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0684901e-01
  1.7152943e-01  2.0750417e-01]
Sparsity at: 0.013428782188841202
Epoch 47/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8259 - accuracy: 0.9014 - val_loss: 0.8108 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0757803e-01
  1.7325091e-01  2.0828106e-01]
Sparsity at: 0.013428782188841202
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8260 - accuracy: 0.9014 - val_loss: 0.8108 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0820795e-01
  1.7399633e-01  2.0915723e-01]
Sparsity at: 0.013428782188841202
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8264 - accuracy: 0.9014 - val_loss: 0.8118 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0864348e-01
  1.7471792e-01  2.0970197e-01]
Sparsity at: 0.013428782188841202
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8262 - accuracy: 0.9015 - val_loss: 0.8109 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0936647e-01
  1.7569935e-01  2.1039537e-01]
Sparsity at: 0.013428782188841202
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.009441253711567343
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.03559083378736716
Thresholhold -0.02379034087061882
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.11488785808859348
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 0.8259 - accuracy: 0.9017 - val_loss: 0.8108 - val_accuracy: 0.9036
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.0965262e-01
  1.7660750e-01  2.1101573e-01]
Sparsity at: 0.013428782188841202
Epoch 52/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8258 - accuracy: 0.9015 - val_loss: 0.8105 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1057877e-01
  1.7766993e-01  2.1179493e-01]
Sparsity at: 0.013428782188841202
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9017 - val_loss: 0.8100 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1084429e-01
  1.7788151e-01  2.1248877e-01]
Sparsity at: 0.013428782188841202
Epoch 54/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9015 - val_loss: 0.8107 - val_accuracy: 0.9036
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1129917e-01
  1.7879543e-01  2.1280846e-01]
Sparsity at: 0.013428782188841202
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9015 - val_loss: 0.8104 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1150069e-01
  1.7997697e-01  2.1306658e-01]
Sparsity at: 0.013428782188841202
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8260 - accuracy: 0.9015 - val_loss: 0.8109 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1159616e-01
  1.8016966e-01  2.1302189e-01]
Sparsity at: 0.013428782188841202
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9016 - val_loss: 0.8104 - val_accuracy: 0.9033
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1159831e-01
  1.8111238e-01  2.1379106e-01]
Sparsity at: 0.013428782188841202
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9013 - val_loss: 0.8108 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1223912e-01
  1.8207943e-01  2.1438715e-01]
Sparsity at: 0.013428782188841202
Epoch 59/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8260 - accuracy: 0.9013 - val_loss: 0.8098 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1250932e-01
  1.8276453e-01  2.1403818e-01]
Sparsity at: 0.013428782188841202
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9018 - val_loss: 0.8110 - val_accuracy: 0.9033
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1265607e-01
  1.8376535e-01  2.1399593e-01]
Sparsity at: 0.013428782188841202
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9019 - val_loss: 0.8104 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1316163e-01
  1.8441734e-01  2.1453735e-01]
Sparsity at: 0.013428782188841202
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8258 - accuracy: 0.9014 - val_loss: 0.8112 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1342434e-01
  1.8508859e-01  2.1536690e-01]
Sparsity at: 0.013428782188841202
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9018 - val_loss: 0.8108 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1366639e-01
  1.8553291e-01  2.1547391e-01]
Sparsity at: 0.013428782188841202
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9014 - val_loss: 0.8101 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1391803e-01
  1.8608092e-01  2.1552931e-01]
Sparsity at: 0.013428782188841202
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8257 - accuracy: 0.9014 - val_loss: 0.8113 - val_accuracy: 0.9031
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1395041e-01
  1.8615401e-01  2.1506196e-01]
Sparsity at: 0.013428782188841202
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9020 - val_loss: 0.8101 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1461868e-01
  1.8680488e-01  2.1583962e-01]
Sparsity at: 0.013428782188841202
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9020 - val_loss: 0.8102 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1452565e-01
  1.8685091e-01  2.1637264e-01]
Sparsity at: 0.013428782188841202
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9015 - val_loss: 0.8097 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1492539e-01
  1.8703038e-01  2.1622847e-01]
Sparsity at: 0.013428782188841202
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9019 - val_loss: 0.8106 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1508636e-01
  1.8720873e-01  2.1651430e-01]
Sparsity at: 0.013428782188841202
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9014 - val_loss: 0.8104 - val_accuracy: 0.9035
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1509993e-01
  1.8755139e-01  2.1654898e-01]
Sparsity at: 0.013428782188841202
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8258 - accuracy: 0.9017 - val_loss: 0.8106 - val_accuracy: 0.9035
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1531779e-01
  1.8756884e-01  2.1704994e-01]
Sparsity at: 0.013428782188841202
Epoch 72/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8257 - accuracy: 0.9016 - val_loss: 0.8109 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1520807e-01
  1.8755570e-01  2.1712416e-01]
Sparsity at: 0.013428782188841202
Epoch 73/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8256 - accuracy: 0.9022 - val_loss: 0.8105 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1546698e-01
  1.8791614e-01  2.1735533e-01]
Sparsity at: 0.013428782188841202
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8099 - val_accuracy: 0.9035
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1571183e-01
  1.8786383e-01  2.1732402e-01]
Sparsity at: 0.013428782188841202
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8103 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1583232e-01
  1.8817924e-01  2.1724449e-01]
Sparsity at: 0.013428782188841202
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9019 - val_loss: 0.8107 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1558972e-01
  1.8796206e-01  2.1694465e-01]
Sparsity at: 0.013428782188841202
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9018 - val_loss: 0.8103 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1542946e-01
  1.8766227e-01  2.1721591e-01]
Sparsity at: 0.013428782188841202
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9021 - val_loss: 0.8110 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1581295e-01
  1.8799131e-01  2.1745715e-01]
Sparsity at: 0.013428782188841202
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9018 - val_loss: 0.8101 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1584940e-01
  1.8828377e-01  2.1732926e-01]
Sparsity at: 0.013428782188841202
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8258 - accuracy: 0.9019 - val_loss: 0.8102 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1579039e-01
  1.8808867e-01  2.1748200e-01]
Sparsity at: 0.013428782188841202
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9017 - val_loss: 0.8101 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1579254e-01
  1.8772689e-01  2.1789362e-01]
Sparsity at: 0.013428782188841202
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9019 - val_loss: 0.8098 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1608305e-01
  1.8840347e-01  2.1798377e-01]
Sparsity at: 0.013428782188841202
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9019 - val_loss: 0.8099 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1589050e-01
  1.8860726e-01  2.1785197e-01]
Sparsity at: 0.013428782188841202
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9020 - val_loss: 0.8099 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1647447e-01
  1.8862234e-01  2.1810566e-01]
Sparsity at: 0.013428782188841202
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8101 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1621238e-01
  1.8888083e-01  2.1785569e-01]
Sparsity at: 0.013428782188841202
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9017 - val_loss: 0.8107 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1600997e-01
  1.8843308e-01  2.1785162e-01]
Sparsity at: 0.013428782188841202
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9014 - val_loss: 0.8105 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1628718e-01
  1.8815732e-01  2.1817762e-01]
Sparsity at: 0.013428782188841202
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9020 - val_loss: 0.8103 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1639244e-01
  1.8846615e-01  2.1817213e-01]
Sparsity at: 0.013428782188841202
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9016 - val_loss: 0.8095 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1641313e-01
  1.8842827e-01  2.1786851e-01]
Sparsity at: 0.013428782188841202
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9021 - val_loss: 0.8103 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1672736e-01
  1.8889549e-01  2.1785705e-01]
Sparsity at: 0.013428782188841202
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8103 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1645734e-01
  1.8916592e-01  2.1797971e-01]
Sparsity at: 0.013428782188841202
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9018 - val_loss: 0.8107 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1660419e-01
  1.8909994e-01  2.1792488e-01]
Sparsity at: 0.013428782188841202
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9019 - val_loss: 0.8094 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1672015e-01
  1.8861338e-01  2.1794356e-01]
Sparsity at: 0.013428782188841202
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9021 - val_loss: 0.8108 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1694936e-01
  1.8928029e-01  2.1819331e-01]
Sparsity at: 0.013428782188841202
Epoch 95/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8254 - accuracy: 0.9020 - val_loss: 0.8106 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1693899e-01
  1.8890829e-01  2.1862958e-01]
Sparsity at: 0.013428782188841202
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9020 - val_loss: 0.8099 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1660189e-01
  1.8887699e-01  2.1852274e-01]
Sparsity at: 0.013428782188841202
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9022 - val_loss: 0.8097 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1708454e-01
  1.8926629e-01  2.1876776e-01]
Sparsity at: 0.013428782188841202
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9017 - val_loss: 0.8092 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1728937e-01
  1.8936679e-01  2.1862167e-01]
Sparsity at: 0.013428782188841202
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8099 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1701878e-01
  1.8890822e-01  2.1881764e-01]
Sparsity at: 0.013428782188841202
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9020 - val_loss: 0.8100 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1732220e-01
  1.8900216e-01  2.1887980e-01]
Sparsity at: 0.013428782188841202
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.015024891875651813
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.045309476913746316
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.14850107733482965
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 52s 7ms/step - loss: 0.8256 - accuracy: 0.9015 - val_loss: 0.8106 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1691750e-01
  1.8893623e-01  2.1873592e-01]
Sparsity at: 0.013428782188841202
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8253 - accuracy: 0.9018 - val_loss: 0.8102 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1730509e-01
  1.8905446e-01  2.1916999e-01]
Sparsity at: 0.013428782188841202
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9021 - val_loss: 0.8096 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1708423e-01
  1.8898202e-01  2.1877083e-01]
Sparsity at: 0.013428782188841202
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9018 - val_loss: 0.8115 - val_accuracy: 0.9030
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1753532e-01
  1.8918510e-01  2.1929243e-01]
Sparsity at: 0.013428782188841202
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9017 - val_loss: 0.8104 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1758722e-01
  1.8914010e-01  2.1874397e-01]
Sparsity at: 0.013428782188841202
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9022 - val_loss: 0.8098 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1752451e-01
  1.8903551e-01  2.1890281e-01]
Sparsity at: 0.013428782188841202
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9015 - val_loss: 0.8097 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1751237e-01
  1.8861207e-01  2.1919568e-01]
Sparsity at: 0.013428782188841202
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9020 - val_loss: 0.8106 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1751499e-01
  1.8857031e-01  2.1907878e-01]
Sparsity at: 0.013428782188841202
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9014 - val_loss: 0.8095 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1744214e-01
  1.8821216e-01  2.1922256e-01]
Sparsity at: 0.013428782188841202
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9018 - val_loss: 0.8103 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1786608e-01
  1.8836269e-01  2.1973003e-01]
Sparsity at: 0.013428782188841202
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9014 - val_loss: 0.8098 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1810207e-01
  1.8837088e-01  2.1995316e-01]
Sparsity at: 0.013428782188841202
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8099 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1805945e-01
  1.8856473e-01  2.1999797e-01]
Sparsity at: 0.013428782188841202
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8089 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1837850e-01
  1.8855773e-01  2.1995427e-01]
Sparsity at: 0.013428782188841202
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9019 - val_loss: 0.8093 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1818906e-01
  1.8835481e-01  2.2043721e-01]
Sparsity at: 0.013428782188841202
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1830077e-01
  1.8831664e-01  2.2045733e-01]
Sparsity at: 0.013428782188841202
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9021 - val_loss: 0.8095 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1863565e-01
  1.8823230e-01  2.2066894e-01]
Sparsity at: 0.013428782188841202
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8100 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1847680e-01
  1.8845226e-01  2.2066788e-01]
Sparsity at: 0.013428782188841202
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9022 - val_loss: 0.8098 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1872316e-01
  1.8790263e-01  2.2087130e-01]
Sparsity at: 0.013428782188841202
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9019 - val_loss: 0.8105 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1904820e-01
  1.8822761e-01  2.2125420e-01]
Sparsity at: 0.013428782188841202
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9022 - val_loss: 0.8100 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1920653e-01
  1.8792649e-01  2.2122265e-01]
Sparsity at: 0.013428782188841202
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9018 - val_loss: 0.8093 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1953017e-01
  1.8759955e-01  2.2166930e-01]
Sparsity at: 0.013428782188841202
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9018 - val_loss: 0.8101 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1953610e-01
  1.8799391e-01  2.2177435e-01]
Sparsity at: 0.013428782188841202
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9018 - val_loss: 0.8102 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1956435e-01
  1.8782035e-01  2.2155693e-01]
Sparsity at: 0.013428782188841202
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9015 - val_loss: 0.8097 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1912192e-01
  1.8740067e-01  2.2146490e-01]
Sparsity at: 0.013428782188841202
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9020 - val_loss: 0.8098 - val_accuracy: 0.9036
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1938056e-01
  1.8762331e-01  2.2166619e-01]
Sparsity at: 0.013428782188841202
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9019 - val_loss: 0.8093 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1946156e-01
  1.8759070e-01  2.2146851e-01]
Sparsity at: 0.013428782188841202
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8094 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1957368e-01
  1.8752669e-01  2.2172451e-01]
Sparsity at: 0.013428782188841202
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9024 - val_loss: 0.8095 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1980201e-01
  1.8817912e-01  2.2157042e-01]
Sparsity at: 0.013428782188841202
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1998744e-01
  1.8802285e-01  2.2170831e-01]
Sparsity at: 0.013428782188841202
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8253 - accuracy: 0.9021 - val_loss: 0.8103 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1983947e-01
  1.8817185e-01  2.2181414e-01]
Sparsity at: 0.013428782188841202
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9024 - val_loss: 0.8096 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1975648e-01
  1.8815584e-01  2.2155789e-01]
Sparsity at: 0.013428782188841202
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9023 - val_loss: 0.8096 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1942802e-01
  1.8780729e-01  2.2130731e-01]
Sparsity at: 0.013428782188841202
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1948864e-01
  1.8789469e-01  2.2165261e-01]
Sparsity at: 0.013428782188841202
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9020 - val_loss: 0.8096 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1928988e-01
  1.8786409e-01  2.2158840e-01]
Sparsity at: 0.013428782188841202
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8089 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1977217e-01
  1.8814011e-01  2.2188319e-01]
Sparsity at: 0.013428782188841202
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9021 - val_loss: 0.8096 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1932432e-01
  1.8787715e-01  2.2187150e-01]
Sparsity at: 0.013428782188841202
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1957201e-01
  1.8818991e-01  2.2189413e-01]
Sparsity at: 0.013428782188841202
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9020 - val_loss: 0.8094 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1952100e-01
  1.8835042e-01  2.2181131e-01]
Sparsity at: 0.013428782188841202
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9020 - val_loss: 0.8099 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1921436e-01
  1.8807252e-01  2.2209415e-01]
Sparsity at: 0.013428782188841202
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9017 - val_loss: 0.8084 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1981990e-01
  1.8825257e-01  2.2213607e-01]
Sparsity at: 0.013428782188841202
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1979170e-01
  1.8868835e-01  2.2230649e-01]
Sparsity at: 0.013428782188841202
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2028460e-01
  1.8890183e-01  2.2244377e-01]
Sparsity at: 0.013428782188841202
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9020 - val_loss: 0.8096 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1957169e-01
  1.8871959e-01  2.2228521e-01]
Sparsity at: 0.013428782188841202
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1979278e-01
  1.8862616e-01  2.2215046e-01]
Sparsity at: 0.013428782188841202
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9027 - val_loss: 0.8088 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2030315e-01
  1.8856107e-01  2.2209099e-01]
Sparsity at: 0.013428782188841202
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8083 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2062553e-01
  1.8887448e-01  2.2240750e-01]
Sparsity at: 0.013428782188841202
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2003874e-01
  1.8867487e-01  2.2192641e-01]
Sparsity at: 0.013428782188841202
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2018965e-01
  1.8927270e-01  2.2217008e-01]
Sparsity at: 0.013428782188841202
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9035
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2000231e-01
  1.8896244e-01  2.2216129e-01]
Sparsity at: 0.013428782188841202
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2002183e-01
  1.8919499e-01  2.2174932e-01]
Sparsity at: 0.013428782188841202
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.020792046287780308
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.05585218787255908
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.1789812290466699
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 49s 7ms/step - loss: 0.8253 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1991199e-01
  1.8881467e-01  2.2163633e-01]
Sparsity at: 0.013428782188841202
Epoch 152/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1990427e-01
  1.8916716e-01  2.2171541e-01]
Sparsity at: 0.013428782188841202
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8095 - val_accuracy: 0.9036
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2021005e-01
  1.8920575e-01  2.2178040e-01]
Sparsity at: 0.013428782188841202
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9016 - val_loss: 0.8089 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2035377e-01
  1.8927330e-01  2.2239132e-01]
Sparsity at: 0.013428782188841202
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9026 - val_loss: 0.8095 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1977003e-01
  1.8908103e-01  2.2162078e-01]
Sparsity at: 0.013428782188841202
Epoch 156/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1971317e-01
  1.8917902e-01  2.2176202e-01]
Sparsity at: 0.013428782188841202
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2006735e-01
  1.8925540e-01  2.2139469e-01]
Sparsity at: 0.013428782188841202
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8252 - accuracy: 0.9025 - val_loss: 0.8092 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1963045e-01
  1.8917167e-01  2.2136983e-01]
Sparsity at: 0.013428782188841202
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1980971e-01
  1.8947741e-01  2.2186331e-01]
Sparsity at: 0.013428782188841202
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8088 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1964361e-01
  1.8970221e-01  2.2164269e-01]
Sparsity at: 0.013428782188841202
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2029231e-01
  1.8955842e-01  2.2126974e-01]
Sparsity at: 0.013428782188841202
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9019 - val_loss: 0.8092 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2012214e-01
  1.8939570e-01  2.2176006e-01]
Sparsity at: 0.013428782188841202
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8089 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1998785e-01
  1.8937102e-01  2.2150403e-01]
Sparsity at: 0.013428782188841202
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9038
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1995552e-01
  1.8943179e-01  2.2161783e-01]
Sparsity at: 0.013428782188841202
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9020 - val_loss: 0.8089 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2002983e-01
  1.8938637e-01  2.2153018e-01]
Sparsity at: 0.013428782188841202
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8084 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1987577e-01
  1.8941106e-01  2.2141796e-01]
Sparsity at: 0.013428782188841202
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1987103e-01
  1.8942073e-01  2.2157770e-01]
Sparsity at: 0.013428782188841202
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9021 - val_loss: 0.8095 - val_accuracy: 0.9034
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2027795e-01
  1.8930638e-01  2.2131224e-01]
Sparsity at: 0.013428782188841202
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9026 - val_loss: 0.8092 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1974969e-01
  1.8940632e-01  2.2121806e-01]
Sparsity at: 0.013428782188841202
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9053
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2018303e-01
  1.8969157e-01  2.2108877e-01]
Sparsity at: 0.013428782188841202
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9021 - val_loss: 0.8098 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2015695e-01
  1.8953590e-01  2.2122557e-01]
Sparsity at: 0.013428782188841202
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8103 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1993667e-01
  1.8967526e-01  2.2101218e-01]
Sparsity at: 0.013428782188841202
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8082 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2028992e-01
  1.8957663e-01  2.2152053e-01]
Sparsity at: 0.013428782188841202
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2000399e-01
  1.8933606e-01  2.2133254e-01]
Sparsity at: 0.013428782188841202
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8094 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1987593e-01
  1.8951559e-01  2.2121239e-01]
Sparsity at: 0.013428782188841202
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9027 - val_loss: 0.8093 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2002859e-01
  1.8961023e-01  2.2128963e-01]
Sparsity at: 0.013428782188841202
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9020 - val_loss: 0.8088 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2017328e-01
  1.8939088e-01  2.2150552e-01]
Sparsity at: 0.013428782188841202
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8096 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2004776e-01
  1.8997234e-01  2.2118588e-01]
Sparsity at: 0.013428782188841202
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9020 - val_loss: 0.8091 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2020438e-01
  1.8936287e-01  2.2140606e-01]
Sparsity at: 0.013428782188841202
Epoch 180/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1987115e-01
  1.8950178e-01  2.2115724e-01]
Sparsity at: 0.013428782188841202
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9021 - val_loss: 0.8084 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2032031e-01
  1.8935455e-01  2.2152431e-01]
Sparsity at: 0.013428782188841202
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1980034e-01
  1.8929297e-01  2.2079025e-01]
Sparsity at: 0.013428782188841202
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1988244e-01
  1.8937515e-01  2.2110310e-01]
Sparsity at: 0.013428782188841202
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2011313e-01
  1.8940745e-01  2.2127511e-01]
Sparsity at: 0.013428782188841202
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9020 - val_loss: 0.8085 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1964718e-01
  1.8902153e-01  2.2106732e-01]
Sparsity at: 0.013428782188841202
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1980679e-01
  1.8939045e-01  2.2137694e-01]
Sparsity at: 0.013428782188841202
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8081 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2026271e-01
  1.8955331e-01  2.2160232e-01]
Sparsity at: 0.013428782188841202
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2005320e-01
  1.8956378e-01  2.2172320e-01]
Sparsity at: 0.013428782188841202
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1991584e-01
  1.8923758e-01  2.2122888e-01]
Sparsity at: 0.013428782188841202
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8095 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1986294e-01
  1.8938507e-01  2.2094774e-01]
Sparsity at: 0.013428782188841202
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1985416e-01
  1.8948595e-01  2.2108071e-01]
Sparsity at: 0.013428782188841202
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8090 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2047018e-01
  1.8943965e-01  2.2143391e-01]
Sparsity at: 0.013428782188841202
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8092 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2053917e-01
  1.8956949e-01  2.2108352e-01]
Sparsity at: 0.013428782188841202
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8084 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2008599e-01
  1.8961443e-01  2.2110626e-01]
Sparsity at: 0.013428782188841202
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8096 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2031750e-01
  1.8945520e-01  2.2134954e-01]
Sparsity at: 0.013428782188841202
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2030391e-01
  1.8999153e-01  2.2126873e-01]
Sparsity at: 0.013428782188841202
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9039
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2011054e-01
  1.8951766e-01  2.2109798e-01]
Sparsity at: 0.013428782188841202
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8095 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2017413e-01
  1.8978642e-01  2.2119749e-01]
Sparsity at: 0.013428782188841202
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1972160e-01
  1.8921435e-01  2.2124641e-01]
Sparsity at: 0.013428782188841202
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2021963e-01
  1.8967004e-01  2.2127877e-01]
Sparsity at: 0.013428782188841202
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.027723946008678446
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.06840466487041752
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.2025289757641069
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1993877e-01
  1.8949914e-01  2.2111784e-01]
Sparsity at: 0.013428782188841202
Epoch 202/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1991411e-01
  1.8950154e-01  2.2149597e-01]
Sparsity at: 0.013428782188841202
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9026 - val_loss: 0.8097 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2021282e-01
  1.8961611e-01  2.2150679e-01]
Sparsity at: 0.013428782188841202
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8093 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1970625e-01
  1.8929419e-01  2.2080384e-01]
Sparsity at: 0.013428782188841202
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8097 - val_accuracy: 0.9037
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2032419e-01
  1.8952590e-01  2.2139420e-01]
Sparsity at: 0.013428782188841202
Epoch 206/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1993960e-01
  1.8951701e-01  2.2126244e-01]
Sparsity at: 0.013428782188841202
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8099 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1996902e-01
  1.8955404e-01  2.2135922e-01]
Sparsity at: 0.013428782188841202
Epoch 208/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8102 - val_accuracy: 0.9033
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2024183e-01
  1.8933730e-01  2.2148785e-01]
Sparsity at: 0.013428782188841202
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8097 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1982183e-01
  1.8909380e-01  2.2116485e-01]
Sparsity at: 0.013428782188841202
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9021 - val_loss: 0.8093 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2034442e-01
  1.8926063e-01  2.2132108e-01]
Sparsity at: 0.013428782188841202
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2000444e-01
  1.8945417e-01  2.2129019e-01]
Sparsity at: 0.013428782188841202
Epoch 212/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1975483e-01
  1.8926625e-01  2.2077030e-01]
Sparsity at: 0.013428782188841202
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9019 - val_loss: 0.8102 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2026406e-01
  1.8967707e-01  2.2103231e-01]
Sparsity at: 0.013428782188841202
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9021 - val_loss: 0.8084 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2012763e-01
  1.8941410e-01  2.2160186e-01]
Sparsity at: 0.013428782188841202
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2002825e-01
  1.8949190e-01  2.2139734e-01]
Sparsity at: 0.013428782188841202
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2003846e-01
  1.8954104e-01  2.2115301e-01]
Sparsity at: 0.013428782188841202
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9021 - val_loss: 0.8097 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1982609e-01
  1.8934388e-01  2.2109407e-01]
Sparsity at: 0.013428782188841202
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1990599e-01
  1.8940960e-01  2.2070596e-01]
Sparsity at: 0.013428782188841202
Epoch 219/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8085 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1975961e-01
  1.8911813e-01  2.2066469e-01]
Sparsity at: 0.013428782188841202
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8249 - accuracy: 0.9019 - val_loss: 0.8100 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1972491e-01
  1.8902649e-01  2.2069360e-01]
Sparsity at: 0.013428782188841202
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1978401e-01
  1.8921059e-01  2.2059932e-01]
Sparsity at: 0.013428782188841202
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2003371e-01
  1.8899971e-01  2.2095919e-01]
Sparsity at: 0.013428782188841202
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2011574e-01
  1.8926242e-01  2.2094275e-01]
Sparsity at: 0.013428782188841202
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8249 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1992840e-01
  1.8944018e-01  2.2096606e-01]
Sparsity at: 0.013428782188841202
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1990557e-01
  1.8933024e-01  2.2125554e-01]
Sparsity at: 0.013428782188841202
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1993418e-01
  1.8894550e-01  2.2077751e-01]
Sparsity at: 0.013428782188841202
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8092 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2018702e-01
  1.8927360e-01  2.2097670e-01]
Sparsity at: 0.013428782188841202
Epoch 228/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1976155e-01
  1.8915358e-01  2.2078781e-01]
Sparsity at: 0.013428782188841202
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8085 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1946758e-01
  1.8882763e-01  2.2063670e-01]
Sparsity at: 0.013428782188841202
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1981329e-01
  1.8903263e-01  2.2096179e-01]
Sparsity at: 0.013428782188841202
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1982016e-01
  1.8895155e-01  2.2080916e-01]
Sparsity at: 0.013428782188841202
Epoch 232/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9022 - val_loss: 0.8083 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2006185e-01
  1.8934600e-01  2.2100881e-01]
Sparsity at: 0.013428782188841202
Epoch 233/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1985021e-01
  1.8926944e-01  2.2117533e-01]
Sparsity at: 0.013428782188841202
Epoch 234/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1961419e-01
  1.8880652e-01  2.2113413e-01]
Sparsity at: 0.013428782188841202
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9022 - val_loss: 0.8094 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1981913e-01
  1.8897901e-01  2.2074836e-01]
Sparsity at: 0.013428782188841202
Epoch 236/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8250 - accuracy: 0.9019 - val_loss: 0.8092 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1940784e-01
  1.8899132e-01  2.2038297e-01]
Sparsity at: 0.013428782188841202
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1976997e-01
  1.8944161e-01  2.2049545e-01]
Sparsity at: 0.013428782188841202
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8094 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1985760e-01
  1.8924576e-01  2.2101867e-01]
Sparsity at: 0.013428782188841202
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9056
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2017215e-01
  1.8924311e-01  2.2069940e-01]
Sparsity at: 0.013428782188841202
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1999571e-01
  1.8924224e-01  2.2092319e-01]
Sparsity at: 0.013428782188841202
Epoch 241/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8084 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1947457e-01
  1.8885359e-01  2.2084881e-01]
Sparsity at: 0.013428782188841202
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9020 - val_loss: 0.8087 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1983674e-01
  1.8916094e-01  2.2093205e-01]
Sparsity at: 0.013428782188841202
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8096 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1974470e-01
  1.8902208e-01  2.2121526e-01]
Sparsity at: 0.013428782188841202
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1971346e-01
  1.8860355e-01  2.2091362e-01]
Sparsity at: 0.013428782188841202
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1984121e-01
  1.8867676e-01  2.2060543e-01]
Sparsity at: 0.013428782188841202
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1975967e-01
  1.8881582e-01  2.2061694e-01]
Sparsity at: 0.013428782188841202
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9025 - val_loss: 0.8097 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1961404e-01
  1.8900225e-01  2.2094241e-01]
Sparsity at: 0.013428782188841202
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1992327e-01
  1.8928017e-01  2.2077848e-01]
Sparsity at: 0.013428782188841202
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1961409e-01
  1.8928488e-01  2.2082821e-01]
Sparsity at: 0.013428782188841202
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1970195e-01
  1.8936919e-01  2.2095348e-01]
Sparsity at: 0.013428782188841202
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.03561964924113026
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.08273246205477491
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.22271870241878666
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 50s 7ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1944305e-01
  1.8890239e-01  2.2072834e-01]
Sparsity at: 0.013428782188841202
Epoch 252/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8094 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1971230e-01
  1.8894389e-01  2.2091855e-01]
Sparsity at: 0.013428782188841202
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9053
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1995465e-01
  1.8915661e-01  2.2078009e-01]
Sparsity at: 0.013428782188841202
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2003731e-01
  1.8941976e-01  2.2064103e-01]
Sparsity at: 0.013428782188841202
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9025 - val_loss: 0.8089 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1999148e-01
  1.8918093e-01  2.2080322e-01]
Sparsity at: 0.013428782188841202
Epoch 256/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2024502e-01
  1.8942998e-01  2.2115825e-01]
Sparsity at: 0.013428782188841202
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9021 - val_loss: 0.8083 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2005776e-01
  1.8902192e-01  2.2104517e-01]
Sparsity at: 0.013428782188841202
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9025 - val_loss: 0.8082 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2010730e-01
  1.8934314e-01  2.2093843e-01]
Sparsity at: 0.013428782188841202
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8091 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2015008e-01
  1.8932320e-01  2.2075191e-01]
Sparsity at: 0.013428782188841202
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9020 - val_loss: 0.8098 - val_accuracy: 0.9036
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1996781e-01
  1.8898408e-01  2.2074127e-01]
Sparsity at: 0.013428782188841202
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8095 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1936001e-01
  1.8894596e-01  2.2044410e-01]
Sparsity at: 0.013428782188841202
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2014165e-01
  1.8896648e-01  2.2104502e-01]
Sparsity at: 0.013428782188841202
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8095 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1975105e-01
  1.8906645e-01  2.2088400e-01]
Sparsity at: 0.013428782188841202
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8082 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1991175e-01
  1.8924665e-01  2.2048570e-01]
Sparsity at: 0.013428782188841202
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8098 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1969028e-01
  1.8894884e-01  2.2032125e-01]
Sparsity at: 0.013428782188841202
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8097 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1978229e-01
  1.8906568e-01  2.2051747e-01]
Sparsity at: 0.013428782188841202
Epoch 267/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2044790e-01
  1.8940170e-01  2.2084177e-01]
Sparsity at: 0.013428782188841202
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8251 - accuracy: 0.9020 - val_loss: 0.8089 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1980360e-01
  1.8937655e-01  2.2039616e-01]
Sparsity at: 0.013428782188841202
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1999250e-01
  1.8929774e-01  2.2053701e-01]
Sparsity at: 0.013428782188841202
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8089 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1982609e-01
  1.8892100e-01  2.2075731e-01]
Sparsity at: 0.013428782188841202
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1993777e-01
  1.8916160e-01  2.2046846e-01]
Sparsity at: 0.013428782188841202
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9018 - val_loss: 0.8085 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1989323e-01
  1.8857199e-01  2.2065207e-01]
Sparsity at: 0.013428782188841202
Epoch 273/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8093 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1989933e-01
  1.8869862e-01  2.2068991e-01]
Sparsity at: 0.013428782188841202
Epoch 274/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8250 - accuracy: 0.9019 - val_loss: 0.8085 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1978649e-01
  1.8846150e-01  2.2023103e-01]
Sparsity at: 0.013428782188841202
Epoch 275/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8091 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1955009e-01
  1.8851633e-01  2.2064289e-01]
Sparsity at: 0.013428782188841202
Epoch 276/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1995509e-01
  1.8889435e-01  2.2068359e-01]
Sparsity at: 0.013428782188841202
Epoch 277/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8250 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1974617e-01
  1.8850484e-01  2.2067928e-01]
Sparsity at: 0.013428782188841202
Epoch 278/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8096 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1980377e-01
  1.8845768e-01  2.2053728e-01]
Sparsity at: 0.013428782188841202
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9027 - val_loss: 0.8089 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2040485e-01
  1.8894394e-01  2.2094204e-01]
Sparsity at: 0.013428782188841202
Epoch 280/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9018 - val_loss: 0.8090 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1973167e-01
  1.8877593e-01  2.2041328e-01]
Sparsity at: 0.013428782188841202
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9020 - val_loss: 0.8084 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2007392e-01
  1.8859774e-01  2.2037424e-01]
Sparsity at: 0.013428782188841202
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8097 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1958351e-01
  1.8878685e-01  2.2049147e-01]
Sparsity at: 0.013428782188841202
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8082 - val_accuracy: 0.9053
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2003034e-01
  1.8873803e-01  2.2077684e-01]
Sparsity at: 0.013428782188841202
Epoch 284/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8084 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1995902e-01
  1.8875501e-01  2.2049251e-01]
Sparsity at: 0.013428782188841202
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8084 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2002561e-01
  1.8895823e-01  2.2036865e-01]
Sparsity at: 0.013428782188841202
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8094 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1967314e-01
  1.8875374e-01  2.2047907e-01]
Sparsity at: 0.013428782188841202
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8090 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1962282e-01
  1.8867871e-01  2.2060379e-01]
Sparsity at: 0.013428782188841202
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8093 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2000699e-01
  1.8883280e-01  2.2047977e-01]
Sparsity at: 0.013428782188841202
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1982931e-01
  1.8866557e-01  2.2004627e-01]
Sparsity at: 0.013428782188841202
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8083 - val_accuracy: 0.9056
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1992235e-01
  1.8886524e-01  2.2034410e-01]
Sparsity at: 0.013428782188841202
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8087 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2017328e-01
  1.8899363e-01  2.2058488e-01]
Sparsity at: 0.013428782188841202
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.2001714e-01
  1.8899842e-01  2.2081670e-01]
Sparsity at: 0.013428782188841202
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8101 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1966176e-01
  1.8870482e-01  2.2087073e-01]
Sparsity at: 0.013428782188841202
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9024 - val_loss: 0.8090 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1971893e-01
  1.8835396e-01  2.2054780e-01]
Sparsity at: 0.013428782188841202
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1978314e-01
  1.8862723e-01  2.2063772e-01]
Sparsity at: 0.013428782188841202
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8087 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1977782e-01
  1.8836731e-01  2.2046098e-01]
Sparsity at: 0.013428782188841202
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1943246e-01
  1.8831220e-01  2.2001575e-01]
Sparsity at: 0.013428782188841202
Epoch 298/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1961206e-01
  1.8846945e-01  2.2028606e-01]
Sparsity at: 0.013428782188841202
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9027 - val_loss: 0.8088 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1948543e-01
  1.8816493e-01  2.2012886e-01]
Sparsity at: 0.013428782188841202
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1941717e-01
  1.8855116e-01  2.2045572e-01]
Sparsity at: 0.013428782188841202
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.04380975309229651
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.09488863894561472
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.24104228113534631
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 49s 7ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8082 - val_accuracy: 0.9053
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1981218e-01
  1.8809834e-01  2.2056501e-01]
Sparsity at: 0.013428782188841202
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8092 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1970063e-01
  1.8824397e-01  2.2004765e-01]
Sparsity at: 0.013428782188841202
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1942891e-01
  1.8819499e-01  2.1975784e-01]
Sparsity at: 0.013428782188841202
Epoch 304/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1962026e-01
  1.8837743e-01  2.2035764e-01]
Sparsity at: 0.013428782188841202
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1962148e-01
  1.8864402e-01  2.2012231e-01]
Sparsity at: 0.013428782188841202
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8083 - val_accuracy: 0.9059
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1954399e-01
  1.8865621e-01  2.2016093e-01]
Sparsity at: 0.013428782188841202
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1951197e-01
  1.8851759e-01  2.2031657e-01]
Sparsity at: 0.013428782188841202
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1963798e-01
  1.8845934e-01  2.2026348e-01]
Sparsity at: 0.013428782188841202
Epoch 309/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1918280e-01
  1.8826146e-01  2.2011593e-01]
Sparsity at: 0.013428782188841202
Epoch 310/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8092 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1923429e-01
  1.8819202e-01  2.1978363e-01]
Sparsity at: 0.013428782188841202
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1918496e-01
  1.8802394e-01  2.1977438e-01]
Sparsity at: 0.013428782188841202
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1937272e-01
  1.8805118e-01  2.2004890e-01]
Sparsity at: 0.013428782188841202
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1893659e-01
  1.8767448e-01  2.1993987e-01]
Sparsity at: 0.013428782188841202
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1898493e-01
  1.8796663e-01  2.2003047e-01]
Sparsity at: 0.013428782188841202
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1879031e-01
  1.8808232e-01  2.1971251e-01]
Sparsity at: 0.013428782188841202
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1958262e-01
  1.8817119e-01  2.1969901e-01]
Sparsity at: 0.013428782188841202
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9018 - val_loss: 0.8091 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1930464e-01
  1.8790632e-01  2.1954504e-01]
Sparsity at: 0.013428782188841202
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1920796e-01
  1.8794324e-01  2.1977848e-01]
Sparsity at: 0.013428782188841202
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9055
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1889052e-01
  1.8765022e-01  2.1949656e-01]
Sparsity at: 0.013428782188841202
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8077 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1896493e-01
  1.8770143e-01  2.1979393e-01]
Sparsity at: 0.013428782188841202
Epoch 321/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8087 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1936275e-01
  1.8801986e-01  2.1980606e-01]
Sparsity at: 0.013428782188841202
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1930154e-01
  1.8776955e-01  2.2003715e-01]
Sparsity at: 0.013428782188841202
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9053
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1892338e-01
  1.8778042e-01  2.2004178e-01]
Sparsity at: 0.013428782188841202
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1938255e-01
  1.8786441e-01  2.1937868e-01]
Sparsity at: 0.013428782188841202
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1920905e-01
  1.8777835e-01  2.1956690e-01]
Sparsity at: 0.013428782188841202
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8096 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1967995e-01
  1.8786703e-01  2.2005415e-01]
Sparsity at: 0.013428782188841202
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9019 - val_loss: 0.8091 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1925557e-01
  1.8782739e-01  2.1988761e-01]
Sparsity at: 0.013428782188841202
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8096 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1921463e-01
  1.8809320e-01  2.2023028e-01]
Sparsity at: 0.013428782188841202
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1970180e-01
  1.8812802e-01  2.2003673e-01]
Sparsity at: 0.013428782188841202
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1947920e-01
  1.8790688e-01  2.1954072e-01]
Sparsity at: 0.013428782188841202
Epoch 331/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9020 - val_loss: 0.8086 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1956597e-01
  1.8830946e-01  2.1946670e-01]
Sparsity at: 0.013428782188841202
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1891636e-01
  1.8776880e-01  2.1961860e-01]
Sparsity at: 0.013428782188841202
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8080 - val_accuracy: 0.9056
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1892539e-01
  1.8730499e-01  2.1979678e-01]
Sparsity at: 0.013428782188841202
Epoch 334/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8083 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1926357e-01
  1.8749920e-01  2.1957660e-01]
Sparsity at: 0.013428782188841202
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1839365e-01
  1.8742250e-01  2.1913566e-01]
Sparsity at: 0.013428782188841202
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1844718e-01
  1.8730378e-01  2.1882646e-01]
Sparsity at: 0.013428782188841202
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8098 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1850280e-01
  1.8779813e-01  2.1905443e-01]
Sparsity at: 0.013428782188841202
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9018 - val_loss: 0.8087 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1878502e-01
  1.8743201e-01  2.1912998e-01]
Sparsity at: 0.013428782188841202
Epoch 339/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8089 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1896920e-01
  1.8774855e-01  2.1915790e-01]
Sparsity at: 0.013428782188841202
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1877247e-01
  1.8763603e-01  2.1909802e-01]
Sparsity at: 0.013428782188841202
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8085 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1886037e-01
  1.8750276e-01  2.1934691e-01]
Sparsity at: 0.013428782188841202
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1875484e-01
  1.8760169e-01  2.1936096e-01]
Sparsity at: 0.013428782188841202
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1870136e-01
  1.8755350e-01  2.1922807e-01]
Sparsity at: 0.013428782188841202
Epoch 344/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8250 - accuracy: 0.9015 - val_loss: 0.8093 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1872808e-01
  1.8740493e-01  2.1914642e-01]
Sparsity at: 0.013428782188841202
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8079 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1838789e-01
  1.8700802e-01  2.1920450e-01]
Sparsity at: 0.013428782188841202
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1843718e-01
  1.8718612e-01  2.1867914e-01]
Sparsity at: 0.013428782188841202
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9028 - val_loss: 0.8088 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1855341e-01
  1.8758440e-01  2.1881008e-01]
Sparsity at: 0.013428782188841202
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1839955e-01
  1.8776670e-01  2.1837036e-01]
Sparsity at: 0.013428782188841202
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9019 - val_loss: 0.8090 - val_accuracy: 0.9053
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1807794e-01
  1.8716247e-01  2.1863133e-01]
Sparsity at: 0.013428782188841202
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1838747e-01
  1.8745445e-01  2.1902086e-01]
Sparsity at: 0.013428782188841202
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.051436077562252436
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.10767383222726767
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.2567379317195666
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 49s 7ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8090 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1762061e-01
  1.8722188e-01  2.1845147e-01]
Sparsity at: 0.013428782188841202
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1795852e-01
  1.8698703e-01  2.1855639e-01]
Sparsity at: 0.013428782188841202
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1778373e-01
  1.8713641e-01  2.1825841e-01]
Sparsity at: 0.013428782188841202
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1780285e-01
  1.8710981e-01  2.1813346e-01]
Sparsity at: 0.013428782188841202
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8090 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1739219e-01
  1.8684207e-01  2.1795422e-01]
Sparsity at: 0.013428782188841202
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9020 - val_loss: 0.8094 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1691717e-01
  1.8699683e-01  2.1788156e-01]
Sparsity at: 0.013428782188841202
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9026 - val_loss: 0.8086 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1703675e-01
  1.8723828e-01  2.1785863e-01]
Sparsity at: 0.013428782188841202
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8090 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1659151e-01
  1.8694058e-01  2.1751326e-01]
Sparsity at: 0.013428782188841202
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8100 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1631376e-01
  1.8712033e-01  2.1733025e-01]
Sparsity at: 0.013428782188841202
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1624607e-01
  1.8718956e-01  2.1734606e-01]
Sparsity at: 0.013428782188841202
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8094 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1562153e-01
  1.8695483e-01  2.1711177e-01]
Sparsity at: 0.013428782188841202
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1565640e-01
  1.8720305e-01  2.1710661e-01]
Sparsity at: 0.013428782188841202
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8083 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1579705e-01
  1.8675593e-01  2.1716076e-01]
Sparsity at: 0.013428782188841202
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8098 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1566996e-01
  1.8710071e-01  2.1692102e-01]
Sparsity at: 0.013428782188841202
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1560460e-01
  1.8692438e-01  2.1658406e-01]
Sparsity at: 0.013428782188841202
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1508086e-01
  1.8690562e-01  2.1649967e-01]
Sparsity at: 0.013428782188841202
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8085 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1526821e-01
  1.8685026e-01  2.1653479e-01]
Sparsity at: 0.013428782188841202
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9021 - val_loss: 0.8097 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1479852e-01
  1.8666197e-01  2.1603902e-01]
Sparsity at: 0.013428782188841202
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8085 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1515261e-01
  1.8695730e-01  2.1645203e-01]
Sparsity at: 0.013428782188841202
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1536201e-01
  1.8722501e-01  2.1659574e-01]
Sparsity at: 0.013428782188841202
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8085 - val_accuracy: 0.9055
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1509950e-01
  1.8701999e-01  2.1654318e-01]
Sparsity at: 0.013428782188841202
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8097 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1504855e-01
  1.8705827e-01  2.1629865e-01]
Sparsity at: 0.013428782188841202
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1532209e-01
  1.8720311e-01  2.1683164e-01]
Sparsity at: 0.013428782188841202
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8091 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1509168e-01
  1.8711156e-01  2.1657753e-01]
Sparsity at: 0.013428782188841202
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9055
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1515152e-01
  1.8680337e-01  2.1644086e-01]
Sparsity at: 0.013428782188841202
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8083 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1557193e-01
  1.8671665e-01  2.1663509e-01]
Sparsity at: 0.013428782188841202
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8086 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1571282e-01
  1.8711688e-01  2.1710549e-01]
Sparsity at: 0.013428782188841202
Epoch 378/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9055
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1574858e-01
  1.8710266e-01  2.1694203e-01]
Sparsity at: 0.013428782188841202
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1533662e-01
  1.8650608e-01  2.1673575e-01]
Sparsity at: 0.013428782188841202
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8089 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1520686e-01
  1.8666007e-01  2.1682781e-01]
Sparsity at: 0.013428782188841202
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1525373e-01
  1.8650870e-01  2.1661696e-01]
Sparsity at: 0.013428782188841202
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9020 - val_loss: 0.8094 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1549810e-01
  1.8662596e-01  2.1670234e-01]
Sparsity at: 0.013428782188841202
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8086 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1549864e-01
  1.8651287e-01  2.1687245e-01]
Sparsity at: 0.013428782188841202
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9020 - val_loss: 0.8083 - val_accuracy: 0.9055
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1538508e-01
  1.8685134e-01  2.1677396e-01]
Sparsity at: 0.013428782188841202
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8079 - val_accuracy: 0.9058
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1524662e-01
  1.8669352e-01  2.1684790e-01]
Sparsity at: 0.013428782188841202
Epoch 386/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1578360e-01
  1.8700577e-01  2.1689986e-01]
Sparsity at: 0.013428782188841202
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8079 - val_accuracy: 0.9055
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1582144e-01
  1.8692987e-01  2.1651964e-01]
Sparsity at: 0.013428782188841202
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1581095e-01
  1.8718390e-01  2.1659426e-01]
Sparsity at: 0.013428782188841202
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8098 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1532865e-01
  1.8711269e-01  2.1631406e-01]
Sparsity at: 0.013428782188841202
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8103 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1566254e-01
  1.8709457e-01  2.1627435e-01]
Sparsity at: 0.013428782188841202
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1572262e-01
  1.8681280e-01  2.1686384e-01]
Sparsity at: 0.013428782188841202
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1519797e-01
  1.8632090e-01  2.1688895e-01]
Sparsity at: 0.013428782188841202
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8081 - val_accuracy: 0.9058
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1519440e-01
  1.8651238e-01  2.1680224e-01]
Sparsity at: 0.013428782188841202
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8093 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1543199e-01
  1.8653014e-01  2.1687053e-01]
Sparsity at: 0.013428782188841202
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9022 - val_loss: 0.8080 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1538907e-01
  1.8680707e-01  2.1705277e-01]
Sparsity at: 0.013428782188841202
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8085 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1556865e-01
  1.8625399e-01  2.1694416e-01]
Sparsity at: 0.013428782188841202
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8243 - accuracy: 0.9024 - val_loss: 0.8080 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1569505e-01
  1.8687463e-01  2.1671921e-01]
Sparsity at: 0.013428782188841202
Epoch 398/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1523504e-01
  1.8677776e-01  2.1690390e-01]
Sparsity at: 0.013428782188841202
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1564639e-01
  1.8691644e-01  2.1695516e-01]
Sparsity at: 0.013428782188841202
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1591075e-01
  1.8681724e-01  2.1719533e-01]
Sparsity at: 0.013428782188841202
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.05637868867256124
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.11695918551944917
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.2671557808276752
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9059
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1557805e-01
  1.8664294e-01  2.1697123e-01]
Sparsity at: 0.013428782188841202
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9053
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1613102e-01
  1.8688075e-01  2.1711615e-01]
Sparsity at: 0.013428782188841202
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9019 - val_loss: 0.8087 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1560346e-01
  1.8662621e-01  2.1711856e-01]
Sparsity at: 0.013428782188841202
Epoch 404/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1562442e-01
  1.8632098e-01  2.1698421e-01]
Sparsity at: 0.013428782188841202
Epoch 405/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8095 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1617462e-01
  1.8687001e-01  2.1766649e-01]
Sparsity at: 0.013428782188841202
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1549730e-01
  1.8671276e-01  2.1710916e-01]
Sparsity at: 0.013428782188841202
Epoch 407/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8098 - val_accuracy: 0.9041
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1582660e-01
  1.8661918e-01  2.1705933e-01]
Sparsity at: 0.013428782188841202
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9019 - val_loss: 0.8089 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1610625e-01
  1.8632816e-01  2.1686175e-01]
Sparsity at: 0.013428782188841202
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1583025e-01
  1.8646628e-01  2.1669018e-01]
Sparsity at: 0.013428782188841202
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1596757e-01
  1.8638910e-01  2.1687196e-01]
Sparsity at: 0.013428782188841202
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1576862e-01
  1.8638015e-01  2.1685511e-01]
Sparsity at: 0.013428782188841202
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8083 - val_accuracy: 0.9053
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1658617e-01
  1.8655561e-01  2.1718721e-01]
Sparsity at: 0.013428782188841202
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1634474e-01
  1.8650711e-01  2.1704403e-01]
Sparsity at: 0.013428782188841202
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8243 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1664622e-01
  1.8680727e-01  2.1743231e-01]
Sparsity at: 0.013428782188841202
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1658838e-01
  1.8684055e-01  2.1778204e-01]
Sparsity at: 0.013428782188841202
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1636407e-01
  1.8660708e-01  2.1740048e-01]
Sparsity at: 0.013428782188841202
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8082 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1663457e-01
  1.8661863e-01  2.1753581e-01]
Sparsity at: 0.013428782188841202
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9027 - val_loss: 0.8088 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1630764e-01
  1.8644385e-01  2.1723606e-01]
Sparsity at: 0.013428782188841202
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8087 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1685036e-01
  1.8649296e-01  2.1729586e-01]
Sparsity at: 0.013428782188841202
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8091 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1635532e-01
  1.8638326e-01  2.1745746e-01]
Sparsity at: 0.013428782188841202
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9019 - val_loss: 0.8094 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1588612e-01
  1.8628250e-01  2.1753040e-01]
Sparsity at: 0.013428782188841202
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1637550e-01
  1.8641600e-01  2.1715343e-01]
Sparsity at: 0.013428782188841202
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1636909e-01
  1.8668395e-01  2.1716903e-01]
Sparsity at: 0.013428782188841202
Epoch 424/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9027 - val_loss: 0.8079 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1653786e-01
  1.8670361e-01  2.1728393e-01]
Sparsity at: 0.013428782188841202
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8086 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1656689e-01
  1.8683933e-01  2.1723074e-01]
Sparsity at: 0.013428782188841202
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9028 - val_loss: 0.8084 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1640152e-01
  1.8693578e-01  2.1722770e-01]
Sparsity at: 0.013428782188841202
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9027 - val_loss: 0.8090 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1616691e-01
  1.8696913e-01  2.1724266e-01]
Sparsity at: 0.013428782188841202
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8095 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1642646e-01
  1.8646422e-01  2.1708453e-01]
Sparsity at: 0.013428782188841202
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8090 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1636097e-01
  1.8656762e-01  2.1711488e-01]
Sparsity at: 0.013428782188841202
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1690458e-01
  1.8655169e-01  2.1790306e-01]
Sparsity at: 0.013428782188841202
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8090 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1664286e-01
  1.8670195e-01  2.1740584e-01]
Sparsity at: 0.013428782188841202
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1663997e-01
  1.8681528e-01  2.1720389e-01]
Sparsity at: 0.013428782188841202
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1596566e-01
  1.8622647e-01  2.1702780e-01]
Sparsity at: 0.013428782188841202
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9019 - val_loss: 0.8090 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1611071e-01
  1.8618940e-01  2.1699849e-01]
Sparsity at: 0.013428782188841202
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1618146e-01
  1.8553138e-01  2.1677530e-01]
Sparsity at: 0.013428782188841202
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8096 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1629345e-01
  1.8663336e-01  2.1733594e-01]
Sparsity at: 0.013428782188841202
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9020 - val_loss: 0.8101 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1647087e-01
  1.8623435e-01  2.1710762e-01]
Sparsity at: 0.013428782188841202
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1686602e-01
  1.8661262e-01  2.1744506e-01]
Sparsity at: 0.013428782188841202
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8092 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1698961e-01
  1.8651502e-01  2.1710502e-01]
Sparsity at: 0.013428782188841202
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1692415e-01
  1.8651094e-01  2.1725184e-01]
Sparsity at: 0.013428782188841202
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9028 - val_loss: 0.8085 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1642426e-01
  1.8615086e-01  2.1722904e-01]
Sparsity at: 0.013428782188841202
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9052
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1682362e-01
  1.8655106e-01  2.1752010e-01]
Sparsity at: 0.013428782188841202
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8095 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1684821e-01
  1.8663618e-01  2.1752967e-01]
Sparsity at: 0.013428782188841202
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8079 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1694435e-01
  1.8643752e-01  2.1728410e-01]
Sparsity at: 0.013428782188841202
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9019 - val_loss: 0.8096 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1710083e-01
  1.8689947e-01  2.1720956e-01]
Sparsity at: 0.013428782188841202
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8087 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1667975e-01
  1.8683153e-01  2.1732639e-01]
Sparsity at: 0.013428782188841202
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1670364e-01
  1.8689214e-01  2.1738324e-01]
Sparsity at: 0.013428782188841202
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8093 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1665311e-01
  1.8662186e-01  2.1709421e-01]
Sparsity at: 0.013428782188841202
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1704017e-01
  1.8652424e-01  2.1718226e-01]
Sparsity at: 0.013428782188841202
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1707648e-01
  1.8667175e-01  2.1736464e-01]
Sparsity at: 0.013428782188841202
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8241 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1758030e-01
  1.8700248e-01  2.1759413e-01]
Sparsity at: 0.013428782188841202
Epoch 452/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9042
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1772984e-01
  1.8706839e-01  2.1777911e-01]
Sparsity at: 0.013428782188841202
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8087 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1747126e-01
  1.8684459e-01  2.1774939e-01]
Sparsity at: 0.013428782188841202
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8092 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1711551e-01
  1.8656528e-01  2.1771207e-01]
Sparsity at: 0.013428782188841202
Epoch 455/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9016 - val_loss: 0.8094 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1726687e-01
  1.8659027e-01  2.1774685e-01]
Sparsity at: 0.013428782188841202
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8084 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1754271e-01
  1.8664283e-01  2.1808924e-01]
Sparsity at: 0.013428782188841202
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8091 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1767873e-01
  1.8667029e-01  2.1820119e-01]
Sparsity at: 0.013428782188841202
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8094 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1763179e-01
  1.8657953e-01  2.1794692e-01]
Sparsity at: 0.013428782188841202
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1752886e-01
  1.8664946e-01  2.1757913e-01]
Sparsity at: 0.013428782188841202
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1752773e-01
  1.8638298e-01  2.1811888e-01]
Sparsity at: 0.013428782188841202
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8078 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1822433e-01
  1.8663824e-01  2.1787530e-01]
Sparsity at: 0.013428782188841202
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8082 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1799141e-01
  1.8672135e-01  2.1765782e-01]
Sparsity at: 0.013428782188841202
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8092 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1805352e-01
  1.8695231e-01  2.1779819e-01]
Sparsity at: 0.013428782188841202
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8098 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1760605e-01
  1.8655524e-01  2.1751258e-01]
Sparsity at: 0.013428782188841202
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8090 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1770091e-01
  1.8704477e-01  2.1735442e-01]
Sparsity at: 0.013428782188841202
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1807921e-01
  1.8662696e-01  2.1793881e-01]
Sparsity at: 0.013428782188841202
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8243 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1764575e-01
  1.8654934e-01  2.1735848e-01]
Sparsity at: 0.013428782188841202
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1792762e-01
  1.8670154e-01  2.1753275e-01]
Sparsity at: 0.013428782188841202
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8085 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1772878e-01
  1.8667828e-01  2.1710508e-01]
Sparsity at: 0.013428782188841202
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1742488e-01
  1.8692984e-01  2.1705100e-01]
Sparsity at: 0.013428782188841202
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1808857e-01
  1.8688983e-01  2.1764354e-01]
Sparsity at: 0.013428782188841202
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8242 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1777602e-01
  1.8694788e-01  2.1726358e-01]
Sparsity at: 0.013428782188841202
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9054
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1805546e-01
  1.8654378e-01  2.1789116e-01]
Sparsity at: 0.013428782188841202
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9020 - val_loss: 0.8088 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1793267e-01
  1.8670490e-01  2.1750720e-01]
Sparsity at: 0.013428782188841202
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8094 - val_accuracy: 0.9046
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1758056e-01
  1.8652563e-01  2.1760991e-01]
Sparsity at: 0.013428782188841202
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1766305e-01
  1.8657550e-01  2.1771425e-01]
Sparsity at: 0.013428782188841202
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1758491e-01
  1.8622246e-01  2.1719523e-01]
Sparsity at: 0.013428782188841202
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9021 - val_loss: 0.8086 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1776079e-01
  1.8657561e-01  2.1756572e-01]
Sparsity at: 0.013428782188841202
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8084 - val_accuracy: 0.9049
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1797785e-01
  1.8707438e-01  2.1718381e-01]
Sparsity at: 0.013428782188841202
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9043
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1812248e-01
  1.8692477e-01  2.1726820e-01]
Sparsity at: 0.013428782188841202
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8089 - val_accuracy: 0.9044
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1833940e-01
  1.8714640e-01  2.1762131e-01]
Sparsity at: 0.013428782188841202
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8083 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1787642e-01
  1.8671899e-01  2.1737672e-01]
Sparsity at: 0.013428782188841202
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9020 - val_loss: 0.8091 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1797058e-01
  1.8697110e-01  2.1743509e-01]
Sparsity at: 0.013428782188841202
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8086 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1786638e-01
  1.8683952e-01  2.1755375e-01]
Sparsity at: 0.013428782188841202
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9040
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1835682e-01
  1.8697698e-01  2.1754761e-01]
Sparsity at: 0.013428782188841202
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8085 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1776596e-01
  1.8684800e-01  2.1769130e-01]
Sparsity at: 0.013428782188841202
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9018 - val_loss: 0.8085 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1783997e-01
  1.8682839e-01  2.1784271e-01]
Sparsity at: 0.013428782188841202
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8241 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1824314e-01
  1.8728532e-01  2.1800563e-01]
Sparsity at: 0.013428782188841202
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8090 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1780233e-01
  1.8671966e-01  2.1759672e-01]
Sparsity at: 0.013428782188841202
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8089 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1768540e-01
  1.8665405e-01  2.1762733e-01]
Sparsity at: 0.013428782188841202
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8084 - val_accuracy: 0.9051
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1723703e-01
  1.8621251e-01  2.1688876e-01]
Sparsity at: 0.013428782188841202
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8080 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1782574e-01
  1.8633138e-01  2.1738504e-01]
Sparsity at: 0.013428782188841202
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8243 - accuracy: 0.9019 - val_loss: 0.8084 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1816485e-01
  1.8680918e-01  2.1727230e-01]
Sparsity at: 0.013428782188841202
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8093 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1760714e-01
  1.8667766e-01  2.1769615e-01]
Sparsity at: 0.013428782188841202
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9020 - val_loss: 0.8093 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1754229e-01
  1.8643820e-01  2.1771030e-01]
Sparsity at: 0.013428782188841202
Epoch 496/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1779652e-01
  1.8645643e-01  2.1758531e-01]
Sparsity at: 0.013428782188841202
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9045
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1773438e-01
  1.8657161e-01  2.1738376e-01]
Sparsity at: 0.013428782188841202
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9048
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1786505e-01
  1.8674621e-01  2.1755655e-01]
Sparsity at: 0.013428782188841202
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8084 - val_accuracy: 0.9047
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1714009e-01
  1.8602277e-01  2.1740323e-01]
Sparsity at: 0.013428782188841202
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8083 - val_accuracy: 0.9050
[ 3.1731274e-34  5.2851388e-34 -4.8786629e-34 ... -2.1743812e-01
  1.8628700e-01  2.1714284e-01]
Sparsity at: 0.013428782188841202
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.042179541662335396
Thresholhold -0.05633559077978134
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.08939405530691147
Thresholhold 0.003573372960090637
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10492659732699394
Thresholhold 0.13731814920902252
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 1:00:01 - loss: 2.3651 - accuracy: 0.0430WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_begin` time: 2.5044s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 0.4918 - accuracy: 0.8660 - val_loss: 0.2549 - val_accuracy: 0.9281
[-0.05633559  0.06754186  0.022034   ... -0.26633793 -0.23490816
  0.21950084]
Sparsity at: 0.013428782188841202
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2297 - accuracy: 0.9337 - val_loss: 0.1887 - val_accuracy: 0.9439
[-0.05633559  0.06754186  0.022034   ... -0.28480592 -0.25065205
  0.25275692]
Sparsity at: 0.013428782188841202
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1725 - accuracy: 0.9501 - val_loss: 0.1548 - val_accuracy: 0.9537
[-0.05633559  0.06754186  0.022034   ... -0.29795736 -0.25991264
  0.28055167]
Sparsity at: 0.013428782188841202
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1378 - accuracy: 0.9598 - val_loss: 0.1351 - val_accuracy: 0.9585
[-0.05633559  0.06754186  0.022034   ... -0.3078569  -0.26523432
  0.302555  ]
Sparsity at: 0.013428782188841202
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1140 - accuracy: 0.9670 - val_loss: 0.1227 - val_accuracy: 0.9609
[-0.05633559  0.06754186  0.022034   ... -0.31615216 -0.26896536
  0.32104367]
Sparsity at: 0.013428782188841202
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0963 - accuracy: 0.9719 - val_loss: 0.1135 - val_accuracy: 0.9634
[-0.05633559  0.06754186  0.022034   ... -0.323512   -0.2708599
  0.33693382]
Sparsity at: 0.013428782188841202
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0825 - accuracy: 0.9762 - val_loss: 0.1089 - val_accuracy: 0.9648
[-0.05633559  0.06754186  0.022034   ... -0.33014584 -0.2723221
  0.35208684]
Sparsity at: 0.013428782188841202
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0713 - accuracy: 0.9793 - val_loss: 0.1050 - val_accuracy: 0.9665
[-0.05633559  0.06754186  0.022034   ... -0.33716258 -0.27280957
  0.36541376]
Sparsity at: 0.013428782188841202
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0616 - accuracy: 0.9823 - val_loss: 0.1020 - val_accuracy: 0.9679
[-0.05633559  0.06754186  0.022034   ... -0.343987   -0.27344334
  0.3783662 ]
Sparsity at: 0.013428782188841202
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0536 - accuracy: 0.9846 - val_loss: 0.1005 - val_accuracy: 0.9697
[-0.05633559  0.06754186  0.022034   ... -0.35165486 -0.27456674
  0.391246  ]
Sparsity at: 0.013428782188841202
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0470 - accuracy: 0.9870 - val_loss: 0.0992 - val_accuracy: 0.9699
[-0.05633559  0.06754186  0.022034   ... -0.36039373 -0.2756228
  0.40290472]
Sparsity at: 0.013428782188841202
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0409 - accuracy: 0.9891 - val_loss: 0.1006 - val_accuracy: 0.9697
[-0.05633559  0.06754186  0.022034   ... -0.3685916  -0.2765783
  0.41513744]
Sparsity at: 0.013428782188841202
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0356 - accuracy: 0.9908 - val_loss: 0.1006 - val_accuracy: 0.9704
[-0.05633559  0.06754186  0.022034   ... -0.3777779  -0.27748364
  0.42662534]
Sparsity at: 0.013428782188841202
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0308 - accuracy: 0.9922 - val_loss: 0.1018 - val_accuracy: 0.9705
[-0.05633559  0.06754186  0.022034   ... -0.38673005 -0.2786867
  0.43802193]
Sparsity at: 0.013428782188841202
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0266 - accuracy: 0.9937 - val_loss: 0.1029 - val_accuracy: 0.9715
[-0.05633559  0.06754186  0.022034   ... -0.3944127  -0.27941284
  0.44916144]
Sparsity at: 0.013428782188841202
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0229 - accuracy: 0.9949 - val_loss: 0.1040 - val_accuracy: 0.9717
[-0.05633559  0.06754186  0.022034   ... -0.40205157 -0.28059885
  0.4595701 ]
Sparsity at: 0.013428782188841202
Epoch 17/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0197 - accuracy: 0.9963 - val_loss: 0.1070 - val_accuracy: 0.9708
[-0.05633559  0.06754186  0.022034   ... -0.40841088 -0.28249142
  0.4703986 ]
Sparsity at: 0.013428782188841202
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0170 - accuracy: 0.9970 - val_loss: 0.1088 - val_accuracy: 0.9714
[-0.05633559  0.06754186  0.022034   ... -0.41430452 -0.28465152
  0.4806524 ]
Sparsity at: 0.013428782188841202
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0148 - accuracy: 0.9975 - val_loss: 0.1118 - val_accuracy: 0.9712
[-0.05633559  0.06754186  0.022034   ... -0.42126396 -0.2863751
  0.49031523]
Sparsity at: 0.013428782188841202
Epoch 20/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0130 - accuracy: 0.9982 - val_loss: 0.1142 - val_accuracy: 0.9716
[-0.05633559  0.06754186  0.022034   ... -0.42939818 -0.28862867
  0.49718717]
Sparsity at: 0.013428782188841202
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0115 - accuracy: 0.9984 - val_loss: 0.1154 - val_accuracy: 0.9716
[-0.05633559  0.06754186  0.022034   ... -0.4356343  -0.29041043
  0.5042449 ]
Sparsity at: 0.013428782188841202
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0104 - accuracy: 0.9986 - val_loss: 0.1152 - val_accuracy: 0.9717
[-0.05633559  0.06754186  0.022034   ... -0.44143128 -0.2931453
  0.5105457 ]
Sparsity at: 0.013428782188841202
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0091 - accuracy: 0.9987 - val_loss: 0.1172 - val_accuracy: 0.9727
[-0.05633559  0.06754186  0.022034   ... -0.44674623 -0.29600975
  0.51846385]
Sparsity at: 0.013428782188841202
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9987 - val_loss: 0.1232 - val_accuracy: 0.9712
[-0.05633559  0.06754186  0.022034   ... -0.4536321  -0.3027641
  0.5253391 ]
Sparsity at: 0.013428782188841202
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0084 - accuracy: 0.9983 - val_loss: 0.1257 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -0.4595385  -0.3014701
  0.5326898 ]
Sparsity at: 0.013428782188841202
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0088 - accuracy: 0.9981 - val_loss: 0.1302 - val_accuracy: 0.9724
[-0.05633559  0.06754186  0.022034   ... -0.46224034 -0.29487988
  0.5365355 ]
Sparsity at: 0.013428782188841202
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0088 - accuracy: 0.9981 - val_loss: 0.1404 - val_accuracy: 0.9710
[-0.05633559  0.06754186  0.022034   ... -0.45769608 -0.31198946
  0.54282606]
Sparsity at: 0.013428782188841202
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0081 - accuracy: 0.9979 - val_loss: 0.1570 - val_accuracy: 0.9671
[-0.05633559  0.06754186  0.022034   ... -0.47193122 -0.30742115
  0.5482475 ]
Sparsity at: 0.013428782188841202
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0075 - accuracy: 0.9981 - val_loss: 0.1517 - val_accuracy: 0.9686
[-0.05633559  0.06754186  0.022034   ... -0.476216   -0.3049791
  0.5490274 ]
Sparsity at: 0.013428782188841202
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0082 - accuracy: 0.9978 - val_loss: 0.1489 - val_accuracy: 0.9699
[-0.05633559  0.06754186  0.022034   ... -0.482712   -0.30353972
  0.5520507 ]
Sparsity at: 0.013428782188841202
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9987 - val_loss: 0.1422 - val_accuracy: 0.9714
[-0.05633559  0.06754186  0.022034   ... -0.49313915 -0.29772556
  0.5661293 ]
Sparsity at: 0.013428782188841202
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9992 - val_loss: 0.1360 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -0.49840492 -0.28638247
  0.5671187 ]
Sparsity at: 0.013428782188841202
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0038 - accuracy: 0.9992 - val_loss: 0.1358 - val_accuracy: 0.9741
[-0.05633559  0.06754186  0.022034   ... -0.5041393  -0.29051673
  0.5691587 ]
Sparsity at: 0.013428782188841202
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0035 - accuracy: 0.9994 - val_loss: 0.1409 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -0.5046102  -0.2940186
  0.576729  ]
Sparsity at: 0.013428782188841202
Epoch 35/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1508 - val_accuracy: 0.9722
[-0.05633559  0.06754186  0.022034   ... -0.51392454 -0.29258806
  0.57948154]
Sparsity at: 0.013428782188841202
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 0.9995 - val_loss: 0.1478 - val_accuracy: 0.9717
[-0.05633559  0.06754186  0.022034   ... -0.5150788  -0.2936962
  0.5796223 ]
Sparsity at: 0.013428782188841202
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 0.9997 - val_loss: 0.1457 - val_accuracy: 0.9727
[-0.05633559  0.06754186  0.022034   ... -0.52052563 -0.29689276
  0.59093803]
Sparsity at: 0.013428782188841202
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 0.1645 - val_accuracy: 0.9695
[-0.05633559  0.06754186  0.022034   ... -0.53709114 -0.30214968
  0.5932121 ]
Sparsity at: 0.013428782188841202
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 0.9997 - val_loss: 0.1588 - val_accuracy: 0.9697
[-0.05633559  0.06754186  0.022034   ... -0.5435376  -0.3006364
  0.5994601 ]
Sparsity at: 0.013428782188841202
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 0.1618 - val_accuracy: 0.9715
[-0.05633559  0.06754186  0.022034   ... -0.54564255 -0.2972169
  0.59858155]
Sparsity at: 0.013428782188841202
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 0.9996 - val_loss: 0.1530 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -0.55633557 -0.30488682
  0.6035942 ]
Sparsity at: 0.013428782188841202
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 0.9997 - val_loss: 0.1523 - val_accuracy: 0.9733
[-0.05633559  0.06754186  0.022034   ... -0.5646002  -0.30382395
  0.6063423 ]
Sparsity at: 0.013428782188841202
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1563 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -0.5751787  -0.3047188
  0.6156139 ]
Sparsity at: 0.013428782188841202
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1713 - val_accuracy: 0.9714
[-0.05633559  0.06754186  0.022034   ... -0.5867486  -0.30517986
  0.62387997]
Sparsity at: 0.013428782188841202
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0091 - accuracy: 0.9972 - val_loss: 0.1881 - val_accuracy: 0.9664
[-0.05633559  0.06754186  0.022034   ... -0.5754035  -0.32278758
  0.6232624 ]
Sparsity at: 0.013428782188841202
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0098 - accuracy: 0.9966 - val_loss: 0.1766 - val_accuracy: 0.9705
[-0.05633559  0.06754186  0.022034   ... -0.5978637  -0.30325073
  0.61905575]
Sparsity at: 0.013428782188841202
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.1495 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -0.58456576 -0.3006236
  0.6074177 ]
Sparsity at: 0.013428782188841202
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1513 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -0.59591955 -0.30828127
  0.61861753]
Sparsity at: 0.013428782188841202
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 6.7111e-04 - accuracy: 1.0000 - val_loss: 0.1543 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -0.5971873  -0.30942565
  0.62039953]
Sparsity at: 0.013428782188841202
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 9.9695e-04 - accuracy: 0.9999 - val_loss: 0.1509 - val_accuracy: 0.9748
[-0.05633559  0.06754186  0.022034   ... -0.5989052  -0.31404325
  0.6195683 ]
Sparsity at: 0.013428782188841202
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.1286661979682755
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.21580730399485404
Thresholhold -0.0714910477399826
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.40890553717479605
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 4.0937e-04 - accuracy: 1.0000 - val_loss: 0.1488 - val_accuracy: 0.9756
[-0.05633559  0.06754186  0.022034   ... -0.6010669  -0.31855294
  0.62328684]
Sparsity at: 0.013428782188841202
Epoch 52/500
235/235 [==============================] - 2s 7ms/step - loss: 2.8579e-04 - accuracy: 1.0000 - val_loss: 0.1502 - val_accuracy: 0.9753
[-0.05633559  0.06754186  0.022034   ... -0.6032623  -0.32047427
  0.6256451 ]
Sparsity at: 0.013428782188841202
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3529e-04 - accuracy: 1.0000 - val_loss: 0.1509 - val_accuracy: 0.9755
[-0.05633559  0.06754186  0.022034   ... -0.605843   -0.32192737
  0.62783355]
Sparsity at: 0.013428782188841202
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0416e-04 - accuracy: 1.0000 - val_loss: 0.1515 - val_accuracy: 0.9754
[-0.05633559  0.06754186  0.022034   ... -0.60847706 -0.32350296
  0.6299726 ]
Sparsity at: 0.013428782188841202
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8212e-04 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9757
[-0.05633559  0.06754186  0.022034   ... -0.61114323 -0.3250241
  0.63212246]
Sparsity at: 0.013428782188841202
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6453e-04 - accuracy: 1.0000 - val_loss: 0.1530 - val_accuracy: 0.9760
[-0.05633559  0.06754186  0.022034   ... -0.613936   -0.326587
  0.6343415 ]
Sparsity at: 0.013428782188841202
Epoch 57/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4943e-04 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9759
[-0.05633559  0.06754186  0.022034   ... -0.61676735 -0.32809937
  0.6365775 ]
Sparsity at: 0.013428782188841202
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3627e-04 - accuracy: 1.0000 - val_loss: 0.1547 - val_accuracy: 0.9760
[-0.05633559  0.06754186  0.022034   ... -0.6198347  -0.32967988
  0.6389321 ]
Sparsity at: 0.013428782188841202
Epoch 59/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2460e-04 - accuracy: 1.0000 - val_loss: 0.1557 - val_accuracy: 0.9761
[-0.05633559  0.06754186  0.022034   ... -0.62295926 -0.331299
  0.6413803 ]
Sparsity at: 0.013428782188841202
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1371e-04 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9762
[-0.05633559  0.06754186  0.022034   ... -0.6262433  -0.3329095
  0.64390975]
Sparsity at: 0.013428782188841202
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0420e-04 - accuracy: 1.0000 - val_loss: 0.1576 - val_accuracy: 0.9761
[-0.05633559  0.06754186  0.022034   ... -0.6296892  -0.33456734
  0.6465586 ]
Sparsity at: 0.013428782188841202
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5432e-05 - accuracy: 1.0000 - val_loss: 0.1587 - val_accuracy: 0.9760
[-0.05633559  0.06754186  0.022034   ... -0.63327694 -0.33629557
  0.64936686]
Sparsity at: 0.013428782188841202
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 8.7259e-05 - accuracy: 1.0000 - val_loss: 0.1597 - val_accuracy: 0.9760
[-0.05633559  0.06754186  0.022034   ... -0.6370913  -0.33807105
  0.6522588 ]
Sparsity at: 0.013428782188841202
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9661e-05 - accuracy: 1.0000 - val_loss: 0.1609 - val_accuracy: 0.9760
[-0.05633559  0.06754186  0.022034   ... -0.6411099  -0.3398783
  0.6552557 ]
Sparsity at: 0.013428782188841202
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2771e-05 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9758
[-0.05633559  0.06754186  0.022034   ... -0.6452972  -0.3417332
  0.6583733 ]
Sparsity at: 0.013428782188841202
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6442e-05 - accuracy: 1.0000 - val_loss: 0.1633 - val_accuracy: 0.9757
[-0.05633559  0.06754186  0.022034   ... -0.6496516  -0.34365332
  0.66162926]
Sparsity at: 0.013428782188841202
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0547e-05 - accuracy: 1.0000 - val_loss: 0.1645 - val_accuracy: 0.9757
[-0.05633559  0.06754186  0.022034   ... -0.6540842  -0.34558222
  0.6650179 ]
Sparsity at: 0.013428782188841202
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5055e-05 - accuracy: 1.0000 - val_loss: 0.1658 - val_accuracy: 0.9757
[-0.05633559  0.06754186  0.022034   ... -0.6586902  -0.34760848
  0.66856354]
Sparsity at: 0.013428782188841202
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0006e-05 - accuracy: 1.0000 - val_loss: 0.1672 - val_accuracy: 0.9757
[-0.05633559  0.06754186  0.022034   ... -0.66357964 -0.34971488
  0.67220914]
Sparsity at: 0.013428782188841202
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5444e-05 - accuracy: 1.0000 - val_loss: 0.1686 - val_accuracy: 0.9757
[-0.05633559  0.06754186  0.022034   ... -0.66848075 -0.3518639
  0.67600536]
Sparsity at: 0.013428782188841202
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1138e-05 - accuracy: 1.0000 - val_loss: 0.1700 - val_accuracy: 0.9758
[-0.05633559  0.06754186  0.022034   ... -0.67367166 -0.35408926
  0.6798579 ]
Sparsity at: 0.013428782188841202
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7210e-05 - accuracy: 1.0000 - val_loss: 0.1716 - val_accuracy: 0.9758
[-0.05633559  0.06754186  0.022034   ... -0.67903805 -0.35637048
  0.68387246]
Sparsity at: 0.013428782188841202
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3646e-05 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9756
[-0.05633559  0.06754186  0.022034   ... -0.6844542  -0.35869354
  0.68798506]
Sparsity at: 0.013428782188841202
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0307e-05 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9755
[-0.05633559  0.06754186  0.022034   ... -0.68984157 -0.3610477
  0.6922244 ]
Sparsity at: 0.013428782188841202
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7309e-05 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9756
[-0.05633559  0.06754186  0.022034   ... -0.6955406  -0.36342543
  0.6965442 ]
Sparsity at: 0.013428782188841202
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4544e-05 - accuracy: 1.0000 - val_loss: 0.1777 - val_accuracy: 0.9755
[-0.05633559  0.06754186  0.022034   ... -0.7011287  -0.36583966
  0.70100665]
Sparsity at: 0.013428782188841202
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2093e-05 - accuracy: 1.0000 - val_loss: 0.1793 - val_accuracy: 0.9755
[-0.05633559  0.06754186  0.022034   ... -0.70684737 -0.3683552
  0.7055873 ]
Sparsity at: 0.013428782188841202
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9755e-05 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9755
[-0.05633559  0.06754186  0.022034   ... -0.7127711  -0.37094063
  0.7101944 ]
Sparsity at: 0.013428782188841202
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7712e-05 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9756
[-0.05633559  0.06754186  0.022034   ... -0.7185977  -0.37349585
  0.71492994]
Sparsity at: 0.013428782188841202
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5858e-05 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9757
[-0.05633559  0.06754186  0.022034   ... -0.72451484 -0.37612066
  0.7197477 ]
Sparsity at: 0.013428782188841202
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4169e-05 - accuracy: 1.0000 - val_loss: 0.1861 - val_accuracy: 0.9759
[-0.05633559  0.06754186  0.022034   ... -0.7303979  -0.37876552
  0.7246014 ]
Sparsity at: 0.013428782188841202
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2658e-05 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9759
[-0.05633559  0.06754186  0.022034   ... -0.73659146 -0.38135976
  0.729486  ]
Sparsity at: 0.013428782188841202
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1299e-05 - accuracy: 1.0000 - val_loss: 0.1896 - val_accuracy: 0.9758
[-0.05633559  0.06754186  0.022034   ... -0.7426247  -0.38403293
  0.73450917]
Sparsity at: 0.013428782188841202
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0066e-05 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9757
[-0.05633559  0.06754186  0.022034   ... -0.7486887  -0.3867805
  0.7396179 ]
Sparsity at: 0.013428782188841202
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 8.9523e-06 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9758
[-0.05633559  0.06754186  0.022034   ... -0.75469095 -0.38952965
  0.7447386 ]
Sparsity at: 0.013428782188841202
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9509e-06 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9756
[-0.05633559  0.06754186  0.022034   ... -0.76076746 -0.3922144
  0.74995196]
Sparsity at: 0.013428782188841202
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0915e-06 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9756
[-0.05633559  0.06754186  0.022034   ... -0.76676595 -0.39493862
  0.75508934]
Sparsity at: 0.013428782188841202
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 6.3027e-06 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9755
[-0.05633559  0.06754186  0.022034   ... -0.77314925 -0.3976347
  0.7601814 ]
Sparsity at: 0.013428782188841202
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5944e-06 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9754
[-0.05633559  0.06754186  0.022034   ... -0.77915096 -0.40042806
  0.76541704]
Sparsity at: 0.013428782188841202
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9684e-06 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9754
[-0.05633559  0.06754186  0.022034   ... -0.7852307  -0.4031282
  0.7706772 ]
Sparsity at: 0.013428782188841202
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4070e-06 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9754
[-0.05633559  0.06754186  0.022034   ... -0.79150957 -0.4059412
  0.7758543 ]
Sparsity at: 0.013428782188841202
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9130e-06 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9754
[-0.05633559  0.06754186  0.022034   ... -0.79763323 -0.4086928
  0.7810083 ]
Sparsity at: 0.013428782188841202
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4668e-06 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9751
[-0.05633559  0.06754186  0.022034   ... -0.80354035 -0.4115484
  0.78627086]
Sparsity at: 0.013428782188841202
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0773e-06 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9751
[-0.05633559  0.06754186  0.022034   ... -0.80960137 -0.41435784
  0.79142904]
Sparsity at: 0.013428782188841202
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7323e-06 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9749
[-0.05633559  0.06754186  0.022034   ... -0.8154423  -0.41720292
  0.7967211 ]
Sparsity at: 0.013428782188841202
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4185e-06 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9748
[-0.05633559  0.06754186  0.022034   ... -0.8213672  -0.41989234
  0.80195564]
Sparsity at: 0.013428782188841202
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1458e-06 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9748
[-0.05633559  0.06754186  0.022034   ... -0.8274858  -0.42262283
  0.80710137]
Sparsity at: 0.013428782188841202
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9038e-06 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9748
[-0.05633559  0.06754186  0.022034   ... -0.83351713 -0.4254581
  0.812327  ]
Sparsity at: 0.013428782188841202
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6847e-06 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9748
[-0.05633559  0.06754186  0.022034   ... -0.83943385 -0.42822215
  0.8174839 ]
Sparsity at: 0.013428782188841202
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4968e-06 - accuracy: 1.0000 - val_loss: 0.2209 - val_accuracy: 0.9747
[-0.05633559  0.06754186  0.022034   ... -0.84534377 -0.4308703
  0.82266194]
Sparsity at: 0.013428782188841202
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.18326435435228028
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.29567940505848966
Thresholhold -0.09674298763275146
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.7449820694404892
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 46s 7ms/step - loss: 1.3272e-06 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9746
[-0.05633559  0.06754186  0.022034   ... -0.8511702  -0.4335675
  0.8278099 ]
Sparsity at: 0.013428782188841202
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 1.1788e-06 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.85686666 -0.4363847
  0.83297074]
Sparsity at: 0.013428782188841202
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0478e-06 - accuracy: 1.0000 - val_loss: 0.2263 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.8625784  -0.4390938
  0.83807963]
Sparsity at: 0.013428782188841202
Epoch 104/500
235/235 [==============================] - 2s 10ms/step - loss: 9.2988e-07 - accuracy: 1.0000 - val_loss: 0.2282 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.86812675 -0.44185814
  0.8432559 ]
Sparsity at: 0.013428782188841202
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2586e-07 - accuracy: 1.0000 - val_loss: 0.2299 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.8736012  -0.44462267
  0.848396  ]
Sparsity at: 0.013428782188841202
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3452e-07 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.8791176  -0.44729233
  0.85354346]
Sparsity at: 0.013428782188841202
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5409e-07 - accuracy: 1.0000 - val_loss: 0.2335 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.88473636 -0.44994158
  0.8584795 ]
Sparsity at: 0.013428782188841202
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8219e-07 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 0.9746
[-0.05633559  0.06754186  0.022034   ... -0.89013773 -0.452586
  0.86342025]
Sparsity at: 0.013428782188841202
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1837e-07 - accuracy: 1.0000 - val_loss: 0.2371 - val_accuracy: 0.9747
[-0.05633559  0.06754186  0.022034   ... -0.8953078  -0.45520103
  0.8684237 ]
Sparsity at: 0.013428782188841202
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6226e-07 - accuracy: 1.0000 - val_loss: 0.2389 - val_accuracy: 0.9747
[-0.05633559  0.06754186  0.022034   ... -0.9004565  -0.45784882
  0.8734141 ]
Sparsity at: 0.013428782188841202
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1366e-07 - accuracy: 1.0000 - val_loss: 0.2406 - val_accuracy: 0.9746
[-0.05633559  0.06754186  0.022034   ... -0.90555996 -0.46046188
  0.87833005]
Sparsity at: 0.013428782188841202
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7062e-07 - accuracy: 1.0000 - val_loss: 0.2422 - val_accuracy: 0.9746
[-0.05633559  0.06754186  0.022034   ... -0.91082555 -0.46303982
  0.8830844 ]
Sparsity at: 0.013428782188841202
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3103e-07 - accuracy: 1.0000 - val_loss: 0.2439 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.91591704 -0.4656211
  0.88782793]
Sparsity at: 0.013428782188841202
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9702e-07 - accuracy: 1.0000 - val_loss: 0.2456 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.9208823  -0.4681336
  0.8925893 ]
Sparsity at: 0.013428782188841202
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6636e-07 - accuracy: 1.0000 - val_loss: 0.2473 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.9255703  -0.470668
  0.89729536]
Sparsity at: 0.013428782188841202
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3907e-07 - accuracy: 1.0000 - val_loss: 0.2487 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.9301279  -0.47303513
  0.90193117]
Sparsity at: 0.013428782188841202
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1539e-07 - accuracy: 1.0000 - val_loss: 0.2503 - val_accuracy: 0.9747
[-0.05633559  0.06754186  0.022034   ... -0.9346598  -0.47540334
  0.90658057]
Sparsity at: 0.013428782188841202
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9430e-07 - accuracy: 1.0000 - val_loss: 0.2518 - val_accuracy: 0.9747
[-0.05633559  0.06754186  0.022034   ... -0.9390739  -0.4777911
  0.91107935]
Sparsity at: 0.013428782188841202
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7587e-07 - accuracy: 1.0000 - val_loss: 0.2535 - val_accuracy: 0.9747
[-0.05633559  0.06754186  0.022034   ... -0.9433782  -0.48011795
  0.9155076 ]
Sparsity at: 0.013428782188841202
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5887e-07 - accuracy: 1.0000 - val_loss: 0.2547 - val_accuracy: 0.9746
[-0.05633559  0.06754186  0.022034   ... -0.9478668  -0.4824114
  0.9197812 ]
Sparsity at: 0.013428782188841202
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4414e-07 - accuracy: 1.0000 - val_loss: 0.2561 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.9520277  -0.48462495
  0.92401826]
Sparsity at: 0.013428782188841202
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3084e-07 - accuracy: 1.0000 - val_loss: 0.2575 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.95591325 -0.4868079
  0.92821795]
Sparsity at: 0.013428782188841202
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1937e-07 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 0.9743
[-0.05633559  0.06754186  0.022034   ... -0.95975107 -0.488917
  0.93237764]
Sparsity at: 0.013428782188841202
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0900e-07 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -0.9634477  -0.4909778
  0.9364204 ]
Sparsity at: 0.013428782188841202
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 9.9444e-08 - accuracy: 1.0000 - val_loss: 0.2616 - val_accuracy: 0.9743
[-0.05633559  0.06754186  0.022034   ... -0.96704346 -0.49299693
  0.94034487]
Sparsity at: 0.013428782188841202
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 9.1277e-08 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9743
[-0.05633559  0.06754186  0.022034   ... -0.97047955 -0.49493715
  0.9442157 ]
Sparsity at: 0.013428782188841202
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3808e-08 - accuracy: 1.0000 - val_loss: 0.2640 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.9738342  -0.49684662
  0.9479262 ]
Sparsity at: 0.013428782188841202
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7228e-08 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.9772304  -0.49868393
  0.95153075]
Sparsity at: 0.013428782188841202
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 7.1168e-08 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.98024726 -0.50045395
  0.95502913]
Sparsity at: 0.013428782188841202
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5901e-08 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.98319644 -0.5021759
  0.95848745]
Sparsity at: 0.013428782188841202
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1154e-08 - accuracy: 1.0000 - val_loss: 0.2681 - val_accuracy: 0.9745
[-0.05633559  0.06754186  0.022034   ... -0.98620117 -0.50384986
  0.9617311 ]
Sparsity at: 0.013428782188841202
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6821e-08 - accuracy: 1.0000 - val_loss: 0.2691 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.98905206 -0.5054923
  0.9649589 ]
Sparsity at: 0.013428782188841202
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2849e-08 - accuracy: 1.0000 - val_loss: 0.2702 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.99163055 -0.50703
  0.96807665]
Sparsity at: 0.013428782188841202
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9404e-08 - accuracy: 1.0000 - val_loss: 0.2709 - val_accuracy: 0.9744
[-0.05633559  0.06754186  0.022034   ... -0.9942317  -0.50853425
  0.97106457]
Sparsity at: 0.013428782188841202
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6231e-08 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -0.99658495 -0.5100042
  0.9739724 ]
Sparsity at: 0.013428782188841202
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3348e-08 - accuracy: 1.0000 - val_loss: 0.2727 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -0.9989903  -0.5113921
  0.9767433 ]
Sparsity at: 0.013428782188841202
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0724e-08 - accuracy: 1.0000 - val_loss: 0.2735 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -1.0012252  -0.5127869
  0.979449  ]
Sparsity at: 0.013428782188841202
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8326e-08 - accuracy: 1.0000 - val_loss: 0.2745 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -1.003309   -0.5140634
  0.9820943 ]
Sparsity at: 0.013428782188841202
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6226e-08 - accuracy: 1.0000 - val_loss: 0.2751 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -1.0053384  -0.5153146
  0.9845796 ]
Sparsity at: 0.013428782188841202
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4209e-08 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -1.0072207  -0.5165245
  0.98701525]
Sparsity at: 0.013428782188841202
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2377e-08 - accuracy: 1.0000 - val_loss: 0.2764 - val_accuracy: 0.9742
[-0.05633559  0.06754186  0.022034   ... -1.0090288  -0.5177041
  0.9893772 ]
Sparsity at: 0.013428782188841202
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0796e-08 - accuracy: 1.0000 - val_loss: 0.2771 - val_accuracy: 0.9741
[-0.05633559  0.06754186  0.022034   ... -1.0107769  -0.5188047
  0.99167633]
Sparsity at: 0.013428782188841202
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9405e-08 - accuracy: 1.0000 - val_loss: 0.2779 - val_accuracy: 0.9740
[-0.05633559  0.06754186  0.022034   ... -1.0125543  -0.5198955
  0.99386847]
Sparsity at: 0.013428782188841202
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7907e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.014247   -0.5208662
  0.9960046 ]
Sparsity at: 0.013428782188841202
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6580e-08 - accuracy: 1.0000 - val_loss: 0.2790 - val_accuracy: 0.9739
[-0.05633559  0.06754186  0.022034   ... -1.0158894  -0.52183783
  0.99804133]
Sparsity at: 0.013428782188841202
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5499e-08 - accuracy: 1.0000 - val_loss: 0.2794 - val_accuracy: 0.9740
[-0.05633559  0.06754186  0.022034   ... -1.0174714  -0.5227763
  1.0000162 ]
Sparsity at: 0.013428782188841202
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4406e-08 - accuracy: 1.0000 - val_loss: 0.2801 - val_accuracy: 0.9739
[-0.05633559  0.06754186  0.022034   ... -1.018961   -0.5236896
  1.0019255 ]
Sparsity at: 0.013428782188841202
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3409e-08 - accuracy: 1.0000 - val_loss: 0.2806 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0204213  -0.524557
  1.0037844 ]
Sparsity at: 0.013428782188841202
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2431e-08 - accuracy: 1.0000 - val_loss: 0.2811 - val_accuracy: 0.9739
[-0.05633559  0.06754186  0.022034   ... -1.0217309  -0.525416
  1.005599  ]
Sparsity at: 0.013428782188841202
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1605e-08 - accuracy: 1.0000 - val_loss: 0.2816 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0230007  -0.5262262
  1.007368  ]
Sparsity at: 0.013428782188841202
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.2415795205951845
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.3770834527345741
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 1.0449872193910679
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 2.0742e-08 - accuracy: 1.0000 - val_loss: 0.2822 - val_accuracy: 0.9740
[-0.05633559  0.06754186  0.022034   ... -1.0243046  -0.527005
  1.0090699 ]
Sparsity at: 0.013428782188841202
Epoch 152/500
235/235 [==============================] - 2s 7ms/step - loss: 1.9956e-08 - accuracy: 1.0000 - val_loss: 0.2827 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0255127  -0.5277754
  1.0107232 ]
Sparsity at: 0.013428782188841202
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9221e-08 - accuracy: 1.0000 - val_loss: 0.2833 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0266932  -0.52851945
  1.0123428 ]
Sparsity at: 0.013428782188841202
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8523e-08 - accuracy: 1.0000 - val_loss: 0.2836 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0278345  -0.52921075
  1.0139028 ]
Sparsity at: 0.013428782188841202
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7889e-08 - accuracy: 1.0000 - val_loss: 0.2840 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0289878  -0.52988535
  1.0153589 ]
Sparsity at: 0.013428782188841202
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7293e-08 - accuracy: 1.0000 - val_loss: 0.2844 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0300411  -0.53053784
  1.0167962 ]
Sparsity at: 0.013428782188841202
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6652e-08 - accuracy: 1.0000 - val_loss: 0.2847 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0310479  -0.53115034
  1.018183  ]
Sparsity at: 0.013428782188841202
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6244e-08 - accuracy: 1.0000 - val_loss: 0.2852 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0320712  -0.53175336
  1.0195091 ]
Sparsity at: 0.013428782188841202
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5748e-08 - accuracy: 1.0000 - val_loss: 0.2854 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0330979  -0.53237724
  1.0208342 ]
Sparsity at: 0.013428782188841202
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5261e-08 - accuracy: 1.0000 - val_loss: 0.2858 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0340749  -0.5329366
  1.0221074 ]
Sparsity at: 0.013428782188841202
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4830e-08 - accuracy: 1.0000 - val_loss: 0.2861 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0350227  -0.53349847
  1.0233537 ]
Sparsity at: 0.013428782188841202
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4393e-08 - accuracy: 1.0000 - val_loss: 0.2865 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0359616  -0.5340087
  1.0245754 ]
Sparsity at: 0.013428782188841202
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3999e-08 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0368762  -0.53452396
  1.0257882 ]
Sparsity at: 0.013428782188841202
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3580e-08 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0377088  -0.53502285
  1.026964  ]
Sparsity at: 0.013428782188841202
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3262e-08 - accuracy: 1.0000 - val_loss: 0.2873 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0385438  -0.5354904
  1.0281081 ]
Sparsity at: 0.013428782188841202
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2944e-08 - accuracy: 1.0000 - val_loss: 0.2877 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0393531  -0.5359215
  1.0292581 ]
Sparsity at: 0.013428782188841202
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2644e-08 - accuracy: 1.0000 - val_loss: 0.2880 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0401715  -0.53633404
  1.0303756 ]
Sparsity at: 0.013428782188841202
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2298e-08 - accuracy: 1.0000 - val_loss: 0.2882 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.040934   -0.53675365
  1.0314819 ]
Sparsity at: 0.013428782188841202
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2044e-08 - accuracy: 1.0000 - val_loss: 0.2885 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0416769  -0.53716636
  1.0325567 ]
Sparsity at: 0.013428782188841202
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1762e-08 - accuracy: 1.0000 - val_loss: 0.2887 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0423969  -0.5375251
  1.033626  ]
Sparsity at: 0.013428782188841202
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1426e-08 - accuracy: 1.0000 - val_loss: 0.2890 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0431185  -0.53789175
  1.0346382 ]
Sparsity at: 0.013428782188841202
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1194e-08 - accuracy: 1.0000 - val_loss: 0.2892 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0438416  -0.5382668
  1.0356227 ]
Sparsity at: 0.013428782188841202
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0949e-08 - accuracy: 1.0000 - val_loss: 0.2893 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0445572  -0.53862035
  1.0366026 ]
Sparsity at: 0.013428782188841202
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0653e-08 - accuracy: 1.0000 - val_loss: 0.2896 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0452425  -0.5389753
  1.0375633 ]
Sparsity at: 0.013428782188841202
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0482e-08 - accuracy: 1.0000 - val_loss: 0.2899 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0458951  -0.5393373
  1.0385144 ]
Sparsity at: 0.013428782188841202
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0200e-08 - accuracy: 1.0000 - val_loss: 0.2900 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.046572   -0.53964907
  1.0393994 ]
Sparsity at: 0.013428782188841202
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 9.9818e-09 - accuracy: 1.0000 - val_loss: 0.2902 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0472268  -0.53995013
  1.0402573 ]
Sparsity at: 0.013428782188841202
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7791e-09 - accuracy: 1.0000 - val_loss: 0.2904 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0478694  -0.5402644
  1.0411122 ]
Sparsity at: 0.013428782188841202
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5805e-09 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0485021  -0.5405748
  1.0419587 ]
Sparsity at: 0.013428782188841202
Epoch 180/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3818e-09 - accuracy: 1.0000 - val_loss: 0.2907 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0491046  -0.5408673
  1.0428065 ]
Sparsity at: 0.013428782188841202
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 9.1890e-09 - accuracy: 1.0000 - val_loss: 0.2908 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0496713  -0.541131
  1.0436116 ]
Sparsity at: 0.013428782188841202
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0162e-09 - accuracy: 1.0000 - val_loss: 0.2911 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.050229   -0.5414186
  1.0444313 ]
Sparsity at: 0.013428782188841202
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 8.8195e-09 - accuracy: 1.0000 - val_loss: 0.2912 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0507895  -0.541692
  1.0452209 ]
Sparsity at: 0.013428782188841202
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 8.6427e-09 - accuracy: 1.0000 - val_loss: 0.2914 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0513501  -0.54195094
  1.045973  ]
Sparsity at: 0.013428782188841202
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 8.4798e-09 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0518947  -0.5422173
  1.0467236 ]
Sparsity at: 0.013428782188841202
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3288e-09 - accuracy: 1.0000 - val_loss: 0.2917 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.052405   -0.5424797
  1.0474819 ]
Sparsity at: 0.013428782188841202
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2056e-09 - accuracy: 1.0000 - val_loss: 0.2919 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0529338  -0.5427118
  1.0482135 ]
Sparsity at: 0.013428782188841202
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 8.0466e-09 - accuracy: 1.0000 - val_loss: 0.2919 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0534321  -0.5429559
  1.0489494 ]
Sparsity at: 0.013428782188841202
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9314e-09 - accuracy: 1.0000 - val_loss: 0.2922 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0539299  -0.5431974
  1.0496535 ]
Sparsity at: 0.013428782188841202
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7883e-09 - accuracy: 1.0000 - val_loss: 0.2923 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0544062  -0.54343486
  1.050365  ]
Sparsity at: 0.013428782188841202
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 7.6294e-09 - accuracy: 1.0000 - val_loss: 0.2923 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0548873  -0.54366857
  1.0510466 ]
Sparsity at: 0.013428782188841202
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 7.5201e-09 - accuracy: 1.0000 - val_loss: 0.2925 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.055369   -0.5438792
  1.0517174 ]
Sparsity at: 0.013428782188841202
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 7.4307e-09 - accuracy: 1.0000 - val_loss: 0.2926 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0558525  -0.5441046
  1.0523689 ]
Sparsity at: 0.013428782188841202
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2340e-09 - accuracy: 1.0000 - val_loss: 0.2927 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0563036  -0.5442977
  1.0530139 ]
Sparsity at: 0.013428782188841202
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 7.1565e-09 - accuracy: 1.0000 - val_loss: 0.2927 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0567434  -0.5445168
  1.0536458 ]
Sparsity at: 0.013428782188841202
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0353e-09 - accuracy: 1.0000 - val_loss: 0.2928 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0571518  -0.5447017
  1.054279  ]
Sparsity at: 0.013428782188841202
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 6.9320e-09 - accuracy: 1.0000 - val_loss: 0.2929 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0575655  -0.5449015
  1.0548942 ]
Sparsity at: 0.013428782188841202
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8148e-09 - accuracy: 1.0000 - val_loss: 0.2929 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0579693  -0.54507416
  1.0555091 ]
Sparsity at: 0.013428782188841202
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7671e-09 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0583631  -0.54527676
  1.0561134 ]
Sparsity at: 0.013428782188841202
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6479e-09 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0587392  -0.5454832
  1.0567143 ]
Sparsity at: 0.013428782188841202
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.3022082178638925
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.4496724135634338
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 1.2477209067633623
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 47s 7ms/step - loss: 6.5863e-09 - accuracy: 1.0000 - val_loss: 0.2932 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0591125  -0.5456722
  1.0573184 ]
Sparsity at: 0.013428782188841202
Epoch 202/500
235/235 [==============================] - 2s 7ms/step - loss: 6.4035e-09 - accuracy: 1.0000 - val_loss: 0.2933 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0594854  -0.5458568
  1.0578749 ]
Sparsity at: 0.013428782188841202
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 6.3539e-09 - accuracy: 1.0000 - val_loss: 0.2934 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0598408  -0.54604596
  1.0584519 ]
Sparsity at: 0.013428782188841202
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2664e-09 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0601937  -0.5462027
  1.0589999 ]
Sparsity at: 0.013428782188841202
Epoch 205/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1830e-09 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0605372  -0.54638743
  1.0595446 ]
Sparsity at: 0.013428782188841202
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 6.0896e-09 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0608796  -0.5465484
  1.0600678 ]
Sparsity at: 0.013428782188841202
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0181e-09 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0612164  -0.54671925
  1.0605888 ]
Sparsity at: 0.013428782188841202
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9823e-09 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.061545   -0.54687
  1.0611254 ]
Sparsity at: 0.013428782188841202
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8711e-09 - accuracy: 1.0000 - val_loss: 0.2938 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.061865   -0.5470361
  1.0616374 ]
Sparsity at: 0.013428782188841202
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 5.8115e-09 - accuracy: 1.0000 - val_loss: 0.2939 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0621754  -0.54719454
  1.0621694 ]
Sparsity at: 0.013428782188841202
Epoch 211/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7042e-09 - accuracy: 1.0000 - val_loss: 0.2940 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0624912  -0.5473169
  1.0626872 ]
Sparsity at: 0.013428782188841202
Epoch 212/500
235/235 [==============================] - 2s 9ms/step - loss: 5.6227e-09 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0627964  -0.5474322
  1.0631889 ]
Sparsity at: 0.013428782188841202
Epoch 213/500
235/235 [==============================] - 2s 9ms/step - loss: 5.6406e-09 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0630938  -0.5475777
  1.0636849 ]
Sparsity at: 0.013428782188841202
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5114e-09 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.063376   -0.5477309
  1.0641999 ]
Sparsity at: 0.013428782188841202
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 5.4936e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.063662   -0.5478796
  1.0646768 ]
Sparsity at: 0.013428782188841202
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 5.4200e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0639541  -0.5480004
  1.0651562 ]
Sparsity at: 0.013428782188841202
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3028e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0642537  -0.5481089
  1.0656278 ]
Sparsity at: 0.013428782188841202
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3485e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0645223  -0.5482378
  1.0661006 ]
Sparsity at: 0.013428782188841202
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 5.2174e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0647815  -0.54838353
  1.0665672 ]
Sparsity at: 0.013428782188841202
Epoch 220/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1796e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0650483  -0.5484913
  1.067021  ]
Sparsity at: 0.013428782188841202
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0704e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0652937  -0.54860663
  1.0674517 ]
Sparsity at: 0.013428782188841202
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0366e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0655464  -0.5487248
  1.0679228 ]
Sparsity at: 0.013428782188841202
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9412e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0657823  -0.54883695
  1.0683628 ]
Sparsity at: 0.013428782188841202
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8856e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0660478  -0.5489589
  1.0687714 ]
Sparsity at: 0.013428782188841202
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8836e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.066274   -0.5491078
  1.0692064 ]
Sparsity at: 0.013428782188841202
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7922e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0665255  -0.549216
  1.0696324 ]
Sparsity at: 0.013428782188841202
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7664e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0667574  -0.5493064
  1.0700636 ]
Sparsity at: 0.013428782188841202
Epoch 228/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7108e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.066983   -0.5494307
  1.0704778 ]
Sparsity at: 0.013428782188841202
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6810e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0672128  -0.549562
  1.0708963 ]
Sparsity at: 0.013428782188841202
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6213e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0674496  -0.54969054
  1.071295  ]
Sparsity at: 0.013428782188841202
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5896e-09 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0676813  -0.54981554
  1.0717046 ]
Sparsity at: 0.013428782188841202
Epoch 232/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5141e-09 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.067873   -0.5499374
  1.072103  ]
Sparsity at: 0.013428782188841202
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5617e-09 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0680895  -0.5500682
  1.0725214 ]
Sparsity at: 0.013428782188841202
Epoch 234/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4922e-09 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0682998  -0.55018085
  1.0729235 ]
Sparsity at: 0.013428782188841202
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4207e-09 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0684992  -0.5502716
  1.0732911 ]
Sparsity at: 0.013428782188841202
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3909e-09 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0687017  -0.5503781
  1.073692  ]
Sparsity at: 0.013428782188841202
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3472e-09 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0688787  -0.5504653
  1.0740886 ]
Sparsity at: 0.013428782188841202
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3154e-09 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0690846  -0.5505604
  1.0744824 ]
Sparsity at: 0.013428782188841202
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2737e-09 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.069279   -0.5506633
  1.0748698 ]
Sparsity at: 0.013428782188841202
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2836e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0694746  -0.55077094
  1.075258  ]
Sparsity at: 0.013428782188841202
Epoch 241/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2558e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0696431  -0.550868
  1.0756494 ]
Sparsity at: 0.013428782188841202
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2439e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0698408  -0.55097467
  1.0760163 ]
Sparsity at: 0.013428782188841202
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1803e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0700303  -0.5510774
  1.0763818 ]
Sparsity at: 0.013428782188841202
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2021e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0702239  -0.55118823
  1.0767487 ]
Sparsity at: 0.013428782188841202
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0809e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0704082  -0.5512641
  1.0771127 ]
Sparsity at: 0.013428782188841202
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0889e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.070596   -0.5513523
  1.0774815 ]
Sparsity at: 0.013428782188841202
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0829e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.070788   -0.5514322
  1.0778255 ]
Sparsity at: 0.013428782188841202
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0909e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0709784  -0.55153596
  1.0781844 ]
Sparsity at: 0.013428782188841202
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9697e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.071152   -0.55160785
  1.078555  ]
Sparsity at: 0.013428782188841202
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 3.9836e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0713245  -0.5516715
  1.0789213 ]
Sparsity at: 0.013428782188841202
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.36929382424799684
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.5139876747259535
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 1.417772480245631
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 3.9697e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0715237  -0.5517544
  1.0792676 ]
Sparsity at: 0.013428782188841202
Epoch 252/500
235/235 [==============================] - 2s 7ms/step - loss: 3.9478e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0716921  -0.5518411
  1.0796167 ]
Sparsity at: 0.013428782188841202
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8862e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0718504  -0.5519133
  1.0799482 ]
Sparsity at: 0.013428782188841202
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9180e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0720376  -0.55199444
  1.0802859 ]
Sparsity at: 0.013428782188841202
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8902e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0721992  -0.5520875
  1.0806527 ]
Sparsity at: 0.013428782188841202
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8187e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0723754  -0.5521393
  1.0810019 ]
Sparsity at: 0.013428782188841202
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8167e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0725332  -0.5522161
  1.081314  ]
Sparsity at: 0.013428782188841202
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7789e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.072715   -0.5522853
  1.0816363 ]
Sparsity at: 0.013428782188841202
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8127e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0728868  -0.55231804
  1.0819876 ]
Sparsity at: 0.013428782188841202
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7611e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0730474  -0.5523813
  1.0823241 ]
Sparsity at: 0.013428782188841202
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7372e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0732039  -0.5524437
  1.0826315 ]
Sparsity at: 0.013428782188841202
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7591e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0733681  -0.552534
  1.0829564 ]
Sparsity at: 0.013428782188841202
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6796e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0735127  -0.5525761
  1.0832627 ]
Sparsity at: 0.013428782188841202
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6498e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9738
[-0.05633559  0.06754186  0.022034   ... -1.0736666  -0.55262893
  1.083581  ]
Sparsity at: 0.013428782188841202
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6577e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0738224  -0.5527044
  1.0839045 ]
Sparsity at: 0.013428782188841202
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6339e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0739535  -0.5527631
  1.0842488 ]
Sparsity at: 0.013428782188841202
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5842e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0740936  -0.55282456
  1.0845733 ]
Sparsity at: 0.013428782188841202
Epoch 268/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6339e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0742531  -0.5528916
  1.0848911 ]
Sparsity at: 0.013428782188841202
Epoch 269/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5624e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0744164  -0.5529584
  1.0852196 ]
Sparsity at: 0.013428782188841202
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5683e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0745513  -0.5530021
  1.0855271 ]
Sparsity at: 0.013428782188841202
Epoch 271/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5683e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0746874  -0.55306816
  1.0858365 ]
Sparsity at: 0.013428782188841202
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5663e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0748256  -0.5531154
  1.0861722 ]
Sparsity at: 0.013428782188841202
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5147e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0749557  -0.55318004
  1.0865016 ]
Sparsity at: 0.013428782188841202
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5266e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0750937  -0.55323726
  1.0868301 ]
Sparsity at: 0.013428782188841202
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4750e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0752294  -0.5532787
  1.0871124 ]
Sparsity at: 0.013428782188841202
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4869e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0753773  -0.5533494
  1.0874112 ]
Sparsity at: 0.013428782188841202
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4750e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0755224  -0.55337983
  1.0877173 ]
Sparsity at: 0.013428782188841202
Epoch 278/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4531e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0756483  -0.55341136
  1.0880392 ]
Sparsity at: 0.013428782188841202
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4332e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9737
[-0.05633559  0.06754186  0.022034   ... -1.0758038  -0.55346584
  1.0883397 ]
Sparsity at: 0.013428782188841202
Epoch 280/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4273e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0759407  -0.5535129
  1.088646  ]
Sparsity at: 0.013428782188841202
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4233e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0760624  -0.55355656
  1.0889182 ]
Sparsity at: 0.013428782188841202
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3677e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0761676  -0.55358624
  1.0892183 ]
Sparsity at: 0.013428782188841202
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3736e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0763241  -0.55363023
  1.089523  ]
Sparsity at: 0.013428782188841202
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3736e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.076455   -0.55365705
  1.0898328 ]
Sparsity at: 0.013428782188841202
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3538e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0765811  -0.5537136
  1.0901015 ]
Sparsity at: 0.013428782188841202
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3458e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0767124  -0.5537499
  1.0903846 ]
Sparsity at: 0.013428782188841202
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3538e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0768273  -0.5538018
  1.0906746 ]
Sparsity at: 0.013428782188841202
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3418e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0769676  -0.5538472
  1.090971  ]
Sparsity at: 0.013428782188841202
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2882e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.07711    -0.55388147
  1.0912235 ]
Sparsity at: 0.013428782188841202
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3021e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0772243  -0.5539069
  1.0915099 ]
Sparsity at: 0.013428782188841202
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3339e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0773516  -0.55398506
  1.091781  ]
Sparsity at: 0.013428782188841202
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3041e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0774622  -0.5540205
  1.092057  ]
Sparsity at: 0.013428782188841202
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2643e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0775805  -0.5540608
  1.0923457 ]
Sparsity at: 0.013428782188841202
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2425e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0777196  -0.5541216
  1.09262   ]
Sparsity at: 0.013428782188841202
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2365e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0778157  -0.55413234
  1.0928867 ]
Sparsity at: 0.013428782188841202
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2683e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0779366  -0.5541998
  1.0931971 ]
Sparsity at: 0.013428782188841202
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2266e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0780588  -0.55422485
  1.09347   ]
Sparsity at: 0.013428782188841202
Epoch 298/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2485e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9736
[-0.05633559  0.06754186  0.022034   ... -1.0781823  -0.5542677
  1.09375   ]
Sparsity at: 0.013428782188841202
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2266e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.078305   -0.5543017
  1.0940162 ]
Sparsity at: 0.013428782188841202
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2306e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0784281  -0.5543414
  1.0942988 ]
Sparsity at: 0.013428782188841202
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.43996519941289236
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.5727221716983735
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 1.603471847124041
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 3.2604e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0785398  -0.5543748
  1.0946075 ]
Sparsity at: 0.013428782188841202
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 3.1213e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0786326  -0.5543894
  1.0948521 ]
Sparsity at: 0.013428782188841202
Epoch 303/500
235/235 [==============================] - 2s 7ms/step - loss: 3.1988e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0787642  -0.55439734
  1.0951487 ]
Sparsity at: 0.013428782188841202
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1730e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0788906  -0.55441797
  1.0954133 ]
Sparsity at: 0.013428782188841202
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1730e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0790163  -0.5544899
  1.095691  ]
Sparsity at: 0.013428782188841202
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1332e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.079133   -0.55452365
  1.0959729 ]
Sparsity at: 0.013428782188841202
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1352e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0792505  -0.5545478
  1.0962436 ]
Sparsity at: 0.013428782188841202
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1213e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.079337   -0.5545703
  1.0965333 ]
Sparsity at: 0.013428782188841202
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1610e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0794402  -0.55458635
  1.0968225 ]
Sparsity at: 0.013428782188841202
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1193e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0795479  -0.5546215
  1.0970931 ]
Sparsity at: 0.013428782188841202
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1034e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0796435  -0.55464476
  1.097349  ]
Sparsity at: 0.013428782188841202
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0994e-09 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0797596  -0.5546882
  1.0976152 ]
Sparsity at: 0.013428782188841202
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0617e-09 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0798457  -0.55469376
  1.0979127 ]
Sparsity at: 0.013428782188841202
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0776e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9735
[-0.05633559  0.06754186  0.022034   ... -1.0799402  -0.5547027
  1.0981953 ]
Sparsity at: 0.013428782188841202
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1074e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0800431  -0.55474025
  1.098474  ]
Sparsity at: 0.013428782188841202
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1193e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0801332  -0.5547743
  1.098768  ]
Sparsity at: 0.013428782188841202
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0796e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9733
[-0.05633559  0.06754186  0.022034   ... -1.0802522  -0.5548106
  1.0990688 ]
Sparsity at: 0.013428782188841202
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0239e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0803477  -0.5548069
  1.0993515 ]
Sparsity at: 0.013428782188841202
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0994e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9733
[-0.05633559  0.06754186  0.022034   ... -1.0804354  -0.55481565
  1.0996249 ]
Sparsity at: 0.013428782188841202
Epoch 320/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0279e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9733
[-0.05633559  0.06754186  0.022034   ... -1.0805384  -0.554815
  1.0998961 ]
Sparsity at: 0.013428782188841202
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0915e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0806353  -0.55485636
  1.1001829 ]
Sparsity at: 0.013428782188841202
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0239e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9734
[-0.05633559  0.06754186  0.022034   ... -1.0807254  -0.5548825
  1.1004514 ]
Sparsity at: 0.013428782188841202
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0160e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9733
[-0.05633559  0.06754186  0.022034   ... -1.0808197  -0.55490273
  1.1007334 ]
Sparsity at: 0.013428782188841202
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0319e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9733
[-0.05633559  0.06754186  0.022034   ... -1.0809133  -0.5549055
  1.1010038 ]
Sparsity at: 0.013428782188841202
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9802e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9733
[-0.05633559  0.06754186  0.022034   ... -1.0810157  -0.5549107
  1.1012746 ]
Sparsity at: 0.013428782188841202
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9922e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9733
[-0.05633559  0.06754186  0.022034   ... -1.0811251  -0.5549459
  1.1015509 ]
Sparsity at: 0.013428782188841202
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0537e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0812094  -0.55499494
  1.1018109 ]
Sparsity at: 0.013428782188841202
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0080e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0812945  -0.5550107
  1.1020998 ]
Sparsity at: 0.013428782188841202
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0080e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0813905  -0.55501103
  1.1023529 ]
Sparsity at: 0.013428782188841202
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9484e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0814731  -0.55502987
  1.1026222 ]
Sparsity at: 0.013428782188841202
Epoch 331/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0319e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0815684  -0.5550552
  1.1029053 ]
Sparsity at: 0.013428782188841202
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9743e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0816787  -0.55504996
  1.1031814 ]
Sparsity at: 0.013428782188841202
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9922e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0817572  -0.5550664
  1.1034521 ]
Sparsity at: 0.013428782188841202
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9922e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0818626  -0.55509764
  1.1037171 ]
Sparsity at: 0.013428782188841202
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9743e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0819473  -0.55511206
  1.1039956 ]
Sparsity at: 0.013428782188841202
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9604e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0820236  -0.55511
  1.1042879 ]
Sparsity at: 0.013428782188841202
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9584e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0821093  -0.5551383
  1.1045696 ]
Sparsity at: 0.013428782188841202
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9663e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.082219   -0.5551519
  1.1048255 ]
Sparsity at: 0.013428782188841202
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9385e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0823312  -0.5551563
  1.1050894 ]
Sparsity at: 0.013428782188841202
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9425e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0823916  -0.5551985
  1.105361  ]
Sparsity at: 0.013428782188841202
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9445e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0824841  -0.5552175
  1.1056228 ]
Sparsity at: 0.013428782188841202
Epoch 342/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9147e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0825711  -0.5552028
  1.105901  ]
Sparsity at: 0.013428782188841202
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9325e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0826535  -0.55525047
  1.1061584 ]
Sparsity at: 0.013428782188841202
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9643e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0827233  -0.55525285
  1.1064085 ]
Sparsity at: 0.013428782188841202
Epoch 345/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9524e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0828197  -0.55525935
  1.1066902 ]
Sparsity at: 0.013428782188841202
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8869e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0828975  -0.5552798
  1.1069564 ]
Sparsity at: 0.013428782188841202
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8928e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0829629  -0.55528384
  1.1072185 ]
Sparsity at: 0.013428782188841202
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9345e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0830433  -0.5552933
  1.1074861 ]
Sparsity at: 0.013428782188841202
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8928e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0831338  -0.5553126
  1.1077353 ]
Sparsity at: 0.013428782188841202
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9345e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0832231  -0.5553386
  1.1079985 ]
Sparsity at: 0.013428782188841202
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.5039231081551279
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.6269931421098747
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 1.759873582266323
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 2.8531e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0833069  -0.55533326
  1.108277  ]
Sparsity at: 0.013428782188841202
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 2.9246e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0833925  -0.55534077
  1.1085579 ]
Sparsity at: 0.013428782188841202
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9167e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0834608  -0.55533946
  1.1088241 ]
Sparsity at: 0.013428782188841202
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9266e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.083549   -0.5553865
  1.1090789 ]
Sparsity at: 0.013428782188841202
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8630e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.083604   -0.5554011
  1.109314  ]
Sparsity at: 0.013428782188841202
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8471e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0836761  -0.555417
  1.1095808 ]
Sparsity at: 0.013428782188841202
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8849e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0837684  -0.55543345
  1.1098304 ]
Sparsity at: 0.013428782188841202
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8789e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0838306  -0.55541676
  1.1100824 ]
Sparsity at: 0.013428782188841202
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9027e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0839455  -0.55544156
  1.1103584 ]
Sparsity at: 0.013428782188841202
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8412e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0840185  -0.555456
  1.1105871 ]
Sparsity at: 0.013428782188841202
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8690e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0841138  -0.555458
  1.1108342 ]
Sparsity at: 0.013428782188841202
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0841864  -0.555461
  1.1110994 ]
Sparsity at: 0.013428782188841202
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8749e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0842693  -0.55549437
  1.1113434 ]
Sparsity at: 0.013428782188841202
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8729e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0843704  -0.55552316
  1.11158   ]
Sparsity at: 0.013428782188841202
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8710e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0844591  -0.55550635
  1.1118448 ]
Sparsity at: 0.013428782188841202
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8531e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0845243  -0.5555163
  1.1120701 ]
Sparsity at: 0.013428782188841202
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8292e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0846028  -0.5555445
  1.1123238 ]
Sparsity at: 0.013428782188841202
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8412e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0846797  -0.55554014
  1.1125789 ]
Sparsity at: 0.013428782188841202
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8551e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0847605  -0.55556107
  1.1128119 ]
Sparsity at: 0.013428782188841202
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8392e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0848389  -0.55558616
  1.1130466 ]
Sparsity at: 0.013428782188841202
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8869e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0849096  -0.5556088
  1.1133163 ]
Sparsity at: 0.013428782188841202
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8193e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0850022  -0.5556504
  1.1135339 ]
Sparsity at: 0.013428782188841202
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8511e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0850708  -0.55567974
  1.1137956 ]
Sparsity at: 0.013428782188841202
Epoch 374/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8431e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0851513  -0.5556991
  1.1140441 ]
Sparsity at: 0.013428782188841202
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8272e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0852171  -0.55572456
  1.1142969 ]
Sparsity at: 0.013428782188841202
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8451e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0853025  -0.55574924
  1.114538  ]
Sparsity at: 0.013428782188841202
Epoch 377/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8114e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0853703  -0.5557616
  1.1147838 ]
Sparsity at: 0.013428782188841202
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8412e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0854343  -0.5557939
  1.115038  ]
Sparsity at: 0.013428782188841202
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8213e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0855118  -0.5558097
  1.1152811 ]
Sparsity at: 0.013428782188841202
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8014e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0855944  -0.55581707
  1.1155328 ]
Sparsity at: 0.013428782188841202
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8749e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0856888  -0.5558424
  1.1157781 ]
Sparsity at: 0.013428782188841202
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8074e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0857422  -0.5558689
  1.1160227 ]
Sparsity at: 0.013428782188841202
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7955e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0858175  -0.5558906
  1.1162298 ]
Sparsity at: 0.013428782188841202
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8233e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0858923  -0.5559091
  1.1164931 ]
Sparsity at: 0.013428782188841202
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7875e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0859535  -0.55590755
  1.1167482 ]
Sparsity at: 0.013428782188841202
Epoch 386/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8054e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0860294  -0.55596876
  1.1169885 ]
Sparsity at: 0.013428782188841202
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8054e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0860847  -0.55598265
  1.1172296 ]
Sparsity at: 0.013428782188841202
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0861596  -0.55599785
  1.1174722 ]
Sparsity at: 0.013428782188841202
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7994e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0862279  -0.5560078
  1.1177093 ]
Sparsity at: 0.013428782188841202
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7676e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.086295   -0.55604
  1.117931  ]
Sparsity at: 0.013428782188841202
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8074e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0863781  -0.55605066
  1.1181844 ]
Sparsity at: 0.013428782188841202
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7776e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0864397  -0.556062
  1.1184121 ]
Sparsity at: 0.013428782188841202
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0865049  -0.5560786
  1.1186612 ]
Sparsity at: 0.013428782188841202
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7955e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0865731  -0.55612504
  1.118881  ]
Sparsity at: 0.013428782188841202
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7955e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0866623  -0.5561498
  1.1191235 ]
Sparsity at: 0.013428782188841202
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7974e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0867125  -0.55616033
  1.1193783 ]
Sparsity at: 0.013428782188841202
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7716e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0867658  -0.5561871
  1.1196122 ]
Sparsity at: 0.013428782188841202
Epoch 398/500
235/235 [==============================] - 2s 7ms/step - loss: 2.7299e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0868374  -0.55620074
  1.1198349 ]
Sparsity at: 0.013428782188841202
Epoch 399/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7537e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0869151  -0.55621296
  1.1200838 ]
Sparsity at: 0.013428782188841202
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7815e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.086965   -0.5562297
  1.1202987 ]
Sparsity at: 0.013428782188841202
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.5411585481782311
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.6513711527622945
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.01965332
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 1.8498217601982532
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 44s 7ms/step - loss: 2.7537e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0870285  -0.55624634
  1.1205351 ]
Sparsity at: 0.013428782188841202
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 2.7835e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0871032  -0.5562805
  1.1207824 ]
Sparsity at: 0.013428782188841202
Epoch 403/500
235/235 [==============================] - 2s 7ms/step - loss: 2.7716e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0871662  -0.5562848
  1.1210102 ]
Sparsity at: 0.013428782188841202
Epoch 404/500
235/235 [==============================] - 2s 7ms/step - loss: 2.7875e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0872446  -0.5563179
  1.1212797 ]
Sparsity at: 0.013428782188841202
Epoch 405/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7557e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.087328   -0.55631924
  1.1214939 ]
Sparsity at: 0.013428782188841202
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0874     -0.55635047
  1.1217571 ]
Sparsity at: 0.013428782188841202
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7974e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0874665  -0.5563733
  1.1220063 ]
Sparsity at: 0.013428782188841202
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7279e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0875151  -0.5564063
  1.122216  ]
Sparsity at: 0.013428782188841202
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7637e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0875914  -0.55642843
  1.1224529 ]
Sparsity at: 0.013428782188841202
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7200e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0876075  -0.5564342
  1.1227118 ]
Sparsity at: 0.013428782188841202
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7637e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0876719  -0.5564558
  1.1229247 ]
Sparsity at: 0.013428782188841202
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0877494  -0.5564585
  1.1231625 ]
Sparsity at: 0.013428782188841202
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7835e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0877979  -0.5564677
  1.1233989 ]
Sparsity at: 0.013428782188841202
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0878704  -0.55645484
  1.1236312 ]
Sparsity at: 0.013428782188841202
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7657e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0879478  -0.55649495
  1.1238286 ]
Sparsity at: 0.013428782188841202
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7498e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0880061  -0.55649245
  1.1240873 ]
Sparsity at: 0.013428782188841202
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7259e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0880781  -0.55650824
  1.1242968 ]
Sparsity at: 0.013428782188841202
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7498e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0881425  -0.5565371
  1.1245513 ]
Sparsity at: 0.013428782188841202
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7597e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0882279  -0.55654913
  1.1247876 ]
Sparsity at: 0.013428782188841202
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7537e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0882796  -0.5565858
  1.1250209 ]
Sparsity at: 0.013428782188841202
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6981e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0883331  -0.55660486
  1.125241  ]
Sparsity at: 0.013428782188841202
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7517e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0884043  -0.55660033
  1.1254902 ]
Sparsity at: 0.013428782188841202
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7001e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0884436  -0.55662143
  1.1257184 ]
Sparsity at: 0.013428782188841202
Epoch 424/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7418e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0885072  -0.5566235
  1.1259737 ]
Sparsity at: 0.013428782188841202
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7398e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0885729  -0.55661196
  1.1262157 ]
Sparsity at: 0.013428782188841202
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7418e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0886418  -0.55665547
  1.1264428 ]
Sparsity at: 0.013428782188841202
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7557e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0887078  -0.5566548
  1.1266809 ]
Sparsity at: 0.013428782188841202
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0887749  -0.55664665
  1.1269158 ]
Sparsity at: 0.013428782188841202
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7875e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0888324  -0.556691
  1.1271358 ]
Sparsity at: 0.013428782188841202
Epoch 430/500
235/235 [==============================] - 2s 10ms/step - loss: 2.7001e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0889066  -0.55671346
  1.1273682 ]
Sparsity at: 0.013428782188841202
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.088986   -0.55672926
  1.1276238 ]
Sparsity at: 0.013428782188841202
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7239e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0890634  -0.5567552
  1.1278421 ]
Sparsity at: 0.013428782188841202
Epoch 433/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7378e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0891514  -0.55677915
  1.1280792 ]
Sparsity at: 0.013428782188841202
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7001e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0892091  -0.556811
  1.1283112 ]
Sparsity at: 0.013428782188841202
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0892348  -0.55680865
  1.1285455 ]
Sparsity at: 0.013428782188841202
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7279e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.089298   -0.5568379
  1.1287674 ]
Sparsity at: 0.013428782188841202
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7239e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0893402  -0.5568598
  1.1289958 ]
Sparsity at: 0.013428782188841202
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7160e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0893786  -0.5568779
  1.1292325 ]
Sparsity at: 0.013428782188841202
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7239e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0894212  -0.5568776
  1.1294671 ]
Sparsity at: 0.013428782188841202
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0894994  -0.5568873
  1.1296865 ]
Sparsity at: 0.013428782188841202
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0895692  -0.5569252
  1.1299376 ]
Sparsity at: 0.013428782188841202
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7061e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0896306  -0.55694807
  1.1301652 ]
Sparsity at: 0.013428782188841202
Epoch 443/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0897048  -0.5569563
  1.1303707 ]
Sparsity at: 0.013428782188841202
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6703e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0897541  -0.5569446
  1.1306064 ]
Sparsity at: 0.013428782188841202
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0898013  -0.5569555
  1.1308707 ]
Sparsity at: 0.013428782188841202
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7398e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0898765  -0.5569737
  1.1311173 ]
Sparsity at: 0.013428782188841202
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0899426  -0.5570142
  1.1313438 ]
Sparsity at: 0.013428782188841202
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7239e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0899928  -0.55702025
  1.1315781 ]
Sparsity at: 0.013428782188841202
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0900477  -0.557049
  1.1318076 ]
Sparsity at: 0.013428782188841202
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0901029  -0.55704075
  1.1320393 ]
Sparsity at: 0.013428782188841202
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0901617  -0.5570621
  1.1322806 ]
Sparsity at: 0.013428782188841202
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7061e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.090206   -0.55703294
  1.1325065 ]
Sparsity at: 0.013428782188841202
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7200e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0902579  -0.5570578
  1.1327393 ]
Sparsity at: 0.013428782188841202
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6663e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0903295  -0.557057
  1.1329612 ]
Sparsity at: 0.013428782188841202
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7657e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0903847  -0.557086
  1.1332154 ]
Sparsity at: 0.013428782188841202
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6842e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0904622  -0.55711716
  1.1334356 ]
Sparsity at: 0.013428782188841202
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7398e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0905455  -0.55715126
  1.1336706 ]
Sparsity at: 0.013428782188841202
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6703e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0905725  -0.5571704
  1.1338882 ]
Sparsity at: 0.013428782188841202
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7120e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9727
[-0.05633559  0.06754186  0.022034   ... -1.0906335  -0.5572192
  1.1341232 ]
Sparsity at: 0.013428782188841202
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7120e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0906909  -0.55722684
  1.1343614 ]
Sparsity at: 0.013428782188841202
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0907413  -0.55726063
  1.1345965 ]
Sparsity at: 0.013428782188841202
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6882e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9727
[-0.05633559  0.06754186  0.022034   ... -1.0907769  -0.55726975
  1.1348163 ]
Sparsity at: 0.013428782188841202
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7120e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9727
[-0.05633559  0.06754186  0.022034   ... -1.0908308  -0.5572906
  1.1350617 ]
Sparsity at: 0.013428782188841202
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7001e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0908751  -0.55730855
  1.1353005 ]
Sparsity at: 0.013428782188841202
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6842e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0909166  -0.5573388
  1.1355215 ]
Sparsity at: 0.013428782188841202
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6524e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0909685  -0.55733424
  1.1357626 ]
Sparsity at: 0.013428782188841202
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7200e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0910352  -0.5573588
  1.1359683 ]
Sparsity at: 0.013428782188841202
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6822e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9728
[-0.05633559  0.06754186  0.022034   ... -1.0911063  -0.55739665
  1.1361955 ]
Sparsity at: 0.013428782188841202
Epoch 469/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6643e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0911616  -0.55743957
  1.1364033 ]
Sparsity at: 0.013428782188841202
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6623e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0912232  -0.5574477
  1.1366552 ]
Sparsity at: 0.013428782188841202
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7299e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0912917  -0.5574726
  1.1368909 ]
Sparsity at: 0.013428782188841202
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6604e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0913473  -0.55748147
  1.137124  ]
Sparsity at: 0.013428782188841202
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6743e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0914009  -0.55751425
  1.137374  ]
Sparsity at: 0.013428782188841202
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6842e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0914673  -0.5575143
  1.1376035 ]
Sparsity at: 0.013428782188841202
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6564e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0915253  -0.55752796
  1.1378359 ]
Sparsity at: 0.013428782188841202
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7021e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0915709  -0.55754733
  1.1380644 ]
Sparsity at: 0.013428782188841202
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6544e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0916281  -0.55756
  1.1382915 ]
Sparsity at: 0.013428782188841202
Epoch 478/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0916711  -0.5575664
  1.1385361 ]
Sparsity at: 0.013428782188841202
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6743e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0917081  -0.55761594
  1.1387622 ]
Sparsity at: 0.013428782188841202
Epoch 480/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6902e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.0917737  -0.5575997
  1.1390038 ]
Sparsity at: 0.013428782188841202
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.091804   -0.5576359
  1.1392337 ]
Sparsity at: 0.013428782188841202
Epoch 482/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6425e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0918503  -0.5576337
  1.139458  ]
Sparsity at: 0.013428782188841202
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6822e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.091901   -0.55765975
  1.1396949 ]
Sparsity at: 0.013428782188841202
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6643e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730
[-0.05633559  0.06754186  0.022034   ... -1.091977   -0.5576624
  1.139935  ]
Sparsity at: 0.013428782188841202
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6385e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0920218  -0.55766255
  1.140163  ]
Sparsity at: 0.013428782188841202
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6683e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0920823  -0.5576811
  1.140378  ]
Sparsity at: 0.013428782188841202
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6762e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0921499  -0.5577089
  1.1406192 ]
Sparsity at: 0.013428782188841202
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6643e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0921764  -0.557727
  1.1408468 ]
Sparsity at: 0.013428782188841202
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6266e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0922095  -0.5577249
  1.1410632 ]
Sparsity at: 0.013428782188841202
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6921e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0922575  -0.55775285
  1.1412922 ]
Sparsity at: 0.013428782188841202
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7041e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0922844  -0.5577603
  1.1415203 ]
Sparsity at: 0.013428782188841202
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6484e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0923324  -0.5577629
  1.1417478 ]
Sparsity at: 0.013428782188841202
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6941e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0923764  -0.55779433
  1.1419835 ]
Sparsity at: 0.013428782188841202
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6882e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0924398  -0.5578017
  1.1422155 ]
Sparsity at: 0.013428782188841202
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5988e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0924804  -0.5577908
  1.1424385 ]
Sparsity at: 0.013428782188841202
Epoch 496/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.2980 - val_accuracy: 0.9731
[-0.05633559  0.06754186  0.022034   ... -1.0925244  -0.5578289
  1.1426855 ]
Sparsity at: 0.013428782188841202
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6246e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9729
[-0.05633559  0.06754186  0.022034   ... -1.0925472  -0.5578102
  1.1428955 ]
Sparsity at: 0.013428782188841202
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.092601   -0.5577968
  1.1431425 ]
Sparsity at: 0.013428782188841202
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6405e-09 - accuracy: 1.0000 - val_loss: 0.2980 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0926409  -0.5577978
  1.1433743 ]
Sparsity at: 0.013428782188841202
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7080e-09 - accuracy: 1.0000 - val_loss: 0.2980 - val_accuracy: 0.9732
[-0.05633559  0.06754186  0.022034   ... -1.0927055  -0.55784476
  1.1436137 ]
Sparsity at: 0.013428782188841202
c:\users\amir ahmed\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:2191: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
  warnings.warn('`layer.add_variable` is deprecated and '
Epoch 1/500
WARNING:tensorflow:From c:\users\amir ahmed\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\ops\array_ops.py:5043: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.
Instructions for updating:
The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.
235/235 [==============================] - 6s 14ms/step - loss: 0.1395 - accuracy: 0.9788 - val_loss: 0.2117 - val_accuracy: 0.9600
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1360 - accuracy: 0.9797 - val_loss: 0.2131 - val_accuracy: 0.9610
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9786 - val_loss: 0.1912 - val_accuracy: 0.9654
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9793 - val_loss: 0.1948 - val_accuracy: 0.9637
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9793 - val_loss: 0.2059 - val_accuracy: 0.9607
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9784 - val_loss: 0.1934 - val_accuracy: 0.9648
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1426 - accuracy: 0.9782 - val_loss: 0.2020 - val_accuracy: 0.9616
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9797 - val_loss: 0.1986 - val_accuracy: 0.9619
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9787 - val_loss: 0.1840 - val_accuracy: 0.9666
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9794 - val_loss: 0.1910 - val_accuracy: 0.9645
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9797 - val_loss: 0.1829 - val_accuracy: 0.9676
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9793 - val_loss: 0.1861 - val_accuracy: 0.9662
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9786 - val_loss: 0.1899 - val_accuracy: 0.9648
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9801 - val_loss: 0.1895 - val_accuracy: 0.9648
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9792 - val_loss: 0.1951 - val_accuracy: 0.9632
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9780 - val_loss: 0.1957 - val_accuracy: 0.9639
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9800 - val_loss: 0.1807 - val_accuracy: 0.9669
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9787 - val_loss: 0.2186 - val_accuracy: 0.9575
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9806 - val_loss: 0.1822 - val_accuracy: 0.9686
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1426 - accuracy: 0.9774 - val_loss: 0.1882 - val_accuracy: 0.9660
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9800 - val_loss: 0.2093 - val_accuracy: 0.9601
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9793 - val_loss: 0.2084 - val_accuracy: 0.9624
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9772 - val_loss: 0.1930 - val_accuracy: 0.9640
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9789 - val_loss: 0.1982 - val_accuracy: 0.9613
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9798 - val_loss: 0.1868 - val_accuracy: 0.9634
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9789 - val_loss: 0.2097 - val_accuracy: 0.9572
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1357 - accuracy: 0.9793 - val_loss: 0.1827 - val_accuracy: 0.9687
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9778 - val_loss: 0.2002 - val_accuracy: 0.9623- loss: 0.1408 - accuracy: 0.97 - ETA: 0s - loss: 0.1405 - accura
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9796 - val_loss: 0.2176 - val_accuracy: 0.9554
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9788 - val_loss: 0.1924 - val_accuracy: 0.9648
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9797 - val_loss: 0.2247 - val_accuracy: 0.9528
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9790 - val_loss: 0.2091 - val_accuracy: 0.9607
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9789 - val_loss: 0.2066 - val_accuracy: 0.9606
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1370 - accuracy: 0.9797 - val_loss: 0.2188 - val_accuracy: 0.9558
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9789 - val_loss: 0.1780 - val_accuracy: 0.9706
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9796 - val_loss: 0.1906 - val_accuracy: 0.9650
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.2348 - val_accuracy: 0.9499
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9783 - val_loss: 0.1844 - val_accuracy: 0.9666 accu -
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9789 - val_loss: 0.1793 - val_accuracy: 0.9685
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9804 - val_loss: 0.1958 - val_accuracy: 0.9621
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9782 - val_loss: 0.2201 - val_accuracy: 0.9571
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9784 - val_loss: 0.2110 - val_accuracy: 0.9581
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9795 - val_loss: 0.1959 - val_accuracy: 0.9632
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9803 - val_loss: 0.2057 - val_accuracy: 0.9604
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.1828 - val_accuracy: 0.9661
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9801 - val_loss: 0.1807 - val_accuracy: 0.9677
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9794 - val_loss: 0.2107 - val_accuracy: 0.9599
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9792 - val_loss: 0.2084 - val_accuracy: 0.9570
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9794 - val_loss: 0.2158 - val_accuracy: 0.9596
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9797 - val_loss: 0.2086 - val_accuracy: 0.9608
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9794 - val_loss: 0.2036 - val_accuracy: 0.9610 0s - loss: 0.1373 - accu
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9791 - val_loss: 0.1984 - val_accuracy: 0.9624
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9791 - val_loss: 0.2115 - val_accuracy: 0.9585
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9800 - val_loss: 0.1907 - val_accuracy: 0.9652
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9799 - val_loss: 0.1958 - val_accuracy: 0.9643
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9787 - val_loss: 0.1986 - val_accuracy: 0.9627
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9797 - val_loss: 0.2527 - val_accuracy: 0.9471
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9790 - val_loss: 0.1903 - val_accuracy: 0.9640
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9795 - val_loss: 0.1978 - val_accuracy: 0.9617
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... -0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1336 - accuracy: 0.9798 - val_loss: 0.1920 - val_accuracy: 0.9646
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9777 - val_loss: 0.1814 - val_accuracy: 0.9675
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9796 - val_loss: 0.1976 - val_accuracy: 0.9613
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9790 - val_loss: 0.1981 - val_accuracy: 0.9627
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9802 - val_loss: 0.1986 - val_accuracy: 0.9647
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1357 - accuracy: 0.9796 - val_loss: 0.1862 - val_accuracy: 0.9673
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1343 - accuracy: 0.9796 - val_loss: 0.2100 - val_accuracy: 0.9579
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1352 - accuracy: 0.9791 - val_loss: 0.1949 - val_accuracy: 0.9641
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9798 - val_loss: 0.1796 - val_accuracy: 0.9687
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9785 - val_loss: 0.1900 - val_accuracy: 0.9672
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9792 - val_loss: 0.1971 - val_accuracy: 0.9627
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 12ms/step - loss: 0.1398 - accuracy: 0.9779 - val_loss: 0.1980 - val_accuracy: 0.9617
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1342 - accuracy: 0.9794 - val_loss: 0.1928 - val_accuracy: 0.9643
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9793 - val_loss: 0.2039 - val_accuracy: 0.9602
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9796 - val_loss: 0.1788 - val_accuracy: 0.9669
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9784 - val_loss: 0.2082 - val_accuracy: 0.9607
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9791 - val_loss: 0.2080 - val_accuracy: 0.9589
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2016 - val_accuracy: 0.9635
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9794 - val_loss: 0.2169 - val_accuracy: 0.9556
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9803 - val_loss: 0.1851 - val_accuracy: 0.9658
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9797 - val_loss: 0.1866 - val_accuracy: 0.9655
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9799 - val_loss: 0.2135 - val_accuracy: 0.9584
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1392 - accuracy: 0.9787 - val_loss: 0.1973 - val_accuracy: 0.9647
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9803 - val_loss: 0.2066 - val_accuracy: 0.9596
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9789 - val_loss: 0.1986 - val_accuracy: 0.9620
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9792 - val_loss: 0.2352 - val_accuracy: 0.9531
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9793 - val_loss: 0.1940 - val_accuracy: 0.9635
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9801 - val_loss: 0.2152 - val_accuracy: 0.9580
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9797 - val_loss: 0.1929 - val_accuracy: 0.9622
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9793 - val_loss: 0.1693 - val_accuracy: 0.9704
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9797 - val_loss: 0.1905 - val_accuracy: 0.9640
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9782 - val_loss: 0.1913 - val_accuracy: 0.9648
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... -0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1381 - accuracy: 0.9783 - val_loss: 0.1961 - val_accuracy: 0.9648
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9793 - val_loss: 0.1945 - val_accuracy: 0.9644
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9787 - val_loss: 0.2161 - val_accuracy: 0.9551
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.1824 - val_accuracy: 0.9646
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9789 - val_loss: 0.2057 - val_accuracy: 0.9594
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.2050 - val_accuracy: 0.9591
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9790 - val_loss: 0.1982 - val_accuracy: 0.9648
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9785 - val_loss: 0.1734 - val_accuracy: 0.9704
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9803 - val_loss: 0.1802 - val_accuracy: 0.9666
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9787 - val_loss: 0.2176 - val_accuracy: 0.9531
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1359 - accuracy: 0.9789 - val_loss: 0.1982 - val_accuracy: 0.9609
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1370 - accuracy: 0.9792 - val_loss: 0.2109 - val_accuracy: 0.9597
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9789 - val_loss: 0.2010 - val_accuracy: 0.9617
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1331 - accuracy: 0.9794 - val_loss: 0.2221 - val_accuracy: 0.9542
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9785 - val_loss: 0.2319 - val_accuracy: 0.9522
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9787 - val_loss: 0.2050 - val_accuracy: 0.9587
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9786 - val_loss: 0.2203 - val_accuracy: 0.9591
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9793 - val_loss: 0.1874 - val_accuracy: 0.9650
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9790 - val_loss: 0.2270 - val_accuracy: 0.9538
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9785 - val_loss: 0.2035 - val_accuracy: 0.9604
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9792 - val_loss: 0.2447 - val_accuracy: 0.9484
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9795 - val_loss: 0.1879 - val_accuracy: 0.9642
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9785 - val_loss: 0.1937 - val_accuracy: 0.9631
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9786 - val_loss: 0.2123 - val_accuracy: 0.9597
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9791 - val_loss: 0.1914 - val_accuracy: 0.9661
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9786 - val_loss: 0.1834 - val_accuracy: 0.9682
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9795 - val_loss: 0.1909 - val_accuracy: 0.9638
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9801 - val_loss: 0.1935 - val_accuracy: 0.9668
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9793 - val_loss: 0.1914 - val_accuracy: 0.9627
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9798 - val_loss: 0.1956 - val_accuracy: 0.9634
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9796 - val_loss: 0.2133 - val_accuracy: 0.9581
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2006 - val_accuracy: 0.9617
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9790 - val_loss: 0.1999 - val_accuracy: 0.9642
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9787 - val_loss: 0.1907 - val_accuracy: 0.9652
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9794 - val_loss: 0.2294 - val_accuracy: 0.9543
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9804 - val_loss: 0.1867 - val_accuracy: 0.9662
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9789 - val_loss: 0.1955 - val_accuracy: 0.9637
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9801 - val_loss: 0.1940 - val_accuracy: 0.9640
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9792 - val_loss: 0.2061 - val_accuracy: 0.9582
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9793 - val_loss: 0.2119 - val_accuracy: 0.9590
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9794 - val_loss: 0.2464 - val_accuracy: 0.9508
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9791 - val_loss: 0.2246 - val_accuracy: 0.9551
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.2410 - val_accuracy: 0.9486
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9788 - val_loss: 0.1918 - val_accuracy: 0.9657
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9803 - val_loss: 0.1979 - val_accuracy: 0.9613
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9794 - val_loss: 0.2176 - val_accuracy: 0.9548
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... -0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9797 - val_loss: 0.2055 - val_accuracy: 0.9607
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9782 - val_loss: 0.1974 - val_accuracy: 0.9632
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9797 - val_loss: 0.2122 - val_accuracy: 0.9599
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1336 - accuracy: 0.9793 - val_loss: 0.2145 - val_accuracy: 0.9595
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9792 - val_loss: 0.2237 - val_accuracy: 0.9533
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9790 - val_loss: 0.2160 - val_accuracy: 0.9571
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... -0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9789 - val_loss: 0.1884 - val_accuracy: 0.9647
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9788 - val_loss: 0.2115 - val_accuracy: 0.9576
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1333 - accuracy: 0.9797 - val_loss: 0.1914 - val_accuracy: 0.9650
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1356 - accuracy: 0.9790 - val_loss: 0.1910 - val_accuracy: 0.9655
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9790 - val_loss: 0.1951 - val_accuracy: 0.9625
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9792 - val_loss: 0.2052 - val_accuracy: 0.9583
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9804 - val_loss: 0.1958 - val_accuracy: 0.9650
[-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9785 - val_loss: 0.1883 - val_accuracy: 0.9637
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1314 - accuracy: 0.9805 - val_loss: 0.1924 - val_accuracy: 0.9642
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1380 - accuracy: 0.9779 - val_loss: 0.2056 - val_accuracy: 0.9574
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1365 - accuracy: 0.9787 - val_loss: 0.1927 - val_accuracy: 0.9630
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9781 - val_loss: 0.1766 - val_accuracy: 0.9692
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9792 - val_loss: 0.2212 - val_accuracy: 0.9573
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9795 - val_loss: 0.2238 - val_accuracy: 0.9530
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9787 - val_loss: 0.1859 - val_accuracy: 0.9669
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9787 - val_loss: 0.2065 - val_accuracy: 0.9612
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2061 - val_accuracy: 0.9608
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9799 - val_loss: 0.1973 - val_accuracy: 0.9607
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1360 - accuracy: 0.9782 - val_loss: 0.1880 - val_accuracy: 0.9643
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1362 - accuracy: 0.9789 - val_loss: 0.2178 - val_accuracy: 0.9572
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9791 - val_loss: 0.2087 - val_accuracy: 0.9588
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1348 - accuracy: 0.9787 - val_loss: 0.1915 - val_accuracy: 0.9646
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9776 - val_loss: 0.2013 - val_accuracy: 0.9617
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9791 - val_loss: 0.2132 - val_accuracy: 0.9586
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9785 - val_loss: 0.1865 - val_accuracy: 0.9669
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9793 - val_loss: 0.2138 - val_accuracy: 0.9616
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9794 - val_loss: 0.1889 - val_accuracy: 0.9654
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ... -0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9798 - val_loss: 0.2005 - val_accuracy: 0.9605
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1358 - accuracy: 0.9786 - val_loss: 0.2069 - val_accuracy: 0.9574
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1371 - accuracy: 0.9787 - val_loss: 0.1948 - val_accuracy: 0.9617
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9796 - val_loss: 0.2269 - val_accuracy: 0.9538
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9784 - val_loss: 0.2106 - val_accuracy: 0.9590
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9798 - val_loss: 0.2000 - val_accuracy: 0.9607
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9785 - val_loss: 0.1890 - val_accuracy: 0.9638
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9784 - val_loss: 0.2133 - val_accuracy: 0.9563
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9793 - val_loss: 0.2026 - val_accuracy: 0.9627
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9801 - val_loss: 0.2089 - val_accuracy: 0.9619
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9785 - val_loss: 0.2047 - val_accuracy: 0.9588
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9797 - val_loss: 0.2117 - val_accuracy: 0.9591
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9797 - val_loss: 0.2260 - val_accuracy: 0.9538
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9779 - val_loss: 0.2070 - val_accuracy: 0.9601
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9803 - val_loss: 0.2279 - val_accuracy: 0.9542
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9780 - val_loss: 0.2226 - val_accuracy: 0.9577
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9782 - val_loss: 0.1922 - val_accuracy: 0.9640
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1326 - accuracy: 0.9797 - val_loss: 0.2036 - val_accuracy: 0.9589
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1364 - accuracy: 0.9792 - val_loss: 0.2017 - val_accuracy: 0.9614
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9796 - val_loss: 0.2047 - val_accuracy: 0.9614
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9788 - val_loss: 0.1866 - val_accuracy: 0.9645
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1340 - accuracy: 0.9793 - val_loss: 0.1964 - val_accuracy: 0.9626
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9796 - val_loss: 0.2211 - val_accuracy: 0.9558
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ... -0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9793 - val_loss: 0.2121 - val_accuracy: 0.9584
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9786 - val_loss: 0.2045 - val_accuracy: 0.9600
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9792 - val_loss: 0.2124 - val_accuracy: 0.9551
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9791 - val_loss: 0.2322 - val_accuracy: 0.9515
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9783 - val_loss: 0.1837 - val_accuracy: 0.9663
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1342 - accuracy: 0.9790 - val_loss: 0.2026 - val_accuracy: 0.9628
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9795 - val_loss: 0.2052 - val_accuracy: 0.9593
[-5.4367922e-34 -0.0000000e+00  0.0000000e+00 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9780 - val_loss: 0.1852 - val_accuracy: 0.9659
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9775 - val_loss: 0.1979 - val_accuracy: 0.9615
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9772 - val_loss: 0.2098 - val_accuracy: 0.9595
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9783 - val_loss: 0.1988 - val_accuracy: 0.9611
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9789 - val_loss: 0.1954 - val_accuracy: 0.9630
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9775 - val_loss: 0.1983 - val_accuracy: 0.9632
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1358 - accuracy: 0.9788 - val_loss: 0.2151 - val_accuracy: 0.9571
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9772 - val_loss: 0.1915 - val_accuracy: 0.9641
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9784 - val_loss: 0.1966 - val_accuracy: 0.9635
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.2091 - val_accuracy: 0.9572
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1365 - accuracy: 0.9787 - val_loss: 0.2029 - val_accuracy: 0.9594
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1340 - accuracy: 0.9793 - val_loss: 0.2206 - val_accuracy: 0.9579
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1334 - accuracy: 0.9792 - val_loss: 0.1936 - val_accuracy: 0.9627
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9773 - val_loss: 0.2345 - val_accuracy: 0.9516
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9783 - val_loss: 0.2154 - val_accuracy: 0.9572
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1351 - accuracy: 0.9785 - val_loss: 0.2229 - val_accuracy: 0.9569
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1365 - accuracy: 0.9784 - val_loss: 0.2034 - val_accuracy: 0.9591
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1387 - accuracy: 0.9779 - val_loss: 0.2017 - val_accuracy: 0.9633
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9790 - val_loss: 0.2139 - val_accuracy: 0.9569
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9776 - val_loss: 0.2098 - val_accuracy: 0.9606
[ 0. -0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9789 - val_loss: 0.2290 - val_accuracy: 0.9543
[ 0. -0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1358 - accuracy: 0.9789 - val_loss: 0.2032 - val_accuracy: 0.9616
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9778 - val_loss: 0.2077 - val_accuracy: 0.9612
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1360 - accuracy: 0.9782 - val_loss: 0.2095 - val_accuracy: 0.9612
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9786 - val_loss: 0.1906 - val_accuracy: 0.9654
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9790 - val_loss: 0.2072 - val_accuracy: 0.9598
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9780 - val_loss: 0.2066 - val_accuracy: 0.9618
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9793 - val_loss: 0.1837 - val_accuracy: 0.9665
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1330 - accuracy: 0.9798 - val_loss: 0.2524 - val_accuracy: 0.9484
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9783 - val_loss: 0.1873 - val_accuracy: 0.9650
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1348 - accuracy: 0.9786 - val_loss: 0.2025 - val_accuracy: 0.9642
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9785 - val_loss: 0.2029 - val_accuracy: 0.9613
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9792 - val_loss: 0.2011 - val_accuracy: 0.9597
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1308 - accuracy: 0.9800 - val_loss: 0.1958 - val_accuracy: 0.9635
[ 0. -0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9793 - val_loss: 0.2313 - val_accuracy: 0.9543
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1400 - accuracy: 0.9779 - val_loss: 0.2116 - val_accuracy: 0.9573
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9782 - val_loss: 0.1876 - val_accuracy: 0.9660
[ 0. -0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9796 - val_loss: 0.2094 - val_accuracy: 0.9603
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9783 - val_loss: 0.1939 - val_accuracy: 0.9662
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1360 - accuracy: 0.9783 - val_loss: 0.1901 - val_accuracy: 0.9657
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9790 - val_loss: 0.1977 - val_accuracy: 0.9645
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1354 - accuracy: 0.9788 - val_loss: 0.1994 - val_accuracy: 0.9623
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1324 - accuracy: 0.9799 - val_loss: 0.2134 - val_accuracy: 0.9590
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9774 - val_loss: 0.2258 - val_accuracy: 0.9548
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9779 - val_loss: 0.1901 - val_accuracy: 0.9655
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1345 - accuracy: 0.9790 - val_loss: 0.1946 - val_accuracy: 0.9636
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9776 - val_loss: 0.2170 - val_accuracy: 0.9549:
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.1901 - val_accuracy: 0.9640
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9782 - val_loss: 0.2035 - val_accuracy: 0.9613
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9797 - val_loss: 0.2193 - val_accuracy: 0.9563
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9775 - val_loss: 0.1847 - val_accuracy: 0.9651
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1277 - accuracy: 0.9796 - val_loss: 0.1999 - val_accuracy: 0.9577
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1277 - accuracy: 0.9789 - val_loss: 0.2218 - val_accuracy: 0.9557
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1235 - accuracy: 0.9803 - val_loss: 0.1774 - val_accuracy: 0.9669
[ 0. -0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1254 - accuracy: 0.9787 - val_loss: 0.1919 - val_accuracy: 0.9618
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9804 - val_loss: 0.2154 - val_accuracy: 0.9550
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1221 - accuracy: 0.9804 - val_loss: 0.1873 - val_accuracy: 0.9597
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9793 - val_loss: 0.2011 - val_accuracy: 0.9574
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1229 - accuracy: 0.9803 - val_loss: 0.2032 - val_accuracy: 0.9583
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9811 - val_loss: 0.2152 - val_accuracy: 0.9556
[ 0. -0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1186 - accuracy: 0.9811 - val_loss: 0.1906 - val_accuracy: 0.9606
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1187 - accuracy: 0.9807 - val_loss: 0.1894 - val_accuracy: 0.9613
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1229 - accuracy: 0.9797 - val_loss: 0.1851 - val_accuracy: 0.9630
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9800 - val_loss: 0.1913 - val_accuracy: 0.9602
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9806 - val_loss: 0.2278 - val_accuracy: 0.9527
[ 0. -0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1188 - accuracy: 0.9815 - val_loss: 0.1853 - val_accuracy: 0.9636
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1190 - accuracy: 0.9804 - val_loss: 0.1908 - val_accuracy: 0.9628
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1189 - accuracy: 0.9801 - val_loss: 0.1949 - val_accuracy: 0.9619
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9801 - val_loss: 0.1949 - val_accuracy: 0.9614
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1182 - accuracy: 0.9805 - val_loss: 0.1942 - val_accuracy: 0.9634
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1189 - accuracy: 0.9801 - val_loss: 0.2298 - val_accuracy: 0.9501
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1180 - accuracy: 0.9812 - val_loss: 0.2313 - val_accuracy: 0.9506
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1203 - accuracy: 0.9810 - val_loss: 0.1881 - val_accuracy: 0.9630
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9803 - val_loss: 0.1985 - val_accuracy: 0.9600
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9803 - val_loss: 0.1896 - val_accuracy: 0.9636
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1192 - accuracy: 0.9804 - val_loss: 0.2150 - val_accuracy: 0.9541
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1190 - accuracy: 0.9810 - val_loss: 0.1832 - val_accuracy: 0.9656
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 7s 29ms/step - loss: 0.1183 - accuracy: 0.9810 - val_loss: 0.1807 - val_accuracy: 0.9645
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 5s 21ms/step - loss: 0.1188 - accuracy: 0.9806 - val_loss: 0.2105 - val_accuracy: 0.9558
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 5s 21ms/step - loss: 0.1189 - accuracy: 0.9808 - val_loss: 0.1881 - val_accuracy: 0.9624
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1200 - accuracy: 0.9809 - val_loss: 0.1759 - val_accuracy: 0.9666
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1170 - accuracy: 0.9817 - val_loss: 0.2275 - val_accuracy: 0.9524
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1189 - accuracy: 0.9814 - val_loss: 0.1801 - val_accuracy: 0.9660
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1199 - accuracy: 0.9804 - val_loss: 0.2095 - val_accuracy: 0.9574
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1169 - accuracy: 0.9819 - val_loss: 0.2020 - val_accuracy: 0.9589
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1217 - accuracy: 0.9792 - val_loss: 0.1795 - val_accuracy: 0.9651
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1181 - accuracy: 0.9814 - val_loss: 0.1836 - val_accuracy: 0.9650
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1186 - accuracy: 0.9810 - val_loss: 0.1986 - val_accuracy: 0.9591
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1180 - accuracy: 0.9812 - val_loss: 0.1948 - val_accuracy: 0.9604
[ 0. -0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1186 - accuracy: 0.9808 - val_loss: 0.1863 - val_accuracy: 0.9654
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1165 - accuracy: 0.9819 - val_loss: 0.1930 - val_accuracy: 0.9600
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1183 - accuracy: 0.9813 - val_loss: 0.1916 - val_accuracy: 0.9610
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1180 - accuracy: 0.9813 - val_loss: 0.1982 - val_accuracy: 0.9641
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1177 - accuracy: 0.9808 - val_loss: 0.2137 - val_accuracy: 0.9546
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1234 - accuracy: 0.9799 - val_loss: 0.1797 - val_accuracy: 0.9625
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1180 - accuracy: 0.9808 - val_loss: 0.1727 - val_accuracy: 0.9677
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1167 - accuracy: 0.9814 - val_loss: 0.1881 - val_accuracy: 0.9629
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1192 - accuracy: 0.9803 - val_loss: 0.2400 - val_accuracy: 0.9518
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1208 - accuracy: 0.9806 - val_loss: 0.1957 - val_accuracy: 0.9589
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1187 - accuracy: 0.9812 - val_loss: 0.1970 - val_accuracy: 0.9593
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1361 - accuracy: 0.9745 - val_loss: 0.1714 - val_accuracy: 0.9644
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1151 - accuracy: 0.9802 - val_loss: 0.1611 - val_accuracy: 0.9675
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1166 - accuracy: 0.9793 - val_loss: 0.1845 - val_accuracy: 0.9626
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1119 - accuracy: 0.9802 - val_loss: 0.1708 - val_accuracy: 0.9655
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1127 - accuracy: 0.9799 - val_loss: 0.1631 - val_accuracy: 0.9664
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1106 - accuracy: 0.9808 - val_loss: 0.1658 - val_accuracy: 0.9675
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1075 - accuracy: 0.9813 - val_loss: 0.1610 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1091 - accuracy: 0.9807 - val_loss: 0.1802 - val_accuracy: 0.9647
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1094 - accuracy: 0.9804 - val_loss: 0.1655 - val_accuracy: 0.9660
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1076 - accuracy: 0.9812 - val_loss: 0.1659 - val_accuracy: 0.9656
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1076 - accuracy: 0.9814 - val_loss: 0.1774 - val_accuracy: 0.9631
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1077 - accuracy: 0.9812 - val_loss: 0.1719 - val_accuracy: 0.9633
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1070 - accuracy: 0.9814 - val_loss: 0.1658 - val_accuracy: 0.9651
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1078 - accuracy: 0.9808 - val_loss: 0.1700 - val_accuracy: 0.9663
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1070 - accuracy: 0.9806 - val_loss: 0.1709 - val_accuracy: 0.9653
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1057 - accuracy: 0.9813 - val_loss: 0.1717 - val_accuracy: 0.9649
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1065 - accuracy: 0.9810 - val_loss: 0.1702 - val_accuracy: 0.9647
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1060 - accuracy: 0.9815 - val_loss: 0.1799 - val_accuracy: 0.9646
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1064 - accuracy: 0.9818 - val_loss: 0.1717 - val_accuracy: 0.9650
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1056 - accuracy: 0.9819 - val_loss: 0.1722 - val_accuracy: 0.9672
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1052 - accuracy: 0.9814 - val_loss: 0.1660 - val_accuracy: 0.9665
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1062 - accuracy: 0.9820 - val_loss: 0.1790 - val_accuracy: 0.9618
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1060 - accuracy: 0.9814 - val_loss: 0.1800 - val_accuracy: 0.9637
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1051 - accuracy: 0.9821 - val_loss: 0.1622 - val_accuracy: 0.9682
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1057 - accuracy: 0.9814 - val_loss: 0.1556 - val_accuracy: 0.9710
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1031 - accuracy: 0.9827 - val_loss: 0.1603 - val_accuracy: 0.9692
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1050 - accuracy: 0.9816 - val_loss: 0.1757 - val_accuracy: 0.9646
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1054 - accuracy: 0.9824 - val_loss: 0.1732 - val_accuracy: 0.9641
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1044 - accuracy: 0.9819 - val_loss: 0.1662 - val_accuracy: 0.9662
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1037 - accuracy: 0.9820 - val_loss: 0.1859 - val_accuracy: 0.9640
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1045 - accuracy: 0.9815 - val_loss: 0.1636 - val_accuracy: 0.9690
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1050 - accuracy: 0.9813 - val_loss: 0.1719 - val_accuracy: 0.9663
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1042 - accuracy: 0.9818 - val_loss: 0.1613 - val_accuracy: 0.9681
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1066 - accuracy: 0.9811 - val_loss: 0.1693 - val_accuracy: 0.9642
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1021 - accuracy: 0.9826 - val_loss: 0.1705 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1067 - accuracy: 0.9810 - val_loss: 0.1605 - val_accuracy: 0.9698
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1049 - accuracy: 0.9816 - val_loss: 0.1703 - val_accuracy: 0.9662
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1071 - accuracy: 0.9808 - val_loss: 0.1859 - val_accuracy: 0.9612
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1034 - accuracy: 0.9825 - val_loss: 0.1660 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1051 - accuracy: 0.9812 - val_loss: 0.1553 - val_accuracy: 0.9676
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1049 - accuracy: 0.9815 - val_loss: 0.1585 - val_accuracy: 0.9696
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1042 - accuracy: 0.9819 - val_loss: 0.1756 - val_accuracy: 0.9632
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1043 - accuracy: 0.9811 - val_loss: 0.1685 - val_accuracy: 0.9664
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1040 - accuracy: 0.9815 - val_loss: 0.1616 - val_accuracy: 0.9685
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1047 - accuracy: 0.9813 - val_loss: 0.1663 - val_accuracy: 0.9681
[ 0. -0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1046 - accuracy: 0.9815 - val_loss: 0.1675 - val_accuracy: 0.9666
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1024 - accuracy: 0.9823 - val_loss: 0.1896 - val_accuracy: 0.9602
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1067 - accuracy: 0.9811 - val_loss: 0.1731 - val_accuracy: 0.9615
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1044 - accuracy: 0.9818 - val_loss: 0.1635 - val_accuracy: 0.9671
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1047 - accuracy: 0.9814 - val_loss: 0.1523 - val_accuracy: 0.9710
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1362 - accuracy: 0.9720 - val_loss: 0.1650 - val_accuracy: 0.9638
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1178 - accuracy: 0.9768 - val_loss: 0.1587 - val_accuracy: 0.9653
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1160 - accuracy: 0.9766 - val_loss: 0.1568 - val_accuracy: 0.9657
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1126 - accuracy: 0.9776 - val_loss: 0.1528 - val_accuracy: 0.9671
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1132 - accuracy: 0.9778 - val_loss: 0.1548 - val_accuracy: 0.9663
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1101 - accuracy: 0.9779 - val_loss: 0.1540 - val_accuracy: 0.9646
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1097 - accuracy: 0.9779 - val_loss: 0.1512 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1093 - accuracy: 0.9779 - val_loss: 0.1669 - val_accuracy: 0.9620
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1087 - accuracy: 0.9785 - val_loss: 0.1608 - val_accuracy: 0.9653
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1099 - accuracy: 0.9785 - val_loss: 0.1605 - val_accuracy: 0.9637
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1081 - accuracy: 0.9779 - val_loss: 0.1570 - val_accuracy: 0.9650
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1076 - accuracy: 0.9785 - val_loss: 0.1501 - val_accuracy: 0.9666
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1072 - accuracy: 0.9787 - val_loss: 0.1497 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1062 - accuracy: 0.9788 - val_loss: 0.1515 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1072 - accuracy: 0.9784 - val_loss: 0.1656 - val_accuracy: 0.9627
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1071 - accuracy: 0.9785 - val_loss: 0.1524 - val_accuracy: 0.9663
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1071 - accuracy: 0.9785 - val_loss: 0.1558 - val_accuracy: 0.9672
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1058 - accuracy: 0.9791 - val_loss: 0.1506 - val_accuracy: 0.9670
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1057 - accuracy: 0.9792 - val_loss: 0.1547 - val_accuracy: 0.9652
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1059 - accuracy: 0.9789 - val_loss: 0.1496 - val_accuracy: 0.9677
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1052 - accuracy: 0.9789 - val_loss: 0.1590 - val_accuracy: 0.9658
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1048 - accuracy: 0.9790 - val_loss: 0.1547 - val_accuracy: 0.9675
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1058 - accuracy: 0.9784 - val_loss: 0.1532 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1041 - accuracy: 0.9792 - val_loss: 0.1537 - val_accuracy: 0.9674
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1045 - accuracy: 0.9797 - val_loss: 0.1479 - val_accuracy: 0.9682
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1051 - accuracy: 0.9792 - val_loss: 0.1506 - val_accuracy: 0.9679
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1053 - accuracy: 0.9785 - val_loss: 0.1504 - val_accuracy: 0.9671
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1065 - accuracy: 0.9786 - val_loss: 0.1489 - val_accuracy: 0.9688
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1052 - accuracy: 0.9790 - val_loss: 0.1462 - val_accuracy: 0.9692
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1046 - accuracy: 0.9786 - val_loss: 0.1476 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1048 - accuracy: 0.9786 - val_loss: 0.1531 - val_accuracy: 0.9682
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1050 - accuracy: 0.9786 - val_loss: 0.1535 - val_accuracy: 0.9675
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1059 - accuracy: 0.9785 - val_loss: 0.1501 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1052 - accuracy: 0.9785 - val_loss: 0.1494 - val_accuracy: 0.9681
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1046 - accuracy: 0.9787 - val_loss: 0.1575 - val_accuracy: 0.9643
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1047 - accuracy: 0.9789 - val_loss: 0.1474 - val_accuracy: 0.9689
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1037 - accuracy: 0.9792 - val_loss: 0.1520 - val_accuracy: 0.9682
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1049 - accuracy: 0.9786 - val_loss: 0.1404 - val_accuracy: 0.9689
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1043 - accuracy: 0.9787 - val_loss: 0.1515 - val_accuracy: 0.9671
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1046 - accuracy: 0.9786 - val_loss: 0.1563 - val_accuracy: 0.9672
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1039 - accuracy: 0.9789 - val_loss: 0.1507 - val_accuracy: 0.9672
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1059 - accuracy: 0.9787 - val_loss: 0.1484 - val_accuracy: 0.9685
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1054 - accuracy: 0.9788 - val_loss: 0.1558 - val_accuracy: 0.9648
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1055 - accuracy: 0.9794 - val_loss: 0.1510 - val_accuracy: 0.9680
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1043 - accuracy: 0.9789 - val_loss: 0.1491 - val_accuracy: 0.9674
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1047 - accuracy: 0.9789 - val_loss: 0.1451 - val_accuracy: 0.9698
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1051 - accuracy: 0.9786 - val_loss: 0.1577 - val_accuracy: 0.9672
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1037 - accuracy: 0.9794 - val_loss: 0.1479 - val_accuracy: 0.9676
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1052 - accuracy: 0.9790 - val_loss: 0.1471 - val_accuracy: 0.9692
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1041 - accuracy: 0.9786 - val_loss: 0.1506 - val_accuracy: 0.9679
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1164 - accuracy: 0.9750 - val_loss: 0.1529 - val_accuracy: 0.9665
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1086 - accuracy: 0.9774 - val_loss: 0.1539 - val_accuracy: 0.9667
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1053 - accuracy: 0.9792 - val_loss: 0.1537 - val_accuracy: 0.9660
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1043 - accuracy: 0.9784 - val_loss: 0.1516 - val_accuracy: 0.9667
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1047 - accuracy: 0.9784 - val_loss: 0.1550 - val_accuracy: 0.9648
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1030 - accuracy: 0.9785 - val_loss: 0.1533 - val_accuracy: 0.9667
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1034 - accuracy: 0.9782 - val_loss: 0.1500 - val_accuracy: 0.9665
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1023 - accuracy: 0.9793 - val_loss: 0.1613 - val_accuracy: 0.9641
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1021 - accuracy: 0.9789 - val_loss: 0.1517 - val_accuracy: 0.9663
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1016 - accuracy: 0.9787 - val_loss: 0.1610 - val_accuracy: 0.9639
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1024 - accuracy: 0.9783 - val_loss: 0.1601 - val_accuracy: 0.9643
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1032 - accuracy: 0.9781 - val_loss: 0.1639 - val_accuracy: 0.9650
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1018 - accuracy: 0.9788 - val_loss: 0.1631 - val_accuracy: 0.9634
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9794 - val_loss: 0.1533 - val_accuracy: 0.9657
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1016 - accuracy: 0.9785 - val_loss: 0.1592 - val_accuracy: 0.9644
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1010 - accuracy: 0.9793 - val_loss: 0.1601 - val_accuracy: 0.9637
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1022 - accuracy: 0.9786 - val_loss: 0.1642 - val_accuracy: 0.9637
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1012 - accuracy: 0.9790 - val_loss: 0.1576 - val_accuracy: 0.9649
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1019 - accuracy: 0.9785 - val_loss: 0.1521 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9789 - val_loss: 0.1509 - val_accuracy: 0.9648
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9793 - val_loss: 0.1502 - val_accuracy: 0.9666
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9793 - val_loss: 0.1512 - val_accuracy: 0.9657
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1010 - accuracy: 0.9788 - val_loss: 0.1579 - val_accuracy: 0.9638
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1008 - accuracy: 0.9790 - val_loss: 0.1547 - val_accuracy: 0.9645
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1021 - accuracy: 0.9784 - val_loss: 0.1507 - val_accuracy: 0.9652
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1020 - accuracy: 0.9787 - val_loss: 0.1558 - val_accuracy: 0.9647
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1016 - accuracy: 0.9786 - val_loss: 0.1525 - val_accuracy: 0.9659
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9792 - val_loss: 0.1522 - val_accuracy: 0.9659
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0999 - accuracy: 0.9795 - val_loss: 0.1509 - val_accuracy: 0.9667
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9787 - val_loss: 0.1623 - val_accuracy: 0.9641
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1015 - accuracy: 0.9781 - val_loss: 0.1563 - val_accuracy: 0.9659
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1005 - accuracy: 0.9789 - val_loss: 0.1473 - val_accuracy: 0.9674
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1010 - accuracy: 0.9787 - val_loss: 0.1541 - val_accuracy: 0.9654
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9793 - val_loss: 0.1511 - val_accuracy: 0.9676
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9789 - val_loss: 0.1480 - val_accuracy: 0.9667
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9784 - val_loss: 0.1497 - val_accuracy: 0.9663
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1006 - accuracy: 0.9789 - val_loss: 0.1484 - val_accuracy: 0.9659
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9790 - val_loss: 0.1480 - val_accuracy: 0.9670
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0999 - accuracy: 0.9793 - val_loss: 0.1526 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1014 - accuracy: 0.9789 - val_loss: 0.1486 - val_accuracy: 0.9679
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1010 - accuracy: 0.9790 - val_loss: 0.1526 - val_accuracy: 0.9652
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1009 - accuracy: 0.9786 - val_loss: 0.1555 - val_accuracy: 0.9648
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0995 - accuracy: 0.9790 - val_loss: 0.1494 - val_accuracy: 0.9663
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1001 - accuracy: 0.9793 - val_loss: 0.1487 - val_accuracy: 0.9662
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0999 - accuracy: 0.9787 - val_loss: 0.1516 - val_accuracy: 0.9664
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0993 - accuracy: 0.9794 - val_loss: 0.1587 - val_accuracy: 0.9649
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1005 - accuracy: 0.9787 - val_loss: 0.1540 - val_accuracy: 0.9650
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1000 - accuracy: 0.9789 - val_loss: 0.1491 - val_accuracy: 0.9659
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1004 - accuracy: 0.9789 - val_loss: 0.1492 - val_accuracy: 0.9657
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1012 - accuracy: 0.9787 - val_loss: 0.1671 - val_accuracy: 0.9620
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0997 - accuracy: 0.9792 - val_loss: 0.1531 - val_accuracy: 0.9656
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1012 - accuracy: 0.9783 - val_loss: 0.1519 - val_accuracy: 0.9667
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1007 - accuracy: 0.9789 - val_loss: 0.1538 - val_accuracy: 0.9670
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1011 - accuracy: 0.9785 - val_loss: 0.1655 - val_accuracy: 0.9647
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1013 - accuracy: 0.9786 - val_loss: 0.1494 - val_accuracy: 0.9671
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1009 - accuracy: 0.9786 - val_loss: 0.1535 - val_accuracy: 0.9656
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1002 - accuracy: 0.9789 - val_loss: 0.1498 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0998 - accuracy: 0.9785 - val_loss: 0.1426 - val_accuracy: 0.9674
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0998 - accuracy: 0.9792 - val_loss: 0.1551 - val_accuracy: 0.9656
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1001 - accuracy: 0.9782 - val_loss: 0.1569 - val_accuracy: 0.9645
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1007 - accuracy: 0.9788 - val_loss: 0.1500 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1010 - accuracy: 0.9784 - val_loss: 0.1509 - val_accuracy: 0.9655
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0992 - accuracy: 0.9796 - val_loss: 0.1526 - val_accuracy: 0.9656
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0993 - accuracy: 0.9792 - val_loss: 0.1508 - val_accuracy: 0.9658
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0999 - accuracy: 0.9790 - val_loss: 0.1463 - val_accuracy: 0.9672
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1009 - accuracy: 0.9784 - val_loss: 0.1486 - val_accuracy: 0.9678
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1013 - accuracy: 0.9786 - val_loss: 0.1501 - val_accuracy: 0.9667
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0998 - accuracy: 0.9792 - val_loss: 0.1569 - val_accuracy: 0.9648
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1008 - accuracy: 0.9788 - val_loss: 0.1552 - val_accuracy: 0.9644
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9788 - val_loss: 0.1560 - val_accuracy: 0.9654
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0998 - accuracy: 0.9793 - val_loss: 0.1557 - val_accuracy: 0.9665
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1006 - accuracy: 0.9788 - val_loss: 0.1528 - val_accuracy: 0.9669
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0994 - accuracy: 0.9794 - val_loss: 0.1439 - val_accuracy: 0.9681
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0996 - accuracy: 0.9791 - val_loss: 0.1489 - val_accuracy: 0.9670
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0992 - accuracy: 0.9789 - val_loss: 0.1566 - val_accuracy: 0.9648
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9783 - val_loss: 0.1530 - val_accuracy: 0.9668
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0995 - accuracy: 0.9789 - val_loss: 0.1467 - val_accuracy: 0.9652
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0991 - accuracy: 0.9793 - val_loss: 0.1531 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0987 - accuracy: 0.9797 - val_loss: 0.1595 - val_accuracy: 0.9639
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0995 - accuracy: 0.9794 - val_loss: 0.1506 - val_accuracy: 0.9664
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9792 - val_loss: 0.1562 - val_accuracy: 0.9659
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0986 - accuracy: 0.9796 - val_loss: 0.1509 - val_accuracy: 0.9676
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0985 - accuracy: 0.9801 - val_loss: 0.1523 - val_accuracy: 0.9658
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1003 - accuracy: 0.9787 - val_loss: 0.1433 - val_accuracy: 0.9677
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 4s 17ms/step - loss: 0.0994 - accuracy: 0.9790 - val_loss: 0.1585 - val_accuracy: 0.9650
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0994 - accuracy: 0.9793 - val_loss: 0.1468 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1002 - accuracy: 0.9790 - val_loss: 0.1506 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0992 - accuracy: 0.9793 - val_loss: 0.1556 - val_accuracy: 0.9644
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0995 - accuracy: 0.9790 - val_loss: 0.1580 - val_accuracy: 0.9652
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1003 - accuracy: 0.9780 - val_loss: 0.1479 - val_accuracy: 0.9681
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0991 - accuracy: 0.9786 - val_loss: 0.1511 - val_accuracy: 0.9665
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1004 - accuracy: 0.9788 - val_loss: 0.1567 - val_accuracy: 0.9652
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0997 - accuracy: 0.9786 - val_loss: 0.1592 - val_accuracy: 0.9657
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0993 - accuracy: 0.9790 - val_loss: 0.1451 - val_accuracy: 0.9675
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0987 - accuracy: 0.9793 - val_loss: 0.1539 - val_accuracy: 0.9669
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0986 - accuracy: 0.9793 - val_loss: 0.1507 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1014 - accuracy: 0.9784 - val_loss: 0.1533 - val_accuracy: 0.9661
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1005 - accuracy: 0.9788 - val_loss: 0.1480 - val_accuracy: 0.9665
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0991 - accuracy: 0.9790 - val_loss: 0.1547 - val_accuracy: 0.9651
[ 0. -0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0997 - accuracy: 0.9791 - val_loss: 0.1500 - val_accuracy: 0.9673
[ 0. -0.  0. ...  0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 5s 15ms/step - loss: 5.1974e-04 - accuracy: 0.9999 - val_loss: 0.0854 - val_accuracy: 0.9837
[-0.         0.         0.        ... -0.5530673 -0.         0.       ]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3461e-04 - accuracy: 1.0000 - val_loss: 0.0842 - val_accuracy: 0.9838
[-0.         0.         0.        ... -0.5557222  0.         0.       ]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7866e-05 - accuracy: 1.0000 - val_loss: 0.0853 - val_accuracy: 0.9845
[-0.          0.          0.         ... -0.55256295 -0.
  0.        ]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7650e-05 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9841
[-0.         0.         0.        ... -0.5550569 -0.         0.       ]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 15ms/step - loss: 3.1267e-05 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9843
[-0.         0.         0.        ... -0.5589374  0.         0.       ]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 15ms/step - loss: 2.2912e-05 - accuracy: 1.0000 - val_loss: 0.0861 - val_accuracy: 0.9843
[-0.          0.          0.         ... -0.55955464  0.
  0.        ]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 15ms/step - loss: 2.6023e-05 - accuracy: 1.0000 - val_loss: 0.0858 - val_accuracy: 0.9846
[-0.          0.          0.         ... -0.56160337 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5238e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9840
[-0.         0.         0.        ... -0.5624639 -0.         0.       ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1368e-04 - accuracy: 0.9999 - val_loss: 0.0924 - val_accuracy: 0.9830
[-0.        0.        0.       ... -0.566357 -0.        0.      ]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 4s 15ms/step - loss: 5.9115e-04 - accuracy: 0.9999 - val_loss: 0.0912 - val_accuracy: 0.9831
[-0.         0.         0.        ... -0.5548585  0.         0.       ]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 15ms/step - loss: 1.8525e-04 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9842
[-0.         0.         0.        ... -0.5577729 -0.         0.       ]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 4s 15ms/step - loss: 1.2232e-04 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9844
[-0.         0.         0.        ... -0.5702418 -0.        -0.       ]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 4s 15ms/step - loss: 2.1000e-04 - accuracy: 0.9999 - val_loss: 0.0877 - val_accuracy: 0.9850
[-0.        0.        0.       ... -0.573148  0.        0.      ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 15ms/step - loss: 3.5954e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9848
[-0.         0.         0.        ... -0.5758063  0.         0.       ]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 15ms/step - loss: 1.8773e-05 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9847
[-0.        0.        0.       ... -0.576613  0.        0.      ]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 15ms/step - loss: 2.2774e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9845
[-0.          0.          0.         ... -0.57610166  0.
  0.        ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3874e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9847
[-0.         0.         0.        ... -0.5776166  0.         0.       ]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0020e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9845
[-0.          0.          0.         ... -0.57820547 -0.
  0.        ]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 15ms/step - loss: 9.4196e-06 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9849
[-0.         0.         0.        ... -0.5780573  0.        -0.       ]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 15ms/step - loss: 8.8427e-06 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9849
[-0.          0.          0.         ... -0.57824266  0.
  0.        ]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 15ms/step - loss: 6.8982e-06 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9851
[-0.          0.          0.         ... -0.57917047  0.
 -0.        ]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 15ms/step - loss: 6.5843e-06 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9851
[-0.         0.         0.        ... -0.5796964  0.        -0.       ]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 15ms/step - loss: 6.1186e-06 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9853
[-0.         0.         0.        ... -0.5801737  0.        -0.       ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6667e-06 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9850
[-0.          0.          0.         ... -0.58366317 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2026e-06 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9848
[-0.         0.         0.        ... -0.5863896  0.         0.       ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1699e-06 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9851
[-0.          0.          0.         ... -0.59061885  0.
 -0.        ]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 6.8785e-06 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9846
[-0.         0.         0.        ... -0.5899389  0.         0.       ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8006e-04 - accuracy: 0.9998 - val_loss: 0.1048 - val_accuracy: 0.9825
[-0.        0.        0.       ... -0.596239  0.       -0.      ]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0994 - val_accuracy: 0.9826
[-0.         0.         0.        ... -0.6218895  0.        -0.       ]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 15ms/step - loss: 5.5281e-04 - accuracy: 0.9998 - val_loss: 0.1035 - val_accuracy: 0.9829
[-0.         0.         0.        ... -0.6150985  0.        -0.       ]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 15ms/step - loss: 2.0528e-04 - accuracy: 0.9999 - val_loss: 0.1016 - val_accuracy: 0.9834
[-0.          0.          0.         ... -0.61611503  0.
 -0.        ]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7312e-05 - accuracy: 1.0000 - val_loss: 0.0994 - val_accuracy: 0.9848
[-0.          0.          0.         ... -0.61966676  0.
 -0.        ]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5500e-05 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9845
[-0.          0.          0.         ... -0.61863166  0.
 -0.        ]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5313e-05 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9844
[-0.          0.          0.         ... -0.61932284  0.
 -0.        ]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1399e-05 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9838
[-0.        0.        0.       ... -0.620924 -0.        0.      ]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9560e-04 - accuracy: 0.9999 - val_loss: 0.0949 - val_accuracy: 0.9841
[-0.          0.          0.         ... -0.59334975  0.
  0.        ]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6057e-05 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9856
[-0.        0.        0.       ... -0.594901  0.        0.      ]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7859e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9857
[-0.        0.        0.       ... -0.596771  0.        0.      ]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2497e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9856
[-0.        0.        0.       ... -0.597793  0.       -0.      ]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0424e-05 - accuracy: 1.0000 - val_loss: 0.0913 - val_accuracy: 0.9855
[-0.        0.        0.       ... -0.599311  0.       -0.      ]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0230e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9849
[-0.         0.         0.        ... -0.6023537  0.        -0.       ]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0588e-04 - accuracy: 1.0000 - val_loss: 0.0993 - val_accuracy: 0.9839
[-0.         0.         0.        ... -0.6026897  0.         0.       ]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0518e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9853
[-0.         0.         0.        ... -0.6037611  0.         0.       ]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6980e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9857
[-0.         0.         0.        ... -0.6053772  0.        -0.       ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 15ms/step - loss: 8.5979e-06 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9855
[-0.         0.         0.        ... -0.6063888  0.         0.       ]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4573e-04 - accuracy: 0.9999 - val_loss: 0.1013 - val_accuracy: 0.9832
[-0.          0.          0.         ... -0.60614705 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 15ms/step - loss: 7.2224e-04 - accuracy: 0.9998 - val_loss: 0.1034 - val_accuracy: 0.9844
[-0.         0.         0.        ... -0.6232613  0.        -0.       ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 15ms/step - loss: 3.1091e-04 - accuracy: 0.9999 - val_loss: 0.0968 - val_accuracy: 0.9843
[-0.         0.         0.        ... -0.6366796 -0.        -0.       ]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 15ms/step - loss: 2.2341e-04 - accuracy: 0.9999 - val_loss: 0.1036 - val_accuracy: 0.9846
[-0.         0.         0.        ... -0.6416714  0.        -0.       ]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6020e-05 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9848
[-0.          0.          0.         ... -0.64035046 -0.
  0.        ]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0037 - accuracy: 0.9987 - val_loss: 0.0997 - val_accuracy: 0.9821
[-0.          0.          0.         ... -0.67721903 -0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 15ms/step - loss: 6.5882e-04 - accuracy: 0.9998 - val_loss: 0.0936 - val_accuracy: 0.9828
[-0.         0.         0.        ... -0.6792987  0.        -0.       ]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1968e-04 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9835
[-0.         0.         0.        ... -0.6801244  0.        -0.       ]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3523e-04 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9840
[-0.          0.          0.         ... -0.68185675 -0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0870e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9840
[-0.         0.         0.        ... -0.6818952  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 13ms/step - loss: 5.9041e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9842
[-0.          0.          0.         ... -0.67859495 -0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 15ms/step - loss: 5.1245e-05 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9843
[-0.          0.          0.         ... -0.68226415 -0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9081e-05 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9842
[-0.         0.         0.        ... -0.6848408  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4416e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9841
[-0.         0.         0.        ... -0.6854666  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2600e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9844
[-0.         0.         0.        ... -0.6891201  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5683e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9841
[-0.         0.         0.        ... -0.6894046  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0525e-05 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9845
[-0.          0.          0.         ... -0.68635917  0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0031e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9845
[-0.         0.         0.        ... -0.6868398  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 15ms/step - loss: 2.8269e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9842
[-0.         0.         0.        ... -0.6877827  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4622e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9844
[-0.          0.          0.         ... -0.68857604 -0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2785e-04 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9842
[-0.         0.         0.        ... -0.6875967 -0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3307e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9846
[-0.          0.          0.         ... -0.68757784 -0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 4s 15ms/step - loss: 3.8052e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9846
[-0.         0.         0.        ... -0.6912779  0.        -0.       ]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5671e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9839
[-0.         0.         0.        ... -0.6941038 -0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5500e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9843
[-0.          0.          0.         ... -0.69580317  0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5026e-05 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9840
[-0.          0.          0.         ... -0.69657964  0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1558e-05 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9839
[-0.         0.         0.        ... -0.6998479  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0486e-05 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9839
[-0.         0.         0.        ... -0.7010317 -0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3231e-05 - accuracy: 1.0000 - val_loss: 0.0928 - val_accuracy: 0.9839
[-0.         0.         0.        ... -0.7006972  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 15ms/step - loss: 2.3321e-05 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9841
[-0.          0.          0.         ... -0.70243347 -0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2470e-05 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9840
[-0.         0.         0.        ... -0.7050213  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7761e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9841
[-0.         0.         0.        ... -0.6966571 -0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 15ms/step - loss: 8.4831e-06 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9838
[-0.         0.         0.        ... -0.6981817  0.        -0.       ]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 15ms/step - loss: 6.8140e-06 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9838
[-0.          0.          0.         ... -0.70088047  0.
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 15ms/step - loss: 7.0180e-06 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9841
[-0.          0.          0.         ... -0.70243925  0.
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 15ms/step - loss: 5.3528e-06 - accuracy: 1.0000 - val_loss: 0.0966 - val_accuracy: 0.9840
[-0.          0.          0.         ... -0.70277315  0.
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 15ms/step - loss: 4.6254e-06 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9843
[-0.          0.          0.         ... -0.70333785  0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 15ms/step - loss: 4.0172e-06 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9840
[-0.         0.         0.        ... -0.7046567  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8390e-06 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9839
[-0.          0.          0.         ... -0.70542276  0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0155e-06 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9842
[-0.         0.         0.        ... -0.7067089  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7225e-06 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9839
[-0.         0.         0.        ... -0.7080183  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7384e-06 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9841
[-0.         0.         0.        ... -0.7082352  0.        -0.       ]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 15ms/step - loss: 2.3948e-06 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 0.9840
[-0.          0.          0.         ... -0.71089333 -0.
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 4s 17ms/step - loss: 2.6843e-06 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9840
[-0.         0.         0.        ... -0.7126137  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 4s 16ms/step - loss: 2.2646e-06 - accuracy: 1.0000 - val_loss: 0.1021 - val_accuracy: 0.9841
[-0.         0.         0.        ... -0.7171528  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 15ms/step - loss: 4.2773e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9838
[-0.         0.         0.        ... -0.7182911 -0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3908e-04 - accuracy: 0.9999 - val_loss: 0.1217 - val_accuracy: 0.9824
[-0.          0.          0.         ... -0.69221926 -0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 0.1233 - val_accuracy: 0.9816
[-0.          0.          0.         ... -0.69016886 -0.
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6124e-04 - accuracy: 0.9999 - val_loss: 0.1117 - val_accuracy: 0.9831
[-0.         0.         0.        ... -0.6866151 -0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3847e-05 - accuracy: 1.0000 - val_loss: 0.1112 - val_accuracy: 0.9832
[-0.         0.         0.        ... -0.6937081  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8299e-04 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9836
[-0.         0.         0.        ... -0.6930453  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8104e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9837
[-0.          0.          0.         ... -0.69321835  0.
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6209e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9837
[-0.         0.         0.        ... -0.6935785  0.         0.       ]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 4s 15ms/step - loss: 7.9636e-06 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9839
[-0.         0.         0.        ... -0.6936766  0.        -0.       ]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1896e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9840
[-0.          0.          0.         ... -0.69357926  0.
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0112 - accuracy: 0.9967 - val_loss: 0.1107 - val_accuracy: 0.9801
[-0.         0.         0.        ... -0.7407761 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1084 - val_accuracy: 0.9820
[-0.         0.         0.        ... -0.7365223 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8578e-04 - accuracy: 0.9999 - val_loss: 0.1059 - val_accuracy: 0.9818
[-0.        0.        0.       ... -0.735703 -0.       -0.      ]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4773e-04 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9817
[-0.          0.          0.         ... -0.73411673 -0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0118e-04 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9819
[-0.          0.          0.         ... -0.73429626 -0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 4s 15ms/step - loss: 1.6714e-04 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9824
[-0.          0.          0.         ... -0.73607904 -0.
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 12ms/step - loss: 1.6384e-04 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9819
[-0.         0.         0.        ... -0.7380855 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2587e-04 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9819
[-0.          0.          0.         ... -0.73282015 -0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2742e-04 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9821
[-0.         0.         0.        ... -0.7321297 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 9.9209e-05 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9818
[-0.         0.         0.        ... -0.7345622 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1317e-04 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9823
[-0.         0.         0.        ... -0.7313788 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 15ms/step - loss: 9.8276e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9822
[-0.        0.        0.       ... -0.736128 -0.        0.      ]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 4s 15ms/step - loss: 6.5150e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9823
[-0.          0.          0.         ... -0.73811364 -0.
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 15ms/step - loss: 6.3891e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9822
[-0.         0.         0.        ... -0.7404297 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 4s 15ms/step - loss: 5.6796e-05 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9823
[-0.         0.         0.        ... -0.7394241 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 15ms/step - loss: 4.5055e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9824
[-0.          0.          0.         ... -0.73982906  0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 15ms/step - loss: 4.0964e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9826
[-0.         0.         0.        ... -0.7416686 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 15ms/step - loss: 4.2198e-05 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9826
[-0.          0.          0.         ... -0.74385947 -0.
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 15ms/step - loss: 5.9155e-05 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9826
[-0.          0.          0.         ... -0.74229836  0.
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 15ms/step - loss: 3.7107e-05 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9827
[-0.         0.         0.        ... -0.7412203  0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 15ms/step - loss: 4.2197e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9832
[-0.          0.          0.         ... -0.74157673 -0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 15ms/step - loss: 3.5201e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9830
[-0.          0.          0.         ... -0.74494153 -0.
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9503e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9829
[-0.          0.          0.         ... -0.74752736 -0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0816e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9827
[-0.         0.         0.        ... -0.7458942 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7131e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9825
[-0.          0.          0.         ... -0.74778545 -0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9934e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9830
[-0.         0.         0.        ... -0.7491825 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6654e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9831
[-0.         0.         0.        ... -0.7542403 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4967e-05 - accuracy: 1.0000 - val_loss: 0.1095 - val_accuracy: 0.9830
[-0.         0.         0.        ... -0.7554976 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4305e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9834
[-0.         0.         0.        ... -0.7567567  0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 15ms/step - loss: 8.9592e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9827
[-0.          0.          0.         ... -0.75846106 -0.
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2192e-04 - accuracy: 0.9999 - val_loss: 0.1172 - val_accuracy: 0.9822
[-0.         0.         0.        ... -0.7628433 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0997e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9828
[-0.          0.          0.         ... -0.79670596  0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8630e-05 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9828
[-0.         0.         0.        ... -0.7897121 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0365e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9830
[-0.         0.         0.        ... -0.7965117 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4366e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9830
[-0.         0.         0.        ... -0.8002411 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2616e-05 - accuracy: 1.0000 - val_loss: 0.1143 - val_accuracy: 0.9826
[-0.         0.         0.        ... -0.8029306  0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1815e-05 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9835
[-0.          0.          0.         ... -0.80553263 -0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8557e-05 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9833
[-0.         0.         0.        ... -0.8039424  0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6699e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9831
[-0.         0.         0.        ... -0.8008044 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2167e-05 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9836
[-0.         0.         0.        ... -0.8060011 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3413e-05 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9833
[-0.         0.         0.        ... -0.8096799  0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 15ms/step - loss: 5.2246e-05 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9830
[-0.         0.         0.        ... -0.8090754 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3822e-05 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9833
[-0.         0.         0.        ... -0.8169751 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 15ms/step - loss: 8.3490e-06 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9830
[-0.         0.         0.        ... -0.8187563 -0.         0.       ]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0631e-05 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9833
[-0.         0.         0.        ... -0.8198522 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 3s 15ms/step - loss: 5.9744e-06 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9833
[-0.         0.         0.        ... -0.8309273 -0.        -0.       ]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 4s 15ms/step - loss: 6.7767e-06 - accuracy: 1.0000 - val_loss: 0.1201 - val_accuracy: 0.9835
[-0.          0.          0.         ... -0.82163715  0.
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 3s 15ms/step - loss: 5.7396e-06 - accuracy: 1.0000 - val_loss: 0.1197 - val_accuracy: 0.9838
[-0.        0.        0.       ... -0.824797  0.       -0.      ]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 15ms/step - loss: 5.8046e-06 - accuracy: 1.0000 - val_loss: 0.1192 - val_accuracy: 0.9837
[-0.          0.          0.         ... -0.82850313  0.
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 3s 15ms/step - loss: 3.5680e-06 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9836
[-0.          0.          0.         ... -0.82699186 -0.
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0254 - accuracy: 0.9930 - val_loss: 0.1070 - val_accuracy: 0.9787
[-0.         0.         0.        ... -0.8346981 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0037 - accuracy: 0.9991 - val_loss: 0.1085 - val_accuracy: 0.9790
[-0.          0.          0.         ... -0.81628245  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0019 - accuracy: 0.9997 - val_loss: 0.1075 - val_accuracy: 0.9795
[-0.         0.         0.        ... -0.8310971  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.1084 - val_accuracy: 0.9801
[-0.          0.          0.         ... -0.82721215 -0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9799
[-0.         0.         0.        ... -0.8300648  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 8.6709e-04 - accuracy: 0.9999 - val_loss: 0.1077 - val_accuracy: 0.9799
[-0.         0.         0.        ... -0.8373513 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4875e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9797
[-0.         0.         0.        ... -0.8430129  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8255e-04 - accuracy: 0.9999 - val_loss: 0.1072 - val_accuracy: 0.9805
[-0.          0.          0.         ... -0.84878075  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 15ms/step - loss: 4.3607e-04 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9802
[-0.        0.        0.       ... -0.850467  0.        0.      ]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7904e-04 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9800
[-0.         0.         0.        ... -0.8549605 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3765e-04 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9805
[-0.          0.          0.         ... -0.85776556  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7837e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9805
[-0.         0.         0.        ... -0.8604504  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 15ms/step - loss: 2.5326e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9809
[-0.         0.         0.        ... -0.8676103 -0.        -0.       ]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3964e-04 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9811
[-0.          0.          0.         ... -0.87280065  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9696e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9803
[-0.          0.          0.         ... -0.88051254  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7303e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9810
[-0.          0.          0.         ... -0.88204795  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5516e-04 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9814
[-0.          0.          0.         ... -0.88364357  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7535e-04 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9814
[-0.         0.         0.        ... -0.8959071 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2331e-04 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9811
[-0.         0.         0.        ... -0.9009514  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2230e-04 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9810
[-0.          0.          0.         ... -0.90102774  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0146e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9808
[-0.         0.         0.        ... -0.9089422  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 4s 16ms/step - loss: 9.0634e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9810
[-0.        0.        0.       ... -0.913678  0.        0.      ]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 3s 15ms/step - loss: 7.7731e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9820
[-0.         0.         0.        ... -0.9183778 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 15ms/step - loss: 7.6318e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9816
[-0.          0.          0.         ... -0.92362833  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 6.8667e-05 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9814
[-0.          0.          0.         ... -0.93130267 -0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 15ms/step - loss: 5.6826e-05 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9813
[-0.         0.         0.        ... -0.9367621  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 15ms/step - loss: 9.4301e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9812
[-0.          0.          0.         ... -0.94127256 -0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2516e-04 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9817
[-0.         0.         0.        ... -0.9542083  0.        -0.       ]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 6.8966e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9817
[-0.          0.          0.         ... -0.98382086  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 15ms/step - loss: 9.0313e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9818
[-0.          0.          0.         ... -0.97958344  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 15ms/step - loss: 8.7264e-05 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9823
[-0.          0.          0.         ... -0.99096423  0.
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 4s 15ms/step - loss: 5.3924e-05 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9818
[-0.         0.         0.        ... -1.0028114  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 15ms/step - loss: 3.4515e-05 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9818
[-0.         0.         0.        ... -1.0061536  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 4s 15ms/step - loss: 2.7056e-05 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9816
[-0.         0.         0.        ... -1.0080017  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7346e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9816
[-0.         0.         0.        ... -1.0091317  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4549e-05 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9818
[-0.         0.         0.        ... -1.0140857 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 15ms/step - loss: 3.6826e-05 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9811
[-0.         0.         0.        ... -1.0208602  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5902e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9814
[-0.         0.         0.        ... -1.0266346  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8659e-05 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9813
[-0.         0.         0.        ... -1.0272093 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8641e-05 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9817
[-0.         0.         0.        ... -1.0217968  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8179e-05 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9815
[-0.         0.         0.        ... -1.0300295 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2688e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9817
[-0.        0.        0.       ... -1.038247  0.       -0.      ]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5018e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9811
[-0.         0.         0.        ... -1.0419943  0.        -0.       ]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7878e-05 - accuracy: 1.0000 - val_loss: 0.1295 - val_accuracy: 0.9820
[-0.        0.        0.       ... -1.033658 -0.        0.      ]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7058e-05 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9817
[-0.         0.         0.        ... -1.0435646  0.        -0.       ]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2595e-05 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9819
[-0.         0.         0.        ... -1.0443585  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3152e-05 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9820
[-0.         0.         0.        ... -1.0500063 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 9.0549e-06 - accuracy: 1.0000 - val_loss: 0.1307 - val_accuracy: 0.9816
[-0.         0.         0.        ... -1.0589355  0.        -0.       ]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4352e-05 - accuracy: 1.0000 - val_loss: 0.1319 - val_accuracy: 0.9814
[-0.         0.         0.        ... -1.0576293 -0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1861e-05 - accuracy: 1.0000 - val_loss: 0.1323 - val_accuracy: 0.9815
[-0.         0.         0.        ... -1.0616847  0.         0.       ]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0728 - accuracy: 0.9818 - val_loss: 0.1485 - val_accuracy: 0.9743
[-0.        0.        0.       ... -1.129212  0.        0.      ]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0229 - accuracy: 0.9934 - val_loss: 0.1385 - val_accuracy: 0.9755
[-0.        0.        0.       ... -1.141127  0.        0.      ]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0150 - accuracy: 0.9955 - val_loss: 0.1333 - val_accuracy: 0.9764
[-0.         0.         0.        ... -1.1500582  0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0114 - accuracy: 0.9966 - val_loss: 0.1313 - val_accuracy: 0.9772
[-0.         0.         0.        ... -1.1486633  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0088 - accuracy: 0.9974 - val_loss: 0.1299 - val_accuracy: 0.9771
[-0.         0.         0.        ... -1.1617391  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0068 - accuracy: 0.9981 - val_loss: 0.1289 - val_accuracy: 0.9771
[-0.        0.        0.       ... -1.162172  0.        0.      ]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0055 - accuracy: 0.9987 - val_loss: 0.1293 - val_accuracy: 0.9772
[-0.         0.         0.        ... -1.1651667  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 0.1292 - val_accuracy: 0.9779
[-0.         0.         0.        ... -1.1716433  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0039 - accuracy: 0.9992 - val_loss: 0.1289 - val_accuracy: 0.9777
[-0.         0.         0.        ... -1.1742398  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0035 - accuracy: 0.9995 - val_loss: 0.1304 - val_accuracy: 0.9776
[-0.         0.         0.        ... -1.1771548  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1310 - val_accuracy: 0.9779
[-0.         0.         0.        ... -1.1768707 -0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0027 - accuracy: 0.9996 - val_loss: 0.1314 - val_accuracy: 0.9782
[-0.         0.         0.        ... -1.1873479  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0023 - accuracy: 0.9998 - val_loss: 0.1316 - val_accuracy: 0.9780
[-0.         0.         0.        ... -1.1942216  0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 0.1347 - val_accuracy: 0.9780
[-0.         0.         0.        ... -1.1818326  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0017 - accuracy: 0.9999 - val_loss: 0.1334 - val_accuracy: 0.9779
[-0.         0.         0.        ... -1.1871382  0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1354 - val_accuracy: 0.9773
[-0.         0.         0.        ... -1.1945542  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1370 - val_accuracy: 0.9780
[-0.         0.         0.        ... -1.2026184  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1372 - val_accuracy: 0.9773
[-0.         0.         0.        ... -1.2075963  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.1379 - val_accuracy: 0.9777
[-0.         0.         0.        ... -1.2079312  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9778
[-0.        0.        0.       ... -1.215596  0.        0.      ]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7103e-04 - accuracy: 0.9999 - val_loss: 0.1395 - val_accuracy: 0.9780
[-0.         0.         0.        ... -1.2153323  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5660e-04 - accuracy: 0.9999 - val_loss: 0.1423 - val_accuracy: 0.9778
[-0.         0.         0.        ... -1.2166299  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5472e-04 - accuracy: 1.0000 - val_loss: 0.1411 - val_accuracy: 0.9781
[-0.         0.         0.        ... -1.2306023  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3086e-04 - accuracy: 0.9999 - val_loss: 0.1429 - val_accuracy: 0.9777
[-0.         0.         0.        ... -1.2321162 -0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 15ms/step - loss: 5.9676e-04 - accuracy: 1.0000 - val_loss: 0.1438 - val_accuracy: 0.9779
[-0.         0.         0.        ... -1.2486132  0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 15ms/step - loss: 6.1295e-04 - accuracy: 0.9999 - val_loss: 0.1448 - val_accuracy: 0.9782
[-0.         0.         0.        ... -1.2411742  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 15ms/step - loss: 5.7922e-04 - accuracy: 0.9999 - val_loss: 0.1462 - val_accuracy: 0.9779
[-0.         0.         0.        ... -1.2460974 -0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7296e-04 - accuracy: 1.0000 - val_loss: 0.1477 - val_accuracy: 0.9784
[-0.         0.         0.        ... -1.2592012  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 15ms/step - loss: 3.9452e-04 - accuracy: 1.0000 - val_loss: 0.1486 - val_accuracy: 0.9784
[-0.         0.         0.        ... -1.2589724  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3251e-04 - accuracy: 1.0000 - val_loss: 0.1486 - val_accuracy: 0.9786
[-0.         0.         0.        ... -1.2649356 -0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5737e-04 - accuracy: 1.0000 - val_loss: 0.1518 - val_accuracy: 0.9778
[-0.         0.         0.        ... -1.2751635 -0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9689e-04 - accuracy: 1.0000 - val_loss: 0.1528 - val_accuracy: 0.9782
[-0.         0.         0.        ... -1.2827413  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6009e-04 - accuracy: 1.0000 - val_loss: 0.1531 - val_accuracy: 0.9782
[-0.         0.         0.        ... -1.2864949  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 15ms/step - loss: 2.6719e-04 - accuracy: 1.0000 - val_loss: 0.1536 - val_accuracy: 0.9780
[-0.         0.         0.        ... -1.2937901  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6804e-04 - accuracy: 1.0000 - val_loss: 0.1568 - val_accuracy: 0.9786
[-0.         0.         0.        ... -1.2982243  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4089e-04 - accuracy: 1.0000 - val_loss: 0.1594 - val_accuracy: 0.9781
[-0.        0.        0.       ... -1.306254  0.       -0.      ]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9973e-04 - accuracy: 1.0000 - val_loss: 0.1597 - val_accuracy: 0.9786
[-0.         0.         0.        ... -1.3152028  0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6836e-04 - accuracy: 1.0000 - val_loss: 0.1610 - val_accuracy: 0.9779
[-0.         0.         0.        ... -1.3103464  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 15ms/step - loss: 3.3480e-04 - accuracy: 1.0000 - val_loss: 0.1620 - val_accuracy: 0.9782
[-0.         0.         0.        ... -1.3056033  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0301e-04 - accuracy: 1.0000 - val_loss: 0.1632 - val_accuracy: 0.9783
[-0.         0.         0.        ... -1.3112738 -0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 4s 15ms/step - loss: 1.8167e-04 - accuracy: 1.0000 - val_loss: 0.1633 - val_accuracy: 0.9787
[-0.        0.        0.       ... -1.315901  0.        0.      ]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 3s 15ms/step - loss: 1.9941e-04 - accuracy: 1.0000 - val_loss: 0.1665 - val_accuracy: 0.9780
[-0.         0.         0.        ... -1.3305752 -0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4542e-04 - accuracy: 0.9999 - val_loss: 0.1661 - val_accuracy: 0.9790
[-0.         0.         0.        ... -1.3301604  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5855e-04 - accuracy: 1.0000 - val_loss: 0.1678 - val_accuracy: 0.9789
[-0.         0.         0.        ... -1.3397722  0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3761e-04 - accuracy: 0.9999 - val_loss: 0.1672 - val_accuracy: 0.9794
[-0.         0.         0.        ... -1.3380265  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7658e-04 - accuracy: 1.0000 - val_loss: 0.1695 - val_accuracy: 0.9788
[-0.         0.         0.        ... -1.3485799  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6581e-04 - accuracy: 1.0000 - val_loss: 0.1719 - val_accuracy: 0.9781
[-0.         0.         0.        ... -1.3614453 -0.        -0.       ]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3767e-04 - accuracy: 1.0000 - val_loss: 0.1692 - val_accuracy: 0.9783
[-0.         0.         0.        ... -1.3544908  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0582e-04 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9792
[-0.        0.        0.       ... -1.369297  0.        0.      ]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1617e-04 - accuracy: 1.0000 - val_loss: 0.1714 - val_accuracy: 0.9794
[-0.         0.         0.        ... -1.3622464  0.         0.       ]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 4s 15ms/step - loss: 0.2157 - accuracy: 0.9503 - val_loss: 0.2026 - val_accuracy: 0.9583
[-0.         0.         0.        ... -1.1765113  0.        -0.       ]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0963 - accuracy: 0.9728 - val_loss: 0.1745 - val_accuracy: 0.9629
[-0.         0.         0.        ... -1.1249127 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0774 - accuracy: 0.9765 - val_loss: 0.1622 - val_accuracy: 0.9646
[-0.         0.         0.        ... -1.0870408 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0657 - accuracy: 0.9798 - val_loss: 0.1542 - val_accuracy: 0.9654
[-0.         0.         0.        ... -1.0596551  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0576 - accuracy: 0.9818 - val_loss: 0.1481 - val_accuracy: 0.9661
[-0.         0.         0.        ... -1.0405695 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0526 - accuracy: 0.9832 - val_loss: 0.1440 - val_accuracy: 0.9659
[-0.         0.         0.        ... -1.0240899 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0477 - accuracy: 0.9850 - val_loss: 0.1404 - val_accuracy: 0.9670
[-0.        0.        0.       ... -1.012937  0.        0.      ]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0444 - accuracy: 0.9857 - val_loss: 0.1375 - val_accuracy: 0.9673
[-0.        0.        0.       ... -1.004185  0.        0.      ]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0413 - accuracy: 0.9867 - val_loss: 0.1361 - val_accuracy: 0.9682
[-0.         0.         0.        ... -0.9975208  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0385 - accuracy: 0.9875 - val_loss: 0.1345 - val_accuracy: 0.9686
[-0.          0.          0.         ... -0.99195725  0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0362 - accuracy: 0.9879 - val_loss: 0.1330 - val_accuracy: 0.9694
[-0.          0.          0.         ... -0.98365176  0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0340 - accuracy: 0.9887 - val_loss: 0.1320 - val_accuracy: 0.9695
[-0.         0.         0.        ... -0.9805692  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0318 - accuracy: 0.9897 - val_loss: 0.1322 - val_accuracy: 0.9696
[-0.         0.         0.        ... -0.9754846  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0304 - accuracy: 0.9902 - val_loss: 0.1315 - val_accuracy: 0.9695
[-0.         0.         0.        ... -0.9749589  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0286 - accuracy: 0.9908 - val_loss: 0.1324 - val_accuracy: 0.9700
[-0.         0.         0.        ... -0.9709703  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0271 - accuracy: 0.9914 - val_loss: 0.1319 - val_accuracy: 0.9702
[-0.          0.          0.         ... -0.96949524  0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0257 - accuracy: 0.9919 - val_loss: 0.1320 - val_accuracy: 0.9698
[-0.         0.         0.        ... -0.9702149 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0245 - accuracy: 0.9924 - val_loss: 0.1325 - val_accuracy: 0.9699
[-0.         0.         0.        ... -0.9675595  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0232 - accuracy: 0.9927 - val_loss: 0.1324 - val_accuracy: 0.9694
[-0.         0.         0.        ... -0.9705282  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0226 - accuracy: 0.9933 - val_loss: 0.1337 - val_accuracy: 0.9695
[-0.          0.          0.         ... -0.97022223  0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0217 - accuracy: 0.9935 - val_loss: 0.1337 - val_accuracy: 0.9705
[-0.          0.          0.         ... -0.97583264  0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0201 - accuracy: 0.9939 - val_loss: 0.1349 - val_accuracy: 0.9703
[-0.          0.          0.         ... -0.97692347 -0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0190 - accuracy: 0.9946 - val_loss: 0.1358 - val_accuracy: 0.9701
[-0.         0.         0.        ... -0.9779637  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0181 - accuracy: 0.9950 - val_loss: 0.1379 - val_accuracy: 0.9707
[-0.         0.         0.        ... -0.9833529  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0171 - accuracy: 0.9953 - val_loss: 0.1386 - val_accuracy: 0.9708
[-0.         0.         0.        ... -0.9822294 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0169 - accuracy: 0.9952 - val_loss: 0.1389 - val_accuracy: 0.9702
[-0.          0.          0.         ... -0.98926944  0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0159 - accuracy: 0.9953 - val_loss: 0.1395 - val_accuracy: 0.9709
[-0.          0.          0.         ... -0.99106526 -0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0151 - accuracy: 0.9957 - val_loss: 0.1406 - val_accuracy: 0.9713
[-0.          0.          0.         ... -0.99646693 -0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0145 - accuracy: 0.9960 - val_loss: 0.1443 - val_accuracy: 0.9708
[-0.         0.         0.        ... -0.9926664 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0144 - accuracy: 0.9957 - val_loss: 0.1456 - val_accuracy: 0.9701
[-0.          0.          0.         ... -0.99850523 -0.
  0.        ]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0132 - accuracy: 0.9963 - val_loss: 0.1454 - val_accuracy: 0.9707
[-0.        0.        0.       ... -1.000106  0.        0.      ]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0128 - accuracy: 0.9966 - val_loss: 0.1471 - val_accuracy: 0.9707
[-0.         0.         0.        ... -1.0031863 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0122 - accuracy: 0.9967 - val_loss: 0.1478 - val_accuracy: 0.9716
[-0.         0.         0.        ... -1.0152969 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0119 - accuracy: 0.9970 - val_loss: 0.1494 - val_accuracy: 0.9710
[-0.         0.         0.        ... -1.0244104  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0109 - accuracy: 0.9973 - val_loss: 0.1500 - val_accuracy: 0.9712
[-0.         0.         0.        ... -1.0313693  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0105 - accuracy: 0.9974 - val_loss: 0.1533 - val_accuracy: 0.9713
[-0.         0.         0.        ... -1.0344816 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0100 - accuracy: 0.9975 - val_loss: 0.1542 - val_accuracy: 0.9707
[-0.         0.         0.        ... -1.0482715  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0099 - accuracy: 0.9976 - val_loss: 0.1547 - val_accuracy: 0.9711
[-0.         0.         0.        ... -1.0522327 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9977 - val_loss: 0.1592 - val_accuracy: 0.9706
[-0.         0.         0.        ... -1.0525693  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0092 - accuracy: 0.9977 - val_loss: 0.1596 - val_accuracy: 0.9710
[-0.         0.         0.        ... -1.0575668  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0085 - accuracy: 0.9981 - val_loss: 0.1610 - val_accuracy: 0.9710
[-0.         0.         0.        ... -1.0626106  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9982 - val_loss: 0.1624 - val_accuracy: 0.9708
[-0.         0.         0.        ... -1.0641626  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9983 - val_loss: 0.1634 - val_accuracy: 0.9703
[-0.         0.         0.        ... -1.0819227  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0080 - accuracy: 0.9981 - val_loss: 0.1662 - val_accuracy: 0.9713
[-0.         0.         0.        ... -1.0779172  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9985 - val_loss: 0.1666 - val_accuracy: 0.9714
[-0.         0.         0.        ... -1.0790343  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0070 - accuracy: 0.9985 - val_loss: 0.1684 - val_accuracy: 0.9709
[-0.         0.         0.        ... -1.0932534  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9982 - val_loss: 0.1711 - val_accuracy: 0.9712
[-0.         0.         0.        ... -1.1132145  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0068 - accuracy: 0.9984 - val_loss: 0.1719 - val_accuracy: 0.9713
[-0.         0.         0.        ... -1.1124408 -0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0065 - accuracy: 0.9987 - val_loss: 0.1731 - val_accuracy: 0.9709
[-0.         0.         0.        ... -1.1116756  0.        -0.       ]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0063 - accuracy: 0.9988 - val_loss: 0.1750 - val_accuracy: 0.9708
[-0.         0.         0.        ... -1.1213894  0.         0.       ]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7795 - accuracy: 0.8075 - val_loss: 0.4690 - val_accuracy: 0.8729
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.3774 - accuracy: 0.8888 - val_loss: 0.3781 - val_accuracy: 0.8978
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 15ms/step - loss: 0.3219 - accuracy: 0.9035 - val_loss: 0.3401 - val_accuracy: 0.9076
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2929 - accuracy: 0.9118 - val_loss: 0.3173 - val_accuracy: 0.9134
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2714 - accuracy: 0.9186 - val_loss: 0.2997 - val_accuracy: 0.9182
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2562 - accuracy: 0.9226 - val_loss: 0.2870 - val_accuracy: 0.9217
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2441 - accuracy: 0.9268 - val_loss: 0.2752 - val_accuracy: 0.9249
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2334 - accuracy: 0.9300 - val_loss: 0.2652 - val_accuracy: 0.9273
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2243 - accuracy: 0.9318 - val_loss: 0.2572 - val_accuracy: 0.9290
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2168 - accuracy: 0.9347 - val_loss: 0.2503 - val_accuracy: 0.9306
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2102 - accuracy: 0.9369 - val_loss: 0.2447 - val_accuracy: 0.9324
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2049 - accuracy: 0.9384 - val_loss: 0.2392 - val_accuracy: 0.9341
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1995 - accuracy: 0.9407 - val_loss: 0.2344 - val_accuracy: 0.9361
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1947 - accuracy: 0.9423 - val_loss: 0.2303 - val_accuracy: 0.9371
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1902 - accuracy: 0.9429 - val_loss: 0.2265 - val_accuracy: 0.9368
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1858 - accuracy: 0.9445 - val_loss: 0.2231 - val_accuracy: 0.9378
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1833 - accuracy: 0.9455 - val_loss: 0.2206 - val_accuracy: 0.9383
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1797 - accuracy: 0.9457 - val_loss: 0.2177 - val_accuracy: 0.9394
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1773 - accuracy: 0.9472 - val_loss: 0.2154 - val_accuracy: 0.9408
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1746 - accuracy: 0.9481 - val_loss: 0.2134 - val_accuracy: 0.9411
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1721 - accuracy: 0.9488 - val_loss: 0.2117 - val_accuracy: 0.9416
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1705 - accuracy: 0.9492 - val_loss: 0.2103 - val_accuracy: 0.9418
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1685 - accuracy: 0.9504 - val_loss: 0.2090 - val_accuracy: 0.9415
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1671 - accuracy: 0.9510 - val_loss: 0.2078 - val_accuracy: 0.9423
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1652 - accuracy: 0.9511 - val_loss: 0.2064 - val_accuracy: 0.9428
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1635 - accuracy: 0.9522 - val_loss: 0.2051 - val_accuracy: 0.9436
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1630 - accuracy: 0.9518 - val_loss: 0.2036 - val_accuracy: 0.9437
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1615 - accuracy: 0.9514 - val_loss: 0.2027 - val_accuracy: 0.9444
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 5s 22ms/step - loss: 0.1594 - accuracy: 0.9528 - val_loss: 0.2020 - val_accuracy: 0.9444
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 5s 20ms/step - loss: 0.1590 - accuracy: 0.9526 - val_loss: 0.2007 - val_accuracy: 0.9451
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 5s 21ms/step - loss: 0.1579 - accuracy: 0.9533 - val_loss: 0.1996 - val_accuracy: 0.9450
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 5s 22ms/step - loss: 0.1563 - accuracy: 0.9534 - val_loss: 0.1990 - val_accuracy: 0.9454
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1548 - accuracy: 0.9536 - val_loss: 0.1980 - val_accuracy: 0.9459
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1533 - accuracy: 0.9550 - val_loss: 0.1977 - val_accuracy: 0.9464
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1527 - accuracy: 0.9543 - val_loss: 0.1970 - val_accuracy: 0.9459
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1516 - accuracy: 0.9552 - val_loss: 0.1968 - val_accuracy: 0.9467
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1511 - accuracy: 0.9550 - val_loss: 0.1956 - val_accuracy: 0.9475
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1497 - accuracy: 0.9555 - val_loss: 0.1956 - val_accuracy: 0.9468
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1485 - accuracy: 0.9563 - val_loss: 0.1953 - val_accuracy: 0.9471
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1482 - accuracy: 0.9561 - val_loss: 0.1946 - val_accuracy: 0.9470
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9566 - val_loss: 0.1949 - val_accuracy: 0.9475
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1471 - accuracy: 0.9568 - val_loss: 0.1942 - val_accuracy: 0.9474
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9572 - val_loss: 0.1941 - val_accuracy: 0.9474
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1457 - accuracy: 0.9565 - val_loss: 0.1938 - val_accuracy: 0.9479
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1453 - accuracy: 0.9568 - val_loss: 0.1944 - val_accuracy: 0.9477
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1437 - accuracy: 0.9572 - val_loss: 0.1938 - val_accuracy: 0.9479
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1439 - accuracy: 0.9573 - val_loss: 0.1940 - val_accuracy: 0.9476
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1432 - accuracy: 0.9574 - val_loss: 0.1938 - val_accuracy: 0.9477
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1419 - accuracy: 0.9577 - val_loss: 0.1941 - val_accuracy: 0.9476
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1420 - accuracy: 0.9582 - val_loss: 0.1936 - val_accuracy: 0.9481
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 4s 15ms/step - loss: 0.6639 - accuracy: 0.7966 - val_loss: 0.5465 - val_accuracy: 0.8385
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 4s 15ms/step - loss: 0.5403 - accuracy: 0.8354 - val_loss: 0.5089 - val_accuracy: 0.8527
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 4s 15ms/step - loss: 0.5132 - accuracy: 0.8451 - val_loss: 0.4909 - val_accuracy: 0.8579
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4977 - accuracy: 0.8506 - val_loss: 0.4808 - val_accuracy: 0.8624
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4876 - accuracy: 0.8529 - val_loss: 0.4735 - val_accuracy: 0.8652
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 4s 17ms/step - loss: 0.4794 - accuracy: 0.8564 - val_loss: 0.4678 - val_accuracy: 0.8672
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4732 - accuracy: 0.8581 - val_loss: 0.4634 - val_accuracy: 0.8687
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4676 - accuracy: 0.8606 - val_loss: 0.4577 - val_accuracy: 0.8714
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4633 - accuracy: 0.8614 - val_loss: 0.4546 - val_accuracy: 0.8726
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4588 - accuracy: 0.8624 - val_loss: 0.4513 - val_accuracy: 0.8740
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4554 - accuracy: 0.8644 - val_loss: 0.4487 - val_accuracy: 0.8752
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4523 - accuracy: 0.8644 - val_loss: 0.4464 - val_accuracy: 0.8753
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4506 - accuracy: 0.8649 - val_loss: 0.4444 - val_accuracy: 0.8753
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4484 - accuracy: 0.8658 - val_loss: 0.4427 - val_accuracy: 0.8759
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4462 - accuracy: 0.8661 - val_loss: 0.4413 - val_accuracy: 0.8757
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4457 - accuracy: 0.8661 - val_loss: 0.4398 - val_accuracy: 0.8759
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4426 - accuracy: 0.8675 - val_loss: 0.4384 - val_accuracy: 0.8769
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4423 - accuracy: 0.8665 - val_loss: 0.4371 - val_accuracy: 0.8771
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4405 - accuracy: 0.8682 - val_loss: 0.4358 - val_accuracy: 0.8769
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4389 - accuracy: 0.8680 - val_loss: 0.4346 - val_accuracy: 0.8775
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4388 - accuracy: 0.8683 - val_loss: 0.4335 - val_accuracy: 0.8785
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4377 - accuracy: 0.8686 - val_loss: 0.4324 - val_accuracy: 0.8790
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4355 - accuracy: 0.8695 - val_loss: 0.4315 - val_accuracy: 0.8795
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4351 - accuracy: 0.8702 - val_loss: 0.4308 - val_accuracy: 0.8788
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4337 - accuracy: 0.8703 - val_loss: 0.4300 - val_accuracy: 0.8789
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4328 - accuracy: 0.8697 - val_loss: 0.4296 - val_accuracy: 0.8785
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4330 - accuracy: 0.8704 - val_loss: 0.4292 - val_accuracy: 0.8784
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4312 - accuracy: 0.8702 - val_loss: 0.4285 - val_accuracy: 0.8786
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4312 - accuracy: 0.8702 - val_loss: 0.4275 - val_accuracy: 0.8784
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4310 - accuracy: 0.8705 - val_loss: 0.4272 - val_accuracy: 0.8782
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4296 - accuracy: 0.8712 - val_loss: 0.4270 - val_accuracy: 0.8783
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4289 - accuracy: 0.8714 - val_loss: 0.4260 - val_accuracy: 0.8792
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4290 - accuracy: 0.8715 - val_loss: 0.4256 - val_accuracy: 0.8791
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4279 - accuracy: 0.8722 - val_loss: 0.4251 - val_accuracy: 0.8791
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4269 - accuracy: 0.8727 - val_loss: 0.4246 - val_accuracy: 0.8797
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4269 - accuracy: 0.8718 - val_loss: 0.4241 - val_accuracy: 0.8798
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4261 - accuracy: 0.8725 - val_loss: 0.4237 - val_accuracy: 0.8803
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4264 - accuracy: 0.8731 - val_loss: 0.4233 - val_accuracy: 0.8803
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4255 - accuracy: 0.8722 - val_loss: 0.4229 - val_accuracy: 0.8806
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4254 - accuracy: 0.8728 - val_loss: 0.4222 - val_accuracy: 0.8809
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4241 - accuracy: 0.8729 - val_loss: 0.4219 - val_accuracy: 0.8807
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4236 - accuracy: 0.8729 - val_loss: 0.4217 - val_accuracy: 0.8814
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4245 - accuracy: 0.8731 - val_loss: 0.4214 - val_accuracy: 0.8802
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4224 - accuracy: 0.8733 - val_loss: 0.4209 - val_accuracy: 0.8807
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4230 - accuracy: 0.8735 - val_loss: 0.4199 - val_accuracy: 0.8815
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4225 - accuracy: 0.8736 - val_loss: 0.4189 - val_accuracy: 0.8818
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4210 - accuracy: 0.8739 - val_loss: 0.4182 - val_accuracy: 0.8821
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4202 - accuracy: 0.8743 - val_loss: 0.4180 - val_accuracy: 0.8823
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4206 - accuracy: 0.8741 - val_loss: 0.4174 - val_accuracy: 0.8822
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4202 - accuracy: 0.8742 - val_loss: 0.4166 - val_accuracy: 0.8825
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0673 - accuracy: 0.6587 - val_loss: 1.0029 - val_accuracy: 0.6754
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9734 - accuracy: 0.6755 - val_loss: 0.9568 - val_accuracy: 0.6862
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9427 - accuracy: 0.6837 - val_loss: 0.9234 - val_accuracy: 0.6967
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9194 - accuracy: 0.6945 - val_loss: 0.9061 - val_accuracy: 0.7071
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9105 - accuracy: 0.7003 - val_loss: 0.9004 - val_accuracy: 0.7099
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9061 - accuracy: 0.7017 - val_loss: 0.8963 - val_accuracy: 0.7114
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9012 - accuracy: 0.7048 - val_loss: 0.8890 - val_accuracy: 0.7153
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8942 - accuracy: 0.7069 - val_loss: 0.8834 - val_accuracy: 0.7172
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8903 - accuracy: 0.7096 - val_loss: 0.8786 - val_accuracy: 0.7203
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8855 - accuracy: 0.7119 - val_loss: 0.8738 - val_accuracy: 0.7218
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8803 - accuracy: 0.7143 - val_loss: 0.8703 - val_accuracy: 0.7235
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8772 - accuracy: 0.7148 - val_loss: 0.8673 - val_accuracy: 0.7254
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8745 - accuracy: 0.7161 - val_loss: 0.8639 - val_accuracy: 0.7259
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8711 - accuracy: 0.7169 - val_loss: 0.8619 - val_accuracy: 0.7268
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8696 - accuracy: 0.7181 - val_loss: 0.8598 - val_accuracy: 0.7282
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8667 - accuracy: 0.7179 - val_loss: 0.8577 - val_accuracy: 0.7287
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8659 - accuracy: 0.7184 - val_loss: 0.8562 - val_accuracy: 0.7304
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8632 - accuracy: 0.7204 - val_loss: 0.8551 - val_accuracy: 0.7299
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8629 - accuracy: 0.7198 - val_loss: 0.8540 - val_accuracy: 0.7306
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8622 - accuracy: 0.7206 - val_loss: 0.8538 - val_accuracy: 0.7309
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8613 - accuracy: 0.7207 - val_loss: 0.8525 - val_accuracy: 0.7322
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8604 - accuracy: 0.7209 - val_loss: 0.8516 - val_accuracy: 0.7320
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8600 - accuracy: 0.7211 - val_loss: 0.8512 - val_accuracy: 0.7322
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8601 - accuracy: 0.7203 - val_loss: 0.8505 - val_accuracy: 0.7321
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8595 - accuracy: 0.7214 - val_loss: 0.8496 - val_accuracy: 0.7328
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 4s 18ms/step - loss: 0.8587 - accuracy: 0.7213 - val_loss: 0.8490 - val_accuracy: 0.7330
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8576 - accuracy: 0.7219 - val_loss: 0.8488 - val_accuracy: 0.7332
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8573 - accuracy: 0.7211 - val_loss: 0.8481 - val_accuracy: 0.7333
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8568 - accuracy: 0.7216 - val_loss: 0.8479 - val_accuracy: 0.7337
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8566 - accuracy: 0.7219 - val_loss: 0.8474 - val_accuracy: 0.7333
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8565 - accuracy: 0.7228 - val_loss: 0.8469 - val_accuracy: 0.7338
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8557 - accuracy: 0.7228 - val_loss: 0.8462 - val_accuracy: 0.7344
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8544 - accuracy: 0.7230 - val_loss: 0.8454 - val_accuracy: 0.7343
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8542 - accuracy: 0.7226 - val_loss: 0.8446 - val_accuracy: 0.7340
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8541 - accuracy: 0.7227 - val_loss: 0.8439 - val_accuracy: 0.7342
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8535 - accuracy: 0.7233 - val_loss: 0.8438 - val_accuracy: 0.7340
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8520 - accuracy: 0.7232 - val_loss: 0.8430 - val_accuracy: 0.7342
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8521 - accuracy: 0.7234 - val_loss: 0.8429 - val_accuracy: 0.7345
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8518 - accuracy: 0.7236 - val_loss: 0.8423 - val_accuracy: 0.7350
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8515 - accuracy: 0.7236 - val_loss: 0.8421 - val_accuracy: 0.7350
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8516 - accuracy: 0.7238 - val_loss: 0.8414 - val_accuracy: 0.7350
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8502 - accuracy: 0.7236 - val_loss: 0.8413 - val_accuracy: 0.7353
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8508 - accuracy: 0.7234 - val_loss: 0.8408 - val_accuracy: 0.7354
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8502 - accuracy: 0.7236 - val_loss: 0.8408 - val_accuracy: 0.7357
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8503 - accuracy: 0.7250 - val_loss: 0.8406 - val_accuracy: 0.7355
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8495 - accuracy: 0.7247 - val_loss: 0.8404 - val_accuracy: 0.7359
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8496 - accuracy: 0.7243 - val_loss: 0.8399 - val_accuracy: 0.7361
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8492 - accuracy: 0.7247 - val_loss: 0.8399 - val_accuracy: 0.7353
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8497 - accuracy: 0.7248 - val_loss: 0.8397 - val_accuracy: 0.7354
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8484 - accuracy: 0.7250 - val_loss: 0.8397 - val_accuracy: 0.7352
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8488 - accuracy: 0.7244 - val_loss: 0.8395 - val_accuracy: 0.7351
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8500 - accuracy: 0.7248 - val_loss: 0.8395 - val_accuracy: 0.7354
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8472 - accuracy: 0.7251 - val_loss: 0.8392 - val_accuracy: 0.7349
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8477 - accuracy: 0.7254 - val_loss: 0.8389 - val_accuracy: 0.7356
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8471 - accuracy: 0.7253 - val_loss: 0.8390 - val_accuracy: 0.7355
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8478 - accuracy: 0.7250 - val_loss: 0.8388 - val_accuracy: 0.7358
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8480 - accuracy: 0.7260 - val_loss: 0.8386 - val_accuracy: 0.7355
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8476 - accuracy: 0.7254 - val_loss: 0.8386 - val_accuracy: 0.7355
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8472 - accuracy: 0.7257 - val_loss: 0.8387 - val_accuracy: 0.7351
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8462 - accuracy: 0.7251 - val_loss: 0.8384 - val_accuracy: 0.7359
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8465 - accuracy: 0.7258 - val_loss: 0.8386 - val_accuracy: 0.7354
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8470 - accuracy: 0.7251 - val_loss: 0.8383 - val_accuracy: 0.7354
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8477 - accuracy: 0.7250 - val_loss: 0.8383 - val_accuracy: 0.7353
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8454 - accuracy: 0.7257 - val_loss: 0.8378 - val_accuracy: 0.7357
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8466 - accuracy: 0.7262 - val_loss: 0.8378 - val_accuracy: 0.7354
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8460 - accuracy: 0.7258 - val_loss: 0.8369 - val_accuracy: 0.7368
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8444 - accuracy: 0.7267 - val_loss: 0.8362 - val_accuracy: 0.7366
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8438 - accuracy: 0.7276 - val_loss: 0.8357 - val_accuracy: 0.7367
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8439 - accuracy: 0.7266 - val_loss: 0.8354 - val_accuracy: 0.7365
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8429 - accuracy: 0.7263 - val_loss: 0.8347 - val_accuracy: 0.7370
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8428 - accuracy: 0.7271 - val_loss: 0.8336 - val_accuracy: 0.7368
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8417 - accuracy: 0.7275 - val_loss: 0.8332 - val_accuracy: 0.7375
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8424 - accuracy: 0.7269 - val_loss: 0.8333 - val_accuracy: 0.7373
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8417 - accuracy: 0.7272 - val_loss: 0.8331 - val_accuracy: 0.7375
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8422 - accuracy: 0.7265 - val_loss: 0.8330 - val_accuracy: 0.7368
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8411 - accuracy: 0.7263 - val_loss: 0.8327 - val_accuracy: 0.7371
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8415 - accuracy: 0.7264 - val_loss: 0.8325 - val_accuracy: 0.7366
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8402 - accuracy: 0.7268 - val_loss: 0.8324 - val_accuracy: 0.7369
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8411 - accuracy: 0.7266 - val_loss: 0.8325 - val_accuracy: 0.7369
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8409 - accuracy: 0.7267 - val_loss: 0.8323 - val_accuracy: 0.7370
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8404 - accuracy: 0.7278 - val_loss: 0.8324 - val_accuracy: 0.7372
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8414 - accuracy: 0.7270 - val_loss: 0.8323 - val_accuracy: 0.7370
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8404 - accuracy: 0.7265 - val_loss: 0.8321 - val_accuracy: 0.7365
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8401 - accuracy: 0.7272 - val_loss: 0.8320 - val_accuracy: 0.7367
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8408 - accuracy: 0.7270 - val_loss: 0.8320 - val_accuracy: 0.7374
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8408 - accuracy: 0.7269 - val_loss: 0.8318 - val_accuracy: 0.7371
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8403 - accuracy: 0.7275 - val_loss: 0.8318 - val_accuracy: 0.7373
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8397 - accuracy: 0.7281 - val_loss: 0.8318 - val_accuracy: 0.7369
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8397 - accuracy: 0.7273 - val_loss: 0.8318 - val_accuracy: 0.7371
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8398 - accuracy: 0.7277 - val_loss: 0.8316 - val_accuracy: 0.7368
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8394 - accuracy: 0.7271 - val_loss: 0.8316 - val_accuracy: 0.7369
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8403 - accuracy: 0.7273 - val_loss: 0.8315 - val_accuracy: 0.7370
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8393 - accuracy: 0.7280 - val_loss: 0.8316 - val_accuracy: 0.7369
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8402 - accuracy: 0.7274 - val_loss: 0.8314 - val_accuracy: 0.7374
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8398 - accuracy: 0.7266 - val_loss: 0.8313 - val_accuracy: 0.7375
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8399 - accuracy: 0.7271 - val_loss: 0.8313 - val_accuracy: 0.7372
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8390 - accuracy: 0.7275 - val_loss: 0.8314 - val_accuracy: 0.7368
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8389 - accuracy: 0.7268 - val_loss: 0.8312 - val_accuracy: 0.7371
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8393 - accuracy: 0.7271 - val_loss: 0.8309 - val_accuracy: 0.7371
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8395 - accuracy: 0.7284 - val_loss: 0.8304 - val_accuracy: 0.7374
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 4s 9ms/step - loss: 0.8536 - accuracy: 0.9010 - val_loss: 0.8275 - val_accuracy: 0.9060
[ 0.          0.          0.         ... -0.         -0.
  0.21805383]
Sparsity at: 0.49998323497854075
Epoch 2/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8433 - accuracy: 0.9020 - val_loss: 0.8256 - val_accuracy: 0.9056
[ 0.          0.          0.         ... -0.         -0.
  0.22636366]
Sparsity at: 0.49998323497854075
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8420 - accuracy: 0.9018 - val_loss: 0.8254 - val_accuracy: 0.9047
[ 0.         0.         0.        ... -0.        -0.         0.2295623]
Sparsity at: 0.49998323497854075
Epoch 4/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8417 - accuracy: 0.9018 - val_loss: 0.8245 - val_accuracy: 0.9048
[ 0.         0.         0.        ... -0.        -0.         0.2311757]
Sparsity at: 0.49998323497854075
Epoch 5/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8412 - accuracy: 0.9019 - val_loss: 0.8244 - val_accuracy: 0.9050
[ 0.          0.          0.         ... -0.         -0.
  0.23199476]
Sparsity at: 0.49998323497854075
Epoch 6/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8408 - accuracy: 0.9019 - val_loss: 0.8247 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.         -0.
  0.23231447]
Sparsity at: 0.49998323497854075
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.9020 - val_loss: 0.8235 - val_accuracy: 0.9053
[ 0.          0.          0.         ... -0.         -0.
  0.23232621]
Sparsity at: 0.49998323497854075
Epoch 8/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8403 - accuracy: 0.9015 - val_loss: 0.8233 - val_accuracy: 0.9050
[ 0.          0.          0.         ... -0.         -0.
  0.23200971]
Sparsity at: 0.49998323497854075
Epoch 9/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9017 - val_loss: 0.8237 - val_accuracy: 0.9039
[ 0.         0.         0.        ... -0.        -0.         0.2316019]
Sparsity at: 0.49998323497854075
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9015 - val_loss: 0.8234 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.         -0.
  0.23150554]
Sparsity at: 0.49998323497854075
Epoch 11/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9017 - val_loss: 0.8231 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.         -0.
  0.23113468]
Sparsity at: 0.49998323497854075
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9015 - val_loss: 0.8232 - val_accuracy: 0.9046
[ 0.        0.        0.       ... -0.       -0.        0.231103]
Sparsity at: 0.49998323497854075
Epoch 13/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9018 - val_loss: 0.8233 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.23084843]
Sparsity at: 0.49998323497854075
Epoch 14/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.23062828]
Sparsity at: 0.49998323497854075
Epoch 15/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8396 - accuracy: 0.9017 - val_loss: 0.8230 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.         -0.
  0.23050003]
Sparsity at: 0.49998323497854075
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8396 - accuracy: 0.9018 - val_loss: 0.8230 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.23042256]
Sparsity at: 0.49998323497854075
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8395 - accuracy: 0.9018 - val_loss: 0.8229 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.         -0.
  0.23043126]
Sparsity at: 0.49998323497854075
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9018 - val_loss: 0.8227 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.23020257]
Sparsity at: 0.49998323497854075
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8395 - accuracy: 0.9020 - val_loss: 0.8225 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.         -0.
  0.22984925]
Sparsity at: 0.49998323497854075
Epoch 20/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8393 - accuracy: 0.9019 - val_loss: 0.8228 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.22988974]
Sparsity at: 0.49998323497854075
Epoch 21/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9014 - val_loss: 0.8226 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.         -0.
  0.22962575]
Sparsity at: 0.49998323497854075
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8393 - accuracy: 0.9020 - val_loss: 0.8223 - val_accuracy: 0.9045
[ 0.         0.         0.        ... -0.        -0.         0.2296686]
Sparsity at: 0.49998323497854075
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8393 - accuracy: 0.9019 - val_loss: 0.8231 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.         -0.
  0.22939289]
Sparsity at: 0.49998323497854075
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8234 - val_accuracy: 0.9044
[ 0.       0.       0.      ... -0.      -0.       0.22926]
Sparsity at: 0.49998323497854075
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9050
[ 0.          0.          0.         ... -0.         -0.
  0.22909662]
Sparsity at: 0.49998323497854075
Epoch 26/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.22895984]
Sparsity at: 0.49998323497854075
Epoch 27/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8392 - accuracy: 0.9020 - val_loss: 0.8224 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.         -0.
  0.22890972]
Sparsity at: 0.49998323497854075
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8225 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.         -0.
  0.22902352]
Sparsity at: 0.49998323497854075
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9020 - val_loss: 0.8227 - val_accuracy: 0.9044
[ 0.          0.          0.         ... -0.         -0.
  0.22851856]
Sparsity at: 0.49998323497854075
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9017 - val_loss: 0.8226 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.22874747]
Sparsity at: 0.49998323497854075
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9019 - val_loss: 0.8224 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.         -0.
  0.22886471]
Sparsity at: 0.49998323497854075
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9018 - val_loss: 0.8227 - val_accuracy: 0.9043
[ 0.          0.          0.         ... -0.         -0.
  0.22834724]
Sparsity at: 0.49998323497854075
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9019 - val_loss: 0.8222 - val_accuracy: 0.9053
[ 0.          0.          0.         ... -0.         -0.
  0.22836772]
Sparsity at: 0.49998323497854075
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9018 - val_loss: 0.8225 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.         -0.
  0.22839575]
Sparsity at: 0.49998323497854075
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9018 - val_loss: 0.8231 - val_accuracy: 0.9046
[ 0.         0.         0.        ... -0.        -0.         0.2281949]
Sparsity at: 0.49998323497854075
Epoch 36/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9018 - val_loss: 0.8220 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.         -0.
  0.22857745]
Sparsity at: 0.49998323497854075
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9018 - val_loss: 0.8226 - val_accuracy: 0.9040
[ 0.          0.          0.         ... -0.         -0.
  0.22819276]
Sparsity at: 0.49998323497854075
Epoch 38/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9013 - val_loss: 0.8224 - val_accuracy: 0.9047
[ 0.         0.         0.        ... -0.        -0.         0.2283566]
Sparsity at: 0.49998323497854075
Epoch 39/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8388 - accuracy: 0.9019 - val_loss: 0.8228 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.         -0.
  0.22793572]
Sparsity at: 0.49998323497854075
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8389 - accuracy: 0.9020 - val_loss: 0.8228 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.22830379]
Sparsity at: 0.49998323497854075
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9015 - val_loss: 0.8222 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.         -0.
  0.22834966]
Sparsity at: 0.49998323497854075
Epoch 42/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8389 - accuracy: 0.9021 - val_loss: 0.8225 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.         -0.
  0.22823043]
Sparsity at: 0.49998323497854075
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8225 - val_accuracy: 0.9050
[ 0.          0.          0.         ... -0.         -0.
  0.22847441]
Sparsity at: 0.49998323497854075
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9015 - val_loss: 0.8225 - val_accuracy: 0.9051
[ 0.          0.          0.         ... -0.         -0.
  0.22821824]
Sparsity at: 0.49998323497854075
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9018 - val_loss: 0.8224 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.         -0.
  0.22794549]
Sparsity at: 0.49998323497854075
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9017 - val_loss: 0.8227 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.         -0.
  0.22805488]
Sparsity at: 0.49998323497854075
Epoch 47/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9016 - val_loss: 0.8221 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.         -0.
  0.22819583]
Sparsity at: 0.49998323497854075
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9017 - val_loss: 0.8226 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.         -0.
  0.22844236]
Sparsity at: 0.49998323497854075
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8389 - accuracy: 0.9017 - val_loss: 0.8227 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.         -0.
  0.22832353]
Sparsity at: 0.49998323497854075
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9019 - val_loss: 0.8227 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.22814119]
Sparsity at: 0.49998323497854075
Epoch 51/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8650 - accuracy: 0.9018 - val_loss: 0.8420 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.         -0.
  0.26181144]
Sparsity at: 0.6458724517167382
Epoch 52/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8599 - accuracy: 0.9024 - val_loss: 0.8409 - val_accuracy: 0.9070
[ 0.         0.         0.        ... -0.        -0.         0.2705809]
Sparsity at: 0.6458724517167382
Epoch 53/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.9026 - val_loss: 0.8404 - val_accuracy: 0.9064
[ 0.         0.         0.        ... -0.        -0.         0.2765177]
Sparsity at: 0.6458724517167382
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.9029 - val_loss: 0.8401 - val_accuracy: 0.9065
[ 0.        0.        0.       ... -0.       -0.        0.280353]
Sparsity at: 0.6458724517167382
Epoch 55/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8588 - accuracy: 0.9026 - val_loss: 0.8402 - val_accuracy: 0.9067
[ 0.        0.        0.       ... -0.       -0.        0.282932]
Sparsity at: 0.6458724517167382
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9027 - val_loss: 0.8402 - val_accuracy: 0.9065
[ 0.         0.         0.        ... -0.        -0.         0.2848979]
Sparsity at: 0.6458724517167382
Epoch 57/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8587 - accuracy: 0.9025 - val_loss: 0.8401 - val_accuracy: 0.9066
[ 0.         0.         0.        ... -0.        -0.         0.2859914]
Sparsity at: 0.6458724517167382
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9025 - val_loss: 0.8402 - val_accuracy: 0.9065
[ 0.         0.         0.        ... -0.        -0.         0.2868721]
Sparsity at: 0.6458724517167382
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9026 - val_loss: 0.8399 - val_accuracy: 0.9064
[ 0.          0.          0.         ... -0.         -0.
  0.28774148]
Sparsity at: 0.6458724517167382
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9027 - val_loss: 0.8400 - val_accuracy: 0.9066
[ 0.         0.         0.        ... -0.        -0.         0.2882026]
Sparsity at: 0.6458724517167382
Epoch 61/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9027 - val_loss: 0.8399 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.         -0.
  0.28876236]
Sparsity at: 0.6458724517167382
Epoch 62/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8587 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.         -0.
  0.28895956]
Sparsity at: 0.6458724517167382
Epoch 63/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9025 - val_loss: 0.8398 - val_accuracy: 0.9063
[ 0.         0.         0.        ... -0.        -0.         0.2891049]
Sparsity at: 0.6458724517167382
Epoch 64/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9026 - val_loss: 0.8400 - val_accuracy: 0.9065
[ 0.          0.          0.         ... -0.         -0.
  0.28957662]
Sparsity at: 0.6458724517167382
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9027 - val_loss: 0.8401 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.         -0.
  0.28979963]
Sparsity at: 0.6458724517167382
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9028 - val_loss: 0.8398 - val_accuracy: 0.9066
[ 0.         0.         0.        ... -0.        -0.         0.2899965]
Sparsity at: 0.6458724517167382
Epoch 67/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9025 - val_loss: 0.8398 - val_accuracy: 0.9070
[ 0.         0.         0.        ... -0.        -0.         0.2901228]
Sparsity at: 0.6458724517167382
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9027 - val_loss: 0.8398 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.         -0.
  0.29049462]
Sparsity at: 0.6458724517167382
Epoch 69/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9028 - val_loss: 0.8399 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.         -0.
  0.29047298]
Sparsity at: 0.6458724517167382
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9026 - val_loss: 0.8398 - val_accuracy: 0.9068
[ 0.          0.          0.         ... -0.         -0.
  0.29082203]
Sparsity at: 0.6458724517167382
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9028 - val_loss: 0.8396 - val_accuracy: 0.9071
[ 0.         0.         0.        ... -0.        -0.         0.2908758]
Sparsity at: 0.6458724517167382
Epoch 72/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9067
[ 0.          0.          0.         ... -0.         -0.
  0.29115957]
Sparsity at: 0.6458724517167382
Epoch 73/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8585 - accuracy: 0.9025 - val_loss: 0.8396 - val_accuracy: 0.9069
[ 0.         0.         0.        ... -0.        -0.         0.2911333]
Sparsity at: 0.6458724517167382
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.         -0.
  0.29126108]
Sparsity at: 0.6458724517167382
Epoch 75/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9029 - val_loss: 0.8399 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.         -0.
  0.29105765]
Sparsity at: 0.6458724517167382
Epoch 76/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8396 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.         -0.
  0.29139096]
Sparsity at: 0.6458724517167382
Epoch 77/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8397 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.         -0.
  0.29164252]
Sparsity at: 0.6458724517167382
Epoch 78/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9025 - val_loss: 0.8396 - val_accuracy: 0.9068
[ 0.          0.          0.         ... -0.         -0.
  0.29165566]
Sparsity at: 0.6458724517167382
Epoch 79/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9028 - val_loss: 0.8398 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.         -0.
  0.29187158]
Sparsity at: 0.6458724517167382
Epoch 80/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9027 - val_loss: 0.8399 - val_accuracy: 0.9065
[ 0.          0.          0.         ... -0.         -0.
  0.29169327]
Sparsity at: 0.6458724517167382
Epoch 81/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8397 - val_accuracy: 0.9074
[ 0.          0.          0.         ... -0.         -0.
  0.29183212]
Sparsity at: 0.6458724517167382
Epoch 82/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9029 - val_loss: 0.8397 - val_accuracy: 0.9068
[ 0.          0.          0.         ... -0.         -0.
  0.29182228]
Sparsity at: 0.6458724517167382
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9026 - val_loss: 0.8399 - val_accuracy: 0.9067
[ 0.         0.         0.        ... -0.        -0.         0.2918862]
Sparsity at: 0.6458724517167382
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8397 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.         -0.
  0.29203737]
Sparsity at: 0.6458724517167382
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8395 - val_accuracy: 0.9071
[ 0.         0.         0.        ... -0.        -0.         0.2917743]
Sparsity at: 0.6458724517167382
Epoch 86/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8398 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.         -0.
  0.29214373]
Sparsity at: 0.6458724517167382
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8582 - accuracy: 0.9028 - val_loss: 0.8399 - val_accuracy: 0.9069
[ 0.         0.         0.        ... -0.        -0.         0.2921344]
Sparsity at: 0.6458724517167382
Epoch 88/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9068
[ 0.         0.         0.        ... -0.        -0.         0.2920466]
Sparsity at: 0.6458724517167382
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8397 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.         -0.
  0.29211465]
Sparsity at: 0.6458724517167382
Epoch 90/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8396 - val_accuracy: 0.9071
[ 0.         0.         0.        ... -0.        -0.         0.2922954]
Sparsity at: 0.6458724517167382
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9025 - val_loss: 0.8400 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.         -0.
  0.29225573]
Sparsity at: 0.6458724517167382
Epoch 92/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9028 - val_loss: 0.8397 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.         -0.
  0.29228523]
Sparsity at: 0.6458724517167382
Epoch 93/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8586 - accuracy: 0.9025 - val_loss: 0.8402 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.         -0.
  0.29233763]
Sparsity at: 0.6458724517167382
Epoch 94/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8583 - accuracy: 0.9028 - val_loss: 0.8399 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.         -0.
  0.29241607]
Sparsity at: 0.6458724517167382
Epoch 95/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9028 - val_loss: 0.8399 - val_accuracy: 0.9067
[ 0.       0.       0.      ... -0.      -0.       0.29256]
Sparsity at: 0.6458724517167382
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9025 - val_loss: 0.8398 - val_accuracy: 0.9068
[ 0.          0.          0.         ... -0.         -0.
  0.29275134]
Sparsity at: 0.6458724517167382
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9023 - val_loss: 0.8395 - val_accuracy: 0.9066
[ 0.        0.        0.       ... -0.       -0.        0.292615]
Sparsity at: 0.6458724517167382
Epoch 98/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9025 - val_loss: 0.8398 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.         -0.
  0.29260945]
Sparsity at: 0.6458724517167382
Epoch 99/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8582 - accuracy: 0.9025 - val_loss: 0.8397 - val_accuracy: 0.9074
[ 0.         0.         0.        ... -0.        -0.         0.2926758]
Sparsity at: 0.6458724517167382
Epoch 100/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.         -0.
  0.29269928]
Sparsity at: 0.6458724517167382
Epoch 101/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8987 - accuracy: 0.9000 - val_loss: 0.8758 - val_accuracy: 0.9044
[ 0.         0.         0.        ... -0.        -0.         0.3242109]
Sparsity at: 0.759438707081545
Epoch 102/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8910 - accuracy: 0.9018 - val_loss: 0.8743 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.         -0.
  0.32834464]
Sparsity at: 0.759438707081545
Epoch 103/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8901 - accuracy: 0.9020 - val_loss: 0.8741 - val_accuracy: 0.9044
[ 0.          0.          0.         ... -0.         -0.
  0.32975054]
Sparsity at: 0.759438707081545
Epoch 104/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9020 - val_loss: 0.8736 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.         -0.
  0.33069235]
Sparsity at: 0.759438707081545
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9018 - val_loss: 0.8733 - val_accuracy: 0.9043
[ 0.          0.          0.         ... -0.         -0.
  0.33105958]
Sparsity at: 0.759438707081545
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8895 - accuracy: 0.9015 - val_loss: 0.8734 - val_accuracy: 0.9044
[ 0.          0.          0.         ... -0.         -0.
  0.33167672]
Sparsity at: 0.759438707081545
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8893 - accuracy: 0.9017 - val_loss: 0.8731 - val_accuracy: 0.9037
[ 0.          0.          0.         ... -0.         -0.
  0.33176517]
Sparsity at: 0.759438707081545
Epoch 108/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8891 - accuracy: 0.9018 - val_loss: 0.8728 - val_accuracy: 0.9043
[ 0.         0.         0.        ... -0.        -0.         0.3320303]
Sparsity at: 0.759438707081545
Epoch 109/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8891 - accuracy: 0.9018 - val_loss: 0.8729 - val_accuracy: 0.9040
[ 0.          0.          0.         ... -0.         -0.
  0.33248746]
Sparsity at: 0.759438707081545
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8889 - accuracy: 0.9018 - val_loss: 0.8728 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.         -0.
  0.33279392]
Sparsity at: 0.759438707081545
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8889 - accuracy: 0.9018 - val_loss: 0.8729 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.         -0.
  0.33287236]
Sparsity at: 0.759438707081545
Epoch 112/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9016 - val_loss: 0.8728 - val_accuracy: 0.9040
[ 0.          0.          0.         ... -0.         -0.
  0.33309814]
Sparsity at: 0.759438707081545
Epoch 113/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9017 - val_loss: 0.8723 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.33315548]
Sparsity at: 0.759438707081545
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9019 - val_loss: 0.8726 - val_accuracy: 0.9039
[ 0.         0.         0.        ... -0.        -0.         0.3335356]
Sparsity at: 0.759438707081545
Epoch 115/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.33334437]
Sparsity at: 0.759438707081545
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9040
[ 0.        0.        0.       ... -0.       -0.        0.333639]
Sparsity at: 0.759438707081545
Epoch 117/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9040
[ 0.         0.         0.        ... -0.        -0.         0.3338172]
Sparsity at: 0.759438707081545
Epoch 118/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.33377978]
Sparsity at: 0.759438707081545
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9017 - val_loss: 0.8726 - val_accuracy: 0.9038
[ 0.          0.          0.         ... -0.         -0.
  0.33386356]
Sparsity at: 0.759438707081545
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9043
[ 0.         0.         0.        ... -0.        -0.         0.3342177]
Sparsity at: 0.759438707081545
Epoch 121/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9015 - val_loss: 0.8725 - val_accuracy: 0.9043
[ 0.          0.          0.         ... -0.         -0.
  0.33423054]
Sparsity at: 0.759438707081545
Epoch 122/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.         -0.
  0.33425477]
Sparsity at: 0.759438707081545
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9016 - val_loss: 0.8725 - val_accuracy: 0.9040
[ 0.          0.          0.         ... -0.         -0.
  0.33455327]
Sparsity at: 0.759438707081545
Epoch 124/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9017 - val_loss: 0.8724 - val_accuracy: 0.9040
[ 0.          0.          0.         ... -0.         -0.
  0.33466637]
Sparsity at: 0.759438707081545
Epoch 125/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9041
[ 0.         0.         0.        ... -0.        -0.         0.3346795]
Sparsity at: 0.759438707081545
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9042
[ 0.         0.         0.        ... -0.        -0.         0.3345989]
Sparsity at: 0.759438707081545
Epoch 127/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9043
[ 0.          0.          0.         ... -0.         -0.
  0.33454058]
Sparsity at: 0.759438707081545
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.         -0.
  0.33486763]
Sparsity at: 0.759438707081545
Epoch 129/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.         -0.
  0.33468127]
Sparsity at: 0.759438707081545
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8726 - val_accuracy: 0.9041
[ 0.         0.         0.        ... -0.        -0.         0.3345939]
Sparsity at: 0.759438707081545
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8723 - val_accuracy: 0.9038
[ 0.          0.          0.         ... -0.         -0.
  0.33467358]
Sparsity at: 0.759438707081545
Epoch 132/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8886 - accuracy: 0.9016 - val_loss: 0.8721 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.33478442]
Sparsity at: 0.759438707081545
Epoch 133/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8884 - accuracy: 0.9017 - val_loss: 0.8722 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.33460888]
Sparsity at: 0.759438707081545
Epoch 134/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8884 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9043
[ 0.          0.          0.         ... -0.         -0.
  0.33514607]
Sparsity at: 0.759438707081545
Epoch 135/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8723 - val_accuracy: 0.9038
[ 0.          0.          0.         ... -0.         -0.
  0.33500412]
Sparsity at: 0.759438707081545
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9019 - val_loss: 0.8723 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.         -0.
  0.33499756]
Sparsity at: 0.759438707081545
Epoch 137/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9014 - val_loss: 0.8722 - val_accuracy: 0.9036
[ 0.          0.          0.         ... -0.         -0.
  0.33509544]
Sparsity at: 0.759438707081545
Epoch 138/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9015 - val_loss: 0.8720 - val_accuracy: 0.9037
[ 0.          0.          0.         ... -0.         -0.
  0.33501717]
Sparsity at: 0.759438707081545
Epoch 139/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8723 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.33497882]
Sparsity at: 0.759438707081545
Epoch 140/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8724 - val_accuracy: 0.9043
[ 0.         0.         0.        ... -0.        -0.         0.3349794]
Sparsity at: 0.759438707081545
Epoch 141/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9044
[ 0.         0.         0.        ... -0.        -0.         0.3350159]
Sparsity at: 0.759438707081545
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8724 - val_accuracy: 0.9043
[ 0.         0.         0.        ... -0.        -0.         0.3350516]
Sparsity at: 0.759438707081545
Epoch 143/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8720 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.         -0.
  0.33516055]
Sparsity at: 0.759438707081545
Epoch 144/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8722 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.33523577]
Sparsity at: 0.759438707081545
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9042
[ 0.         0.         0.        ... -0.        -0.         0.3353102]
Sparsity at: 0.759438707081545
Epoch 146/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8722 - val_accuracy: 0.9043
[ 0.         0.         0.        ... -0.        -0.         0.3350531]
Sparsity at: 0.759438707081545
Epoch 147/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8883 - accuracy: 0.9016 - val_loss: 0.8722 - val_accuracy: 0.9037
[ 0.         0.         0.        ... -0.        -0.         0.3351312]
Sparsity at: 0.759438707081545
Epoch 148/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8884 - accuracy: 0.9018 - val_loss: 0.8721 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.         -0.
  0.33521622]
Sparsity at: 0.759438707081545
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8884 - accuracy: 0.9017 - val_loss: 0.8722 - val_accuracy: 0.9038
[ 0.          0.          0.         ... -0.         -0.
  0.33528093]
Sparsity at: 0.759438707081545
Epoch 150/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8722 - val_accuracy: 0.9041
[ 0.          0.          0.         ... -0.         -0.
  0.33523038]
Sparsity at: 0.759438707081545
Epoch 151/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9672 - accuracy: 0.8919 - val_loss: 0.9408 - val_accuracy: 0.8990
[ 0.         0.         0.        ... -0.        -0.         0.3569514]
Sparsity at: 0.8448061963519313
Epoch 152/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9546 - accuracy: 0.8961 - val_loss: 0.9387 - val_accuracy: 0.9000
[ 0.          0.          0.         ... -0.         -0.
  0.36170727]
Sparsity at: 0.8448061963519313
Epoch 153/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9531 - accuracy: 0.8972 - val_loss: 0.9378 - val_accuracy: 0.9006
[ 0.          0.          0.         ... -0.         -0.
  0.36849245]
Sparsity at: 0.8448061963519313
Epoch 154/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9522 - accuracy: 0.8981 - val_loss: 0.9370 - val_accuracy: 0.9006
[ 0.          0.          0.         ... -0.         -0.
  0.37500384]
Sparsity at: 0.8448061963519313
Epoch 155/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9516 - accuracy: 0.8984 - val_loss: 0.9364 - val_accuracy: 0.9013
[ 0.          0.          0.         ... -0.         -0.
  0.38067392]
Sparsity at: 0.8448061963519313
Epoch 156/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9510 - accuracy: 0.8986 - val_loss: 0.9359 - val_accuracy: 0.9018
[ 0.          0.          0.         ... -0.         -0.
  0.38540465]
Sparsity at: 0.8448061963519313
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9506 - accuracy: 0.8989 - val_loss: 0.9357 - val_accuracy: 0.9016
[ 0.          0.          0.         ... -0.         -0.
  0.38882956]
Sparsity at: 0.8448061963519313
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9503 - accuracy: 0.8990 - val_loss: 0.9352 - val_accuracy: 0.9016
[ 0.          0.          0.         ... -0.         -0.
  0.39076683]
Sparsity at: 0.8448061963519313
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9501 - accuracy: 0.8994 - val_loss: 0.9352 - val_accuracy: 0.9020
[ 0.          0.          0.         ... -0.         -0.
  0.39233917]
Sparsity at: 0.8448061963519313
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9498 - accuracy: 0.8996 - val_loss: 0.9351 - val_accuracy: 0.9020
[ 0.          0.          0.         ... -0.         -0.
  0.39321017]
Sparsity at: 0.8448061963519313
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9497 - accuracy: 0.8996 - val_loss: 0.9348 - val_accuracy: 0.9022
[ 0.         0.         0.        ... -0.        -0.         0.3938331]
Sparsity at: 0.8448061963519313
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9495 - accuracy: 0.8999 - val_loss: 0.9346 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.39416364]
Sparsity at: 0.8448061963519313
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9495 - accuracy: 0.8997 - val_loss: 0.9345 - val_accuracy: 0.9021
[ 0.         0.         0.        ... -0.        -0.         0.3942465]
Sparsity at: 0.8448061963519313
Epoch 164/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9493 - accuracy: 0.9002 - val_loss: 0.9343 - val_accuracy: 0.9022
[ 0.          0.          0.         ... -0.         -0.
  0.39422846]
Sparsity at: 0.8448061963519313
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9492 - accuracy: 0.8999 - val_loss: 0.9344 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.39412263]
Sparsity at: 0.8448061963519313
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9492 - accuracy: 0.9001 - val_loss: 0.9343 - val_accuracy: 0.9020
[ 0.          0.          0.         ... -0.         -0.
  0.39411062]
Sparsity at: 0.8448061963519313
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9491 - accuracy: 0.9001 - val_loss: 0.9342 - val_accuracy: 0.9022
[ 0.         0.         0.        ... -0.        -0.         0.3938568]
Sparsity at: 0.8448061963519313
Epoch 168/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9490 - accuracy: 0.9002 - val_loss: 0.9342 - val_accuracy: 0.9024
[ 0.          0.          0.         ... -0.         -0.
  0.39366263]
Sparsity at: 0.8448061963519313
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9490 - accuracy: 0.9001 - val_loss: 0.9340 - val_accuracy: 0.9023
[ 0.          0.          0.         ... -0.         -0.
  0.39358422]
Sparsity at: 0.8448061963519313
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9490 - accuracy: 0.9000 - val_loss: 0.9341 - val_accuracy: 0.9017
[ 0.         0.         0.        ... -0.        -0.         0.3933039]
Sparsity at: 0.8448061963519313
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9489 - accuracy: 0.9002 - val_loss: 0.9341 - val_accuracy: 0.9024
[ 0.        0.        0.       ... -0.       -0.        0.392975]
Sparsity at: 0.8448061963519313
Epoch 172/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9001 - val_loss: 0.9339 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.39265066]
Sparsity at: 0.8448061963519313
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9001 - val_loss: 0.9339 - val_accuracy: 0.9024
[ 0.         0.         0.        ... -0.        -0.         0.3926857]
Sparsity at: 0.8448061963519313
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9001 - val_loss: 0.9338 - val_accuracy: 0.9024
[ 0.         0.         0.        ... -0.        -0.         0.3923594]
Sparsity at: 0.8448061963519313
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9003 - val_loss: 0.9340 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.39229864]
Sparsity at: 0.8448061963519313
Epoch 176/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9004 - val_loss: 0.9337 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.39191854]
Sparsity at: 0.8448061963519313
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9339 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.39191952]
Sparsity at: 0.8448061963519313
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9000 - val_loss: 0.9338 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.39167577]
Sparsity at: 0.8448061963519313
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9000 - val_loss: 0.9338 - val_accuracy: 0.9026
[ 0.         0.         0.        ... -0.        -0.         0.3915152]
Sparsity at: 0.8448061963519313
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9023
[ 0.          0.          0.         ... -0.         -0.
  0.39140016]
Sparsity at: 0.8448061963519313
Epoch 181/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9339 - val_accuracy: 0.9019
[ 0.          0.          0.         ... -0.         -0.
  0.39123145]
Sparsity at: 0.8448061963519313
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9487 - accuracy: 0.9002 - val_loss: 0.9337 - val_accuracy: 0.9022
[ 0.        0.        0.       ... -0.       -0.        0.391031]
Sparsity at: 0.8448061963519313
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9002 - val_loss: 0.9338 - val_accuracy: 0.9029
[ 0.        0.        0.       ... -0.       -0.        0.390863]
Sparsity at: 0.8448061963519313
Epoch 184/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9024
[ 0.         0.         0.        ... -0.        -0.         0.3906115]
Sparsity at: 0.8448061963519313
Epoch 185/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9023
[ 0.          0.          0.         ... -0.         -0.
  0.39056227]
Sparsity at: 0.8448061963519313
Epoch 186/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9001 - val_loss: 0.9339 - val_accuracy: 0.9017
[ 0.          0.          0.         ... -0.         -0.
  0.39052004]
Sparsity at: 0.8448061963519313
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9004 - val_loss: 0.9337 - val_accuracy: 0.9025
[ 0.          0.          0.         ... -0.         -0.
  0.39022812]
Sparsity at: 0.8448061963519313
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9025
[ 0.          0.          0.         ... -0.         -0.
  0.39030516]
Sparsity at: 0.8448061963519313
Epoch 189/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9004 - val_loss: 0.9337 - val_accuracy: 0.9024
[ 0.         0.         0.        ... -0.        -0.         0.3902272]
Sparsity at: 0.8448061963519313
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.39020577]
Sparsity at: 0.8448061963519313
Epoch 191/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9005 - val_loss: 0.9337 - val_accuracy: 0.9025
[ 0.         0.         0.        ... -0.        -0.         0.3899172]
Sparsity at: 0.8448061963519313
Epoch 192/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9002 - val_loss: 0.9337 - val_accuracy: 0.9027
[ 0.          0.          0.         ... -0.         -0.
  0.38981766]
Sparsity at: 0.8448061963519313
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9006 - val_loss: 0.9339 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.38972422]
Sparsity at: 0.8448061963519313
Epoch 194/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9005 - val_loss: 0.9338 - val_accuracy: 0.9024
[ 0.          0.          0.         ... -0.         -0.
  0.38974184]
Sparsity at: 0.8448061963519313
Epoch 195/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9004 - val_loss: 0.9336 - val_accuracy: 0.9024
[ 0.         0.         0.        ... -0.        -0.         0.3893766]
Sparsity at: 0.8448061963519313
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9004 - val_loss: 0.9336 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.38940224]
Sparsity at: 0.8448061963519313
Epoch 197/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9004 - val_loss: 0.9336 - val_accuracy: 0.9020
[ 0.         0.         0.        ... -0.        -0.         0.3893821]
Sparsity at: 0.8448061963519313
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9002 - val_loss: 0.9337 - val_accuracy: 0.9024
[ 0.         0.         0.        ... -0.        -0.         0.3894376]
Sparsity at: 0.8448061963519313
Epoch 199/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9002 - val_loss: 0.9337 - val_accuracy: 0.9021
[ 0.          0.          0.         ... -0.         -0.
  0.38932446]
Sparsity at: 0.8448061963519313
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9337 - val_accuracy: 0.9023
[ 0.          0.          0.         ... -0.         -0.
  0.38929915]
Sparsity at: 0.8448061963519313
Epoch 201/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0491 - accuracy: 0.8890 - val_loss: 1.0147 - val_accuracy: 0.8980
[ 0.          0.          0.         ... -0.         -0.
  0.44045904]
Sparsity at: 0.9059482296137339
Epoch 202/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0282 - accuracy: 0.8952 - val_loss: 1.0124 - val_accuracy: 0.8988
[ 0.          0.          0.         ... -0.         -0.
  0.43820158]
Sparsity at: 0.9059482296137339
Epoch 203/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0269 - accuracy: 0.8955 - val_loss: 1.0118 - val_accuracy: 0.8993
[ 0.          0.          0.         ... -0.         -0.
  0.43797222]
Sparsity at: 0.9059482296137339
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0264 - accuracy: 0.8953 - val_loss: 1.0111 - val_accuracy: 0.8989
[ 0.        0.        0.       ... -0.       -0.        0.437651]
Sparsity at: 0.9059482296137339
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0260 - accuracy: 0.8955 - val_loss: 1.0109 - val_accuracy: 0.8990
[ 0.          0.          0.         ... -0.         -0.
  0.43710318]
Sparsity at: 0.9059482296137339
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0258 - accuracy: 0.8952 - val_loss: 1.0106 - val_accuracy: 0.8985
[ 0.         0.         0.        ... -0.        -0.         0.4365483]
Sparsity at: 0.9059482296137339
Epoch 207/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0256 - accuracy: 0.8953 - val_loss: 1.0104 - val_accuracy: 0.8987
[ 0.          0.          0.         ... -0.         -0.
  0.43554276]
Sparsity at: 0.9059482296137339
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0255 - accuracy: 0.8954 - val_loss: 1.0103 - val_accuracy: 0.8985
[ 0.          0.          0.         ... -0.         -0.
  0.43491262]
Sparsity at: 0.9059482296137339
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0254 - accuracy: 0.8954 - val_loss: 1.0103 - val_accuracy: 0.8987
[ 0.          0.          0.         ... -0.         -0.
  0.43428048]
Sparsity at: 0.9059482296137339
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0253 - accuracy: 0.8954 - val_loss: 1.0101 - val_accuracy: 0.8981
[ 0.          0.          0.         ... -0.         -0.
  0.43354118]
Sparsity at: 0.9059482296137339
Epoch 211/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0252 - accuracy: 0.8953 - val_loss: 1.0100 - val_accuracy: 0.8980
[ 0.         0.         0.        ... -0.        -0.         0.4328327]
Sparsity at: 0.9059482296137339
Epoch 212/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0251 - accuracy: 0.8955 - val_loss: 1.0100 - val_accuracy: 0.8981
[ 0.         0.         0.        ... -0.        -0.         0.4324414]
Sparsity at: 0.9059482296137339
Epoch 213/500
235/235 [==============================] - 2s 10ms/step - loss: 1.0251 - accuracy: 0.8955 - val_loss: 1.0100 - val_accuracy: 0.8981
[ 0.          0.          0.         ... -0.         -0.
  0.43207797]
Sparsity at: 0.9059482296137339
Epoch 214/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0250 - accuracy: 0.8954 - val_loss: 1.0100 - val_accuracy: 0.8982
[ 0.          0.          0.         ... -0.         -0.
  0.43149152]
Sparsity at: 0.9059482296137339
Epoch 215/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0250 - accuracy: 0.8953 - val_loss: 1.0098 - val_accuracy: 0.8982
[ 0.         0.         0.        ... -0.        -0.         0.4309252]
Sparsity at: 0.9059482296137339
Epoch 216/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0250 - accuracy: 0.8952 - val_loss: 1.0097 - val_accuracy: 0.8979
[ 0.          0.          0.         ... -0.         -0.
  0.43057793]
Sparsity at: 0.9059482296137339
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0249 - accuracy: 0.8954 - val_loss: 1.0096 - val_accuracy: 0.8979
[ 0.         0.         0.        ... -0.        -0.         0.4300944]
Sparsity at: 0.9059482296137339
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0249 - accuracy: 0.8952 - val_loss: 1.0097 - val_accuracy: 0.8978
[ 0.          0.          0.         ... -0.         -0.
  0.43006754]
Sparsity at: 0.9059482296137339
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0249 - accuracy: 0.8953 - val_loss: 1.0097 - val_accuracy: 0.8983
[ 0.          0.          0.         ... -0.         -0.
  0.42986378]
Sparsity at: 0.9059482296137339
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8953 - val_loss: 1.0096 - val_accuracy: 0.8978
[ 0.         0.         0.        ... -0.        -0.         0.4296145]
Sparsity at: 0.9059482296137339
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8954 - val_loss: 1.0096 - val_accuracy: 0.8978
[ 0.          0.          0.         ... -0.         -0.
  0.42912394]
Sparsity at: 0.9059482296137339
Epoch 222/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8955 - val_loss: 1.0095 - val_accuracy: 0.8980
[ 0.          0.          0.         ... -0.         -0.
  0.42883182]
Sparsity at: 0.9059482296137339
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8952 - val_loss: 1.0096 - val_accuracy: 0.8977
[ 0.          0.          0.         ... -0.         -0.
  0.42854697]
Sparsity at: 0.9059482296137339
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8953 - val_loss: 1.0096 - val_accuracy: 0.8981
[ 0.         0.         0.        ... -0.        -0.         0.4285308]
Sparsity at: 0.9059482296137339
Epoch 225/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8954 - val_loss: 1.0095 - val_accuracy: 0.8980
[ 0.         0.         0.        ... -0.        -0.         0.4283238]
Sparsity at: 0.9059482296137339
Epoch 226/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8954 - val_loss: 1.0096 - val_accuracy: 0.8980
[ 0.       0.       0.      ... -0.      -0.       0.42821]
Sparsity at: 0.9059482296137339
Epoch 227/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8953 - val_loss: 1.0096 - val_accuracy: 0.8983
[ 0.         0.         0.        ... -0.        -0.         0.4280149]
Sparsity at: 0.9059482296137339
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8953 - val_loss: 1.0095 - val_accuracy: 0.8979
[ 0.         0.         0.        ... -0.        -0.         0.4277269]
Sparsity at: 0.9059482296137339
Epoch 229/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8978
[ 0.          0.          0.         ... -0.         -0.
  0.42757627]
Sparsity at: 0.9059482296137339
Epoch 230/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8953 - val_loss: 1.0095 - val_accuracy: 0.8979
[ 0.          0.          0.         ... -0.         -0.
  0.42745447]
Sparsity at: 0.9059482296137339
Epoch 231/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0095 - val_accuracy: 0.8974
[ 0.         0.         0.        ... -0.        -0.         0.4275424]
Sparsity at: 0.9059482296137339
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8980
[ 0.         0.         0.        ... -0.        -0.         0.4271754]
Sparsity at: 0.9059482296137339
Epoch 233/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8977
[ 0.         0.         0.        ... -0.        -0.         0.4270703]
Sparsity at: 0.9059482296137339
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8951 - val_loss: 1.0094 - val_accuracy: 0.8978
[ 0.          0.          0.         ... -0.         -0.
  0.42695278]
Sparsity at: 0.9059482296137339
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8981
[ 0.         0.         0.        ... -0.        -0.         0.4267202]
Sparsity at: 0.9059482296137339
Epoch 236/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8953 - val_loss: 1.0094 - val_accuracy: 0.8977
[ 0.         0.         0.        ... -0.        -0.         0.4267205]
Sparsity at: 0.9059482296137339
Epoch 237/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8980
[ 0.          0.          0.         ... -0.         -0.
  0.42663738]
Sparsity at: 0.9059482296137339
Epoch 238/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8982
[ 0.          0.          0.         ... -0.         -0.
  0.42656147]
Sparsity at: 0.9059482296137339
Epoch 239/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8950 - val_loss: 1.0092 - val_accuracy: 0.8981
[ 0.         0.         0.        ... -0.        -0.         0.4262798]
Sparsity at: 0.9059482296137339
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0093 - val_accuracy: 0.8980
[ 0.          0.          0.         ... -0.         -0.
  0.42621666]
Sparsity at: 0.9059482296137339
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0092 - val_accuracy: 0.8978
[ 0.          0.          0.         ... -0.         -0.
  0.42633358]
Sparsity at: 0.9059482296137339
Epoch 242/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0093 - val_accuracy: 0.8981
[ 0.          0.          0.         ... -0.         -0.
  0.42596006]
Sparsity at: 0.9059482296137339
Epoch 243/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8952 - val_loss: 1.0092 - val_accuracy: 0.8983
[ 0.         0.         0.        ... -0.        -0.         0.4261564]
Sparsity at: 0.9059482296137339
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8950 - val_loss: 1.0091 - val_accuracy: 0.8982
[ 0.         0.         0.        ... -0.        -0.         0.4260047]
Sparsity at: 0.9059482296137339
Epoch 245/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0092 - val_accuracy: 0.8981
[ 0.          0.          0.         ... -0.         -0.
  0.42587775]
Sparsity at: 0.9059482296137339
Epoch 246/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8948 - val_loss: 1.0092 - val_accuracy: 0.8983
[ 0.          0.          0.         ... -0.         -0.
  0.42569843]
Sparsity at: 0.9059482296137339
Epoch 247/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8950 - val_loss: 1.0093 - val_accuracy: 0.8980
[ 0.          0.          0.         ... -0.         -0.
  0.42559257]
Sparsity at: 0.9059482296137339
Epoch 248/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0092 - val_accuracy: 0.8982
[ 0.          0.          0.         ... -0.         -0.
  0.42568636]
Sparsity at: 0.9059482296137339
Epoch 249/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0092 - val_accuracy: 0.8983
[ 0.          0.          0.         ... -0.         -0.
  0.42566693]
Sparsity at: 0.9059482296137339
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0245 - accuracy: 0.8949 - val_loss: 1.0091 - val_accuracy: 0.8981
[ 0.          0.          0.         ... -0.         -0.
  0.42551887]
Sparsity at: 0.9059482296137339
Epoch 251/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2078 - accuracy: 0.8616 - val_loss: 1.1473 - val_accuracy: 0.8788
[ 0.          0.          0.         ...  0.         -0.
  0.68105495]
Sparsity at: 0.9468716469957081
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1674 - accuracy: 0.8744 - val_loss: 1.1415 - val_accuracy: 0.8812
[ 0.         0.         0.        ...  0.        -0.         0.7017333]
Sparsity at: 0.9468716469957081
Epoch 253/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1634 - accuracy: 0.8748 - val_loss: 1.1392 - val_accuracy: 0.8819
[ 0.         0.         0.        ...  0.        -0.         0.7052732]
Sparsity at: 0.9468716469957081
Epoch 254/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1616 - accuracy: 0.8751 - val_loss: 1.1381 - val_accuracy: 0.8818
[ 0.         0.         0.        ...  0.        -0.         0.7065625]
Sparsity at: 0.9468716469957081
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1605 - accuracy: 0.8759 - val_loss: 1.1373 - val_accuracy: 0.8822
[ 0.         0.         0.        ...  0.        -0.         0.7076424]
Sparsity at: 0.9468716469957081
Epoch 256/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1597 - accuracy: 0.8759 - val_loss: 1.1369 - val_accuracy: 0.8819
[ 0.         0.         0.        ...  0.        -0.         0.7085091]
Sparsity at: 0.9468716469957081
Epoch 257/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1593 - accuracy: 0.8758 - val_loss: 1.1366 - val_accuracy: 0.8821
[ 0.          0.          0.         ...  0.         -0.
  0.70922065]
Sparsity at: 0.9468716469957081
Epoch 258/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1590 - accuracy: 0.8756 - val_loss: 1.1364 - val_accuracy: 0.8815
[ 0.          0.          0.         ...  0.         -0.
  0.70972717]
Sparsity at: 0.9468716469957081
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1589 - accuracy: 0.8754 - val_loss: 1.1361 - val_accuracy: 0.8819
[ 0.          0.          0.         ...  0.         -0.
  0.71010655]
Sparsity at: 0.9468716469957081
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1587 - accuracy: 0.8755 - val_loss: 1.1361 - val_accuracy: 0.8813
[ 0.          0.          0.         ...  0.         -0.
  0.71046257]
Sparsity at: 0.9468716469957081
Epoch 261/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1585 - accuracy: 0.8756 - val_loss: 1.1360 - val_accuracy: 0.8814
[ 0.          0.          0.         ...  0.         -0.
  0.71066684]
Sparsity at: 0.9468716469957081
Epoch 262/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1584 - accuracy: 0.8755 - val_loss: 1.1359 - val_accuracy: 0.8815
[ 0.         0.         0.        ...  0.        -0.         0.7108232]
Sparsity at: 0.9468716469957081
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1584 - accuracy: 0.8755 - val_loss: 1.1359 - val_accuracy: 0.8813
[ 0.          0.          0.         ...  0.         -0.
  0.71093494]
Sparsity at: 0.9468716469957081
Epoch 264/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1583 - accuracy: 0.8756 - val_loss: 1.1358 - val_accuracy: 0.8813
[ 0.          0.          0.         ...  0.         -0.
  0.71103567]
Sparsity at: 0.9468716469957081
Epoch 265/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1583 - accuracy: 0.8755 - val_loss: 1.1358 - val_accuracy: 0.8815
[ 0.          0.          0.         ...  0.         -0.
  0.71122766]
Sparsity at: 0.9468716469957081
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1582 - accuracy: 0.8755 - val_loss: 1.1358 - val_accuracy: 0.8817
[ 0.          0.          0.         ...  0.         -0.
  0.71111274]
Sparsity at: 0.9468716469957081
Epoch 267/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1582 - accuracy: 0.8757 - val_loss: 1.1358 - val_accuracy: 0.8815
[ 0.         0.         0.        ...  0.        -0.         0.7110791]
Sparsity at: 0.9468716469957081
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8755 - val_loss: 1.1357 - val_accuracy: 0.8814
[ 0.          0.          0.         ...  0.         -0.
  0.71092844]
Sparsity at: 0.9468716469957081
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8816
[ 0.         0.         0.        ...  0.        -0.         0.7109319]
Sparsity at: 0.9468716469957081
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1358 - val_accuracy: 0.8818
[ 0.          0.          0.         ...  0.         -0.
  0.71097296]
Sparsity at: 0.9468716469957081
Epoch 271/500
235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8820
[ 0.        0.        0.       ...  0.       -0.        0.710939]
Sparsity at: 0.9468716469957081
Epoch 272/500
235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8818
[ 0.          0.          0.         ...  0.         -0.
  0.71078634]
Sparsity at: 0.9468716469957081
Epoch 273/500
235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8818
[ 0.          0.          0.         ...  0.         -0.
  0.71088874]
Sparsity at: 0.9468716469957081
Epoch 274/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8819
[ 0.         0.         0.        ...  0.        -0.         0.7109329]
Sparsity at: 0.9468716469957081
Epoch 275/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8755 - val_loss: 1.1357 - val_accuracy: 0.8820
[ 0.          0.          0.         ...  0.         -0.
  0.71085817]
Sparsity at: 0.9468716469957081
Epoch 276/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8819
[ 0.         0.         0.        ...  0.        -0.         0.7106811]
Sparsity at: 0.9468716469957081
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1356 - val_accuracy: 0.8820
[ 0.          0.          0.         ...  0.         -0.
  0.71067774]
Sparsity at: 0.9468716469957081
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8819
[ 0.         0.         0.        ...  0.        -0.         0.7104908]
Sparsity at: 0.9468716469957081
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8758 - val_loss: 1.1357 - val_accuracy: 0.8820
[ 0.         0.         0.        ...  0.        -0.         0.7106232]
Sparsity at: 0.9468716469957081
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8821
[ 0.         0.         0.        ...  0.        -0.         0.7105501]
Sparsity at: 0.9468716469957081
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1356 - val_accuracy: 0.8822
[ 0.          0.          0.         ...  0.         -0.
  0.71067584]
Sparsity at: 0.9468716469957081
Epoch 282/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8756 - val_loss: 1.1356 - val_accuracy: 0.8821
[ 0.         0.         0.        ...  0.        -0.         0.7104435]
Sparsity at: 0.9468716469957081
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8820
[ 0.         0.         0.        ...  0.        -0.         0.7104664]
Sparsity at: 0.9468716469957081
Epoch 284/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8758 - val_loss: 1.1357 - val_accuracy: 0.8820
[ 0.        0.        0.       ...  0.       -0.        0.710571]
Sparsity at: 0.9468716469957081
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8821
[ 0.          0.          0.         ...  0.         -0.
  0.71033967]
Sparsity at: 0.9468716469957081
Epoch 286/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8755 - val_loss: 1.1357 - val_accuracy: 0.8821
[ 0.         0.         0.        ...  0.        -0.         0.7105198]
Sparsity at: 0.9468716469957081
Epoch 287/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8757 - val_loss: 1.1356 - val_accuracy: 0.8822
[ 0.         0.         0.        ...  0.        -0.         0.7103677]
Sparsity at: 0.9468716469957081
Epoch 288/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8757 - val_loss: 1.1356 - val_accuracy: 0.8824
[ 0.          0.          0.         ...  0.         -0.
  0.71035534]
Sparsity at: 0.9468716469957081
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8756 - val_loss: 1.1356 - val_accuracy: 0.8824
[ 0.        0.        0.       ...  0.       -0.        0.710402]
Sparsity at: 0.9468716469957081
Epoch 290/500
235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8823
[ 0.          0.          0.         ...  0.         -0.
  0.71020424]
Sparsity at: 0.9468716469957081
Epoch 291/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1581 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8822
[ 0.         0.         0.        ...  0.        -0.         0.7104156]
Sparsity at: 0.9468716469957081
Epoch 292/500
235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8820
[ 0.         0.         0.        ...  0.        -0.         0.7103795]
Sparsity at: 0.9468716469957081
Epoch 293/500
235/235 [==============================] - 3s 11ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8821
[ 0.          0.          0.         ...  0.         -0.
  0.71035546]
Sparsity at: 0.9468716469957081
Epoch 294/500
235/235 [==============================] - 3s 12ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8823
[ 0.         0.         0.        ...  0.        -0.         0.7101318]
Sparsity at: 0.9468716469957081
Epoch 295/500
235/235 [==============================] - 3s 12ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8824
[ 0.         0.         0.        ...  0.        -0.         0.7100907]
Sparsity at: 0.9468716469957081
Epoch 296/500
235/235 [==============================] - 3s 11ms/step - loss: 1.1580 - accuracy: 0.8759 - val_loss: 1.1356 - val_accuracy: 0.8824
[ 0.          0.          0.         ...  0.         -0.
  0.71015835]
Sparsity at: 0.9468716469957081
Epoch 297/500
235/235 [==============================] - 3s 11ms/step - loss: 1.1580 - accuracy: 0.8759 - val_loss: 1.1356 - val_accuracy: 0.8824
[ 0.          0.          0.         ...  0.         -0.
  0.71022433]
Sparsity at: 0.9468716469957081
Epoch 298/500
235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8759 - val_loss: 1.1356 - val_accuracy: 0.8822
[ 0.          0.          0.         ...  0.         -0.
  0.71004605]
Sparsity at: 0.9468716469957081
Epoch 299/500
235/235 [==============================] - 3s 12ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1355 - val_accuracy: 0.8825
[ 0.         0.         0.        ...  0.        -0.         0.7101235]
Sparsity at: 0.9468716469957081
Epoch 300/500
235/235 [==============================] - 2s 10ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8826
[ 0.          0.          0.         ...  0.         -0.
  0.71020883]
Sparsity at: 0.9468716469957081
Epoch 301/500
235/235 [==============================] - 3s 11ms/step - loss: 1.5396 - accuracy: 0.7085 - val_loss: 1.4520 - val_accuracy: 0.7639
[ 0.         0.         0.        ...  0.        -0.         0.8391014]
Sparsity at: 0.9717844688841202
Epoch 302/500
235/235 [==============================] - 3s 11ms/step - loss: 1.4530 - accuracy: 0.7676 - val_loss: 1.4319 - val_accuracy: 0.7773
[ 0.          0.          0.         ...  0.         -0.
  0.84345615]
Sparsity at: 0.9717844688841202
Epoch 303/500
235/235 [==============================] - 3s 11ms/step - loss: 1.4429 - accuracy: 0.7737 - val_loss: 1.4264 - val_accuracy: 0.7798
[ 0.         0.         0.        ...  0.        -0.         0.8446345]
Sparsity at: 0.9717844688841202
Epoch 304/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4394 - accuracy: 0.7755 - val_loss: 1.4241 - val_accuracy: 0.7805
[ 0.         0.         0.        ...  0.        -0.         0.8456495]
Sparsity at: 0.9717844688841202
Epoch 305/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4378 - accuracy: 0.7762 - val_loss: 1.4228 - val_accuracy: 0.7808
[ 0.          0.          0.         ...  0.         -0.
  0.84632176]
Sparsity at: 0.9717844688841202
Epoch 306/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4368 - accuracy: 0.7767 - val_loss: 1.4221 - val_accuracy: 0.7810
[ 0.          0.          0.         ...  0.         -0.
  0.84689754]
Sparsity at: 0.9717844688841202
Epoch 307/500
235/235 [==============================] - 2s 11ms/step - loss: 1.4362 - accuracy: 0.7770 - val_loss: 1.4217 - val_accuracy: 0.7814
[ 0.         0.         0.        ...  0.        -0.         0.8471032]
Sparsity at: 0.9717844688841202
Epoch 308/500
235/235 [==============================] - 3s 11ms/step - loss: 1.4357 - accuracy: 0.7771 - val_loss: 1.4213 - val_accuracy: 0.7813
[ 0.         0.         0.        ...  0.        -0.         0.8475388]
Sparsity at: 0.9717844688841202
Epoch 309/500
235/235 [==============================] - 3s 11ms/step - loss: 1.4353 - accuracy: 0.7771 - val_loss: 1.4210 - val_accuracy: 0.7816
[ 0.         0.         0.        ...  0.        -0.         0.8478986]
Sparsity at: 0.9717844688841202
Epoch 310/500
235/235 [==============================] - 3s 11ms/step - loss: 1.4351 - accuracy: 0.7774 - val_loss: 1.4208 - val_accuracy: 0.7817
[ 0.         0.         0.        ...  0.        -0.         0.8479132]
Sparsity at: 0.9717844688841202
Epoch 311/500
235/235 [==============================] - 3s 11ms/step - loss: 1.4349 - accuracy: 0.7775 - val_loss: 1.4207 - val_accuracy: 0.7818
[ 0.          0.          0.         ...  0.         -0.
  0.84826994]
Sparsity at: 0.9717844688841202
Epoch 312/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4347 - accuracy: 0.7776 - val_loss: 1.4206 - val_accuracy: 0.7819
[ 0.          0.          0.         ...  0.         -0.
  0.84840566]
Sparsity at: 0.9717844688841202
Epoch 313/500
235/235 [==============================] - 3s 11ms/step - loss: 1.4346 - accuracy: 0.7774 - val_loss: 1.4204 - val_accuracy: 0.7817
[ 0.         0.         0.        ...  0.        -0.         0.8484093]
Sparsity at: 0.9717844688841202
Epoch 314/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4345 - accuracy: 0.7776 - val_loss: 1.4203 - val_accuracy: 0.7819
[ 0.          0.          0.         ...  0.         -0.
  0.84843224]
Sparsity at: 0.9717844688841202
Epoch 315/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4344 - accuracy: 0.7775 - val_loss: 1.4202 - val_accuracy: 0.7820
[ 0.          0.          0.         ...  0.         -0.
  0.84858185]
Sparsity at: 0.9717844688841202
Epoch 316/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4343 - accuracy: 0.7775 - val_loss: 1.4202 - val_accuracy: 0.7819
[ 0.         0.         0.        ...  0.        -0.         0.8484623]
Sparsity at: 0.9717844688841202
Epoch 317/500
235/235 [==============================] - 2s 11ms/step - loss: 1.4343 - accuracy: 0.7776 - val_loss: 1.4202 - val_accuracy: 0.7818
[ 0.          0.          0.         ...  0.         -0.
  0.84858584]
Sparsity at: 0.9717844688841202
Epoch 318/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4342 - accuracy: 0.7777 - val_loss: 1.4200 - val_accuracy: 0.7822
[ 0.         0.         0.        ...  0.        -0.         0.8484583]
Sparsity at: 0.9717844688841202
Epoch 319/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4342 - accuracy: 0.7776 - val_loss: 1.4200 - val_accuracy: 0.7819
[ 0.          0.          0.         ...  0.         -0.
  0.84858406]
Sparsity at: 0.9717844688841202
Epoch 320/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4341 - accuracy: 0.7775 - val_loss: 1.4199 - val_accuracy: 0.7820
[ 0.          0.          0.         ...  0.         -0.
  0.84860015]
Sparsity at: 0.9717844688841202
Epoch 321/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4341 - accuracy: 0.7775 - val_loss: 1.4200 - val_accuracy: 0.7822
[ 0.         0.         0.        ...  0.        -0.         0.8487723]
Sparsity at: 0.9717844688841202
Epoch 322/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4341 - accuracy: 0.7776 - val_loss: 1.4199 - val_accuracy: 0.7823
[ 0.         0.         0.        ...  0.        -0.         0.8485342]
Sparsity at: 0.9717844688841202
Epoch 323/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4341 - accuracy: 0.7777 - val_loss: 1.4199 - val_accuracy: 0.7825
[ 0.          0.          0.         ...  0.         -0.
  0.84870464]
Sparsity at: 0.9717844688841202
Epoch 324/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4199 - val_accuracy: 0.7826
[ 0.          0.          0.         ...  0.         -0.
  0.84863997]
Sparsity at: 0.9717844688841202
Epoch 325/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7777 - val_loss: 1.4198 - val_accuracy: 0.7828
[ 0.         0.         0.        ...  0.        -0.         0.8483867]
Sparsity at: 0.9717844688841202
Epoch 326/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4199 - val_accuracy: 0.7826
[ 0.          0.          0.         ...  0.         -0.
  0.84857863]
Sparsity at: 0.9717844688841202
Epoch 327/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7827
[ 0.          0.          0.         ...  0.         -0.
  0.84858686]
Sparsity at: 0.9717844688841202
Epoch 328/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7777 - val_loss: 1.4198 - val_accuracy: 0.7828
[ 0.         0.         0.        ...  0.        -0.         0.8484626]
Sparsity at: 0.9717844688841202
Epoch 329/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7827
[ 0.          0.          0.         ...  0.         -0.
  0.84849095]
Sparsity at: 0.9717844688841202
Epoch 330/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4199 - val_accuracy: 0.7829
[ 0.         0.         0.        ...  0.        -0.         0.8485646]
Sparsity at: 0.9717844688841202
Epoch 331/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7827
[ 0.          0.          0.         ...  0.         -0.
  0.84840494]
Sparsity at: 0.9717844688841202
Epoch 332/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7829
[ 0.          0.          0.         ...  0.         -0.
  0.84838897]
Sparsity at: 0.9717844688841202
Epoch 333/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7775 - val_loss: 1.4197 - val_accuracy: 0.7827
[ 0.         0.         0.        ...  0.        -0.         0.8484002]
Sparsity at: 0.9717844688841202
Epoch 334/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4198 - val_accuracy: 0.7828
[ 0.         0.         0.        ...  0.        -0.         0.8484606]
Sparsity at: 0.9717844688841202
Epoch 335/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4197 - val_accuracy: 0.7828
[ 0.         0.         0.        ...  0.        -0.         0.8483319]
Sparsity at: 0.9717844688841202
Epoch 336/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4197 - val_accuracy: 0.7828
[ 0.         0.         0.        ...  0.        -0.         0.8483831]
Sparsity at: 0.9717844688841202
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7828
[ 0.         0.         0.        ...  0.        -0.         0.8483909]
Sparsity at: 0.9717844688841202
Epoch 338/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4197 - val_accuracy: 0.7830
[ 0.        0.        0.       ...  0.       -0.        0.848298]
Sparsity at: 0.9717844688841202
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4198 - val_accuracy: 0.7830
[ 0.          0.          0.         ...  0.         -0.
  0.84855366]
Sparsity at: 0.9717844688841202
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7830
[ 0.         0.         0.        ...  0.        -0.         0.8484151]
Sparsity at: 0.9717844688841202
Epoch 341/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4197 - val_accuracy: 0.7829
[ 0.          0.          0.         ...  0.         -0.
  0.84838897]
Sparsity at: 0.9717844688841202
Epoch 342/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7830
[ 0.          0.          0.         ...  0.         -0.
  0.84850764]
Sparsity at: 0.9717844688841202
Epoch 343/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7831
[ 0.          0.          0.         ...  0.         -0.
  0.84846354]
Sparsity at: 0.9717844688841202
Epoch 344/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7779 - val_loss: 1.4198 - val_accuracy: 0.7830
[ 0.         0.         0.        ...  0.        -0.         0.8486075]
Sparsity at: 0.9717844688841202
Epoch 345/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4197 - val_accuracy: 0.7831
[ 0.         0.         0.        ...  0.        -0.         0.8484398]
Sparsity at: 0.9717844688841202
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4197 - val_accuracy: 0.7829
[ 0.          0.          0.         ...  0.         -0.
  0.84838116]
Sparsity at: 0.9717844688841202
Epoch 347/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7828
[ 0.      0.      0.     ...  0.     -0.      0.8483]
Sparsity at: 0.9717844688841202
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7831
[ 0.         0.         0.        ...  0.        -0.         0.8484819]
Sparsity at: 0.9717844688841202
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7829
[ 0.         0.         0.        ...  0.        -0.         0.8483743]
Sparsity at: 0.9717844688841202
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4197 - val_accuracy: 0.7829
[ 0.         0.         0.        ...  0.        -0.         0.8482588]
Sparsity at: 0.9717844688841202
Epoch 351/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7362 - accuracy: 0.5803 - val_loss: 1.6912 - val_accuracy: 0.5809
[ 0.         0.         0.        ...  0.        -0.         0.8864601]
Sparsity at: 0.9845426502145923
Epoch 352/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6842 - accuracy: 0.6015 - val_loss: 1.6785 - val_accuracy: 0.6037
[ 0.          0.          0.         ...  0.         -0.
  0.88791704]
Sparsity at: 0.9845426502145923
Epoch 353/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6782 - accuracy: 0.6061 - val_loss: 1.6759 - val_accuracy: 0.6037
[ 0.          0.          0.         ...  0.         -0.
  0.88857573]
Sparsity at: 0.9845426502145923
Epoch 354/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6767 - accuracy: 0.6061 - val_loss: 1.6750 - val_accuracy: 0.6035
[ 0.        0.        0.       ...  0.       -0.        0.889125]
Sparsity at: 0.9845426502145923
Epoch 355/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6762 - accuracy: 0.6057 - val_loss: 1.6747 - val_accuracy: 0.6037
[ 0.         0.         0.        ...  0.        -0.         0.8896398]
Sparsity at: 0.9845426502145923
Epoch 356/500
235/235 [==============================] - 3s 11ms/step - loss: 1.6759 - accuracy: 0.6057 - val_loss: 1.6744 - val_accuracy: 0.6036
[ 0.         0.         0.        ...  0.        -0.         0.8902248]
Sparsity at: 0.9845426502145923
Epoch 357/500
235/235 [==============================] - 3s 11ms/step - loss: 1.6758 - accuracy: 0.6057 - val_loss: 1.6744 - val_accuracy: 0.6037
[ 0.         0.         0.        ...  0.        -0.         0.8906863]
Sparsity at: 0.9845426502145923
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6757 - accuracy: 0.6055 - val_loss: 1.6743 - val_accuracy: 0.6038
[ 0.         0.         0.        ...  0.        -0.         0.8908476]
Sparsity at: 0.9845426502145923
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6756 - accuracy: 0.6056 - val_loss: 1.6742 - val_accuracy: 0.6039
[ 0.          0.          0.         ...  0.         -0.
  0.89108646]
Sparsity at: 0.9845426502145923
Epoch 360/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6756 - accuracy: 0.6056 - val_loss: 1.6742 - val_accuracy: 0.6036
[ 0.         0.         0.        ...  0.        -0.         0.8913852]
Sparsity at: 0.9845426502145923
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6756 - accuracy: 0.6055 - val_loss: 1.6742 - val_accuracy: 0.6036
[ 0.          0.          0.         ...  0.         -0.
  0.89179623]
Sparsity at: 0.9845426502145923
Epoch 362/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6756 - accuracy: 0.6055 - val_loss: 1.6742 - val_accuracy: 0.6037
[ 0.          0.          0.         ...  0.         -0.
  0.89191407]
Sparsity at: 0.9845426502145923
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.          0.          0.         ...  0.         -0.
  0.89185876]
Sparsity at: 0.9845426502145923
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.        0.        0.       ...  0.       -0.        0.892028]
Sparsity at: 0.9845426502145923
Epoch 365/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6035
[ 0.         0.         0.        ...  0.        -0.         0.8921946]
Sparsity at: 0.9845426502145923
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6033
[ 0.          0.          0.         ...  0.         -0.
  0.89231884]
Sparsity at: 0.9845426502145923
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6034
[ 0.         0.         0.        ...  0.        -0.         0.8921553]
Sparsity at: 0.9845426502145923
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6035
[ 0.         0.         0.        ...  0.        -0.         0.8922771]
Sparsity at: 0.9845426502145923
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.         0.         0.        ...  0.        -0.         0.8924218]
Sparsity at: 0.9845426502145923
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.         0.         0.        ...  0.        -0.         0.8924572]
Sparsity at: 0.9845426502145923
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.         0.         0.        ...  0.        -0.         0.8924386]
Sparsity at: 0.9845426502145923
Epoch 372/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.          0.          0.         ...  0.         -0.
  0.89247346]
Sparsity at: 0.9845426502145923
Epoch 373/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6740 - val_accuracy: 0.6036
[ 0.          0.          0.         ...  0.         -0.
  0.89250517]
Sparsity at: 0.9845426502145923
Epoch 374/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6037
[ 0.         0.         0.        ...  0.        -0.         0.8927145]
Sparsity at: 0.9845426502145923
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6034
[ 0.         0.         0.        ...  0.        -0.         0.8925544]
Sparsity at: 0.9845426502145923
Epoch 376/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6037
[ 0.         0.         0.        ...  0.        -0.         0.8926245]
Sparsity at: 0.9845426502145923
Epoch 377/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6033
[ 0.        0.        0.       ...  0.       -0.        0.892683]
Sparsity at: 0.9845426502145923
Epoch 378/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6037
[ 0.          0.          0.         ...  0.         -0.
  0.89268535]
Sparsity at: 0.9845426502145923
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6034
[ 0.          0.          0.         ...  0.         -0.
  0.89262277]
Sparsity at: 0.9845426502145923
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6035
[ 0.         0.         0.        ...  0.        -0.         0.8925358]
Sparsity at: 0.9845426502145923
Epoch 381/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6740 - val_accuracy: 0.6036
[ 0.          0.          0.         ...  0.         -0.
  0.89253837]
Sparsity at: 0.9845426502145923
Epoch 382/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6034
[ 0.         0.         0.        ...  0.        -0.         0.8925617]
Sparsity at: 0.9845426502145923
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6038
[ 0.          0.          0.         ...  0.         -0.
  0.89263034]
Sparsity at: 0.9845426502145923
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6035
[ 0.         0.         0.        ...  0.        -0.         0.8926805]
Sparsity at: 0.9845426502145923
Epoch 385/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6740 - val_accuracy: 0.6035
[ 0.          0.          0.         ...  0.         -0.
  0.89263535]
Sparsity at: 0.9845426502145923
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6032
[ 0.          0.          0.         ...  0.         -0.
  0.89255166]
Sparsity at: 0.9845426502145923
Epoch 387/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.         0.         0.        ...  0.        -0.         0.8927294]
Sparsity at: 0.9845426502145923
Epoch 388/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6740 - val_accuracy: 0.6034
[ 0.         0.         0.        ...  0.        -0.         0.8926091]
Sparsity at: 0.9845426502145923
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6035
[ 0.          0.          0.         ...  0.         -0.
  0.89263994]
Sparsity at: 0.9845426502145923
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.          0.          0.         ...  0.         -0.
  0.89255685]
Sparsity at: 0.9845426502145923
Epoch 391/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6035
[ 0.         0.         0.        ...  0.        -0.         0.8927181]
Sparsity at: 0.9845426502145923
Epoch 392/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6035
[ 0.         0.         0.        ...  0.        -0.         0.8926914]
Sparsity at: 0.9845426502145923
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6037
[ 0.         0.         0.        ...  0.        -0.         0.8925936]
Sparsity at: 0.9845426502145923
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.         0.         0.        ...  0.        -0.         0.8926167]
Sparsity at: 0.9845426502145923
Epoch 395/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6034
[ 0.         0.         0.        ...  0.        -0.         0.8928026]
Sparsity at: 0.9845426502145923
Epoch 396/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.         0.         0.        ...  0.        -0.         0.8926983]
Sparsity at: 0.9845426502145923
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.          0.          0.         ...  0.         -0.
  0.89268494]
Sparsity at: 0.9845426502145923
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6034
[ 0.          0.          0.         ...  0.         -0.
  0.89276546]
Sparsity at: 0.9845426502145923
Epoch 399/500
235/235 [==============================] - 2s 10ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6036
[ 0.          0.          0.         ...  0.         -0.
  0.89261985]
Sparsity at: 0.9845426502145923
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6740 - val_accuracy: 0.6035
[ 0.         0.         0.        ...  0.        -0.         0.8926298]
Sparsity at: 0.9845426502145923
Epoch 401/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8498 - accuracy: 0.4744 - val_loss: 1.8203 - val_accuracy: 0.4758
[ 0.         0.         0.        ...  0.        -0.         0.8776634]
Sparsity at: 0.9892871512875536
Epoch 402/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8166 - accuracy: 0.4764 - val_loss: 1.8107 - val_accuracy: 0.4737
[ 0.         0.         0.        ...  0.        -0.         0.8803248]
Sparsity at: 0.9892871512875536
Epoch 403/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8121 - accuracy: 0.5045 - val_loss: 1.8087 - val_accuracy: 0.5155
[ 0.          0.          0.         ...  0.         -0.
  0.88228405]
Sparsity at: 0.9892871512875536
Epoch 404/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8110 - accuracy: 0.5185 - val_loss: 1.8081 - val_accuracy: 0.5153
[ 0.         0.         0.        ...  0.        -0.         0.8834614]
Sparsity at: 0.9892871512875536
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8107 - accuracy: 0.5186 - val_loss: 1.8079 - val_accuracy: 0.5152
[ 0.         0.         0.        ...  0.        -0.         0.8843171]
Sparsity at: 0.9892871512875536
Epoch 406/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8105 - accuracy: 0.5185 - val_loss: 1.8078 - val_accuracy: 0.5154
[ 0.         0.         0.        ...  0.        -0.         0.8848582]
Sparsity at: 0.9892871512875536
Epoch 407/500
235/235 [==============================] - 3s 11ms/step - loss: 1.8105 - accuracy: 0.5185 - val_loss: 1.8077 - val_accuracy: 0.5150
[ 0.         0.         0.        ...  0.        -0.         0.8855228]
Sparsity at: 0.9892871512875536
Epoch 408/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8105 - accuracy: 0.5185 - val_loss: 1.8077 - val_accuracy: 0.5150
[ 0.         0.         0.        ...  0.        -0.         0.8858662]
Sparsity at: 0.9892871512875536
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8077 - val_accuracy: 0.5153
[ 0.         0.         0.        ...  0.        -0.         0.8859212]
Sparsity at: 0.9892871512875536
Epoch 410/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8077 - val_accuracy: 0.5150
[ 0.         0.         0.        ...  0.        -0.         0.8862884]
Sparsity at: 0.9892871512875536
Epoch 411/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5152
[ 0.          0.          0.         ...  0.         -0.
  0.88648665]
Sparsity at: 0.9892871512875536
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5154
[ 0.         0.         0.        ...  0.        -0.         0.8866848]
Sparsity at: 0.9892871512875536
Epoch 413/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5153
[ 0.         0.         0.        ...  0.        -0.         0.8867208]
Sparsity at: 0.9892871512875536
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5152
[ 0.         0.         0.        ...  0.        -0.         0.8870142]
Sparsity at: 0.9892871512875536
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5152
[ 0.         0.         0.        ...  0.        -0.         0.8869678]
Sparsity at: 0.9892871512875536
Epoch 416/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5150
[ 0.          0.          0.         ...  0.         -0.
  0.88720185]
Sparsity at: 0.9892871512875536
Epoch 417/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5182 - val_loss: 1.8076 - val_accuracy: 0.5150
[ 0.         0.         0.        ...  0.        -0.         0.8872306]
Sparsity at: 0.9892871512875536
Epoch 418/500
235/235 [==============================] - 3s 12ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5151
[ 0.         0.         0.        ...  0.        -0.         0.8872322]
Sparsity at: 0.9892871512875536
Epoch 419/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5151
[ 0.         0.         0.        ...  0.        -0.         0.8873701]
Sparsity at: 0.9892871512875536
Epoch 420/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8873265]
Sparsity at: 0.9892871512875536
Epoch 421/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5149
[ 0.          0.          0.         ...  0.         -0.
  0.88742524]
Sparsity at: 0.9892871512875536
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5150
[ 0.          0.          0.         ...  0.         -0.
  0.88729703]
Sparsity at: 0.9892871512875536
Epoch 423/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5150
[ 0.          0.          0.         ...  0.         -0.
  0.88741076]
Sparsity at: 0.9892871512875536
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5149
[ 0.         0.         0.        ...  0.        -0.         0.8873623]
Sparsity at: 0.9892871512875536
Epoch 425/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88746285]
Sparsity at: 0.9892871512875536
Epoch 426/500
235/235 [==============================] - 2s 11ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88742024]
Sparsity at: 0.9892871512875536
Epoch 427/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.         0.         0.        ...  0.        -0.         0.8875567]
Sparsity at: 0.9892871512875536
Epoch 428/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.          0.          0.         ...  0.         -0.
  0.88754386]
Sparsity at: 0.9892871512875536
Epoch 429/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.         0.         0.        ...  0.        -0.         0.8874343]
Sparsity at: 0.9892871512875536
Epoch 430/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5144
[ 0.         0.         0.        ...  0.        -0.         0.8874935]
Sparsity at: 0.9892871512875536
Epoch 431/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.         0.         0.        ...  0.        -0.         0.8874417]
Sparsity at: 0.9892871512875536
Epoch 432/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8874639]
Sparsity at: 0.9892871512875536
Epoch 433/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8876132]
Sparsity at: 0.9892871512875536
Epoch 434/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8875087]
Sparsity at: 0.9892871512875536
Epoch 435/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5145
[ 0.          0.          0.         ...  0.         -0.
  0.88745046]
Sparsity at: 0.9892871512875536
Epoch 436/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5149
[ 0.          0.          0.         ...  0.         -0.
  0.88748956]
Sparsity at: 0.9892871512875536
Epoch 437/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5143
[ 0.          0.          0.         ...  0.         -0.
  0.88747364]
Sparsity at: 0.9892871512875536
Epoch 438/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8876321]
Sparsity at: 0.9892871512875536
Epoch 439/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88764673]
Sparsity at: 0.9892871512875536
Epoch 440/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8877175]
Sparsity at: 0.9892871512875536
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8875513]
Sparsity at: 0.9892871512875536
Epoch 442/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88752633]
Sparsity at: 0.9892871512875536
Epoch 443/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5149
[ 0.          0.          0.         ...  0.         -0.
  0.88751143]
Sparsity at: 0.9892871512875536
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8875532]
Sparsity at: 0.9892871512875536
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8875033]
Sparsity at: 0.9892871512875536
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5148
[ 0.          0.          0.         ...  0.         -0.
  0.88757986]
Sparsity at: 0.9892871512875536
Epoch 447/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.        0.        0.       ...  0.       -0.        0.887458]
Sparsity at: 0.9892871512875536
Epoch 448/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8875297]
Sparsity at: 0.9892871512875536
Epoch 449/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88765854]
Sparsity at: 0.9892871512875536
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.          0.          0.         ...  0.         -0.
  0.88760966]
Sparsity at: 0.9892871512875536
Epoch 451/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.          0.          0.         ...  0.         -0.
  0.88772136]
Sparsity at: 0.9892871512875536
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5151
[ 0.         0.         0.        ...  0.        -0.         0.8875618]
Sparsity at: 0.9892871512875536
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88762987]
Sparsity at: 0.9892871512875536
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5147
[ 0.        0.        0.       ...  0.       -0.        0.887594]
Sparsity at: 0.9892871512875536
Epoch 455/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8875695]
Sparsity at: 0.9892871512875536
Epoch 456/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8876889]
Sparsity at: 0.9892871512875536
Epoch 457/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8873603]
Sparsity at: 0.9892871512875536
Epoch 458/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8875618]
Sparsity at: 0.9892871512875536
Epoch 459/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88742465]
Sparsity at: 0.9892871512875536
Epoch 460/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5151
[ 0.          0.          0.         ...  0.         -0.
  0.88764745]
Sparsity at: 0.9892871512875536
Epoch 461/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.          0.          0.         ...  0.         -0.
  0.88739806]
Sparsity at: 0.9892871512875536
Epoch 462/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88756514]
Sparsity at: 0.9892871512875536
Epoch 463/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.         0.         0.        ...  0.        -0.         0.8874534]
Sparsity at: 0.9892871512875536
Epoch 464/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5149
[ 0.          0.          0.         ...  0.         -0.
  0.88758135]
Sparsity at: 0.9892871512875536
Epoch 465/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8874519]
Sparsity at: 0.9892871512875536
Epoch 466/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8876314]
Sparsity at: 0.9892871512875536
Epoch 467/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.          0.          0.         ...  0.         -0.
  0.88750505]
Sparsity at: 0.9892871512875536
Epoch 468/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88767457]
Sparsity at: 0.9892871512875536
Epoch 469/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88755065]
Sparsity at: 0.9892871512875536
Epoch 470/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88759047]
Sparsity at: 0.9892871512875536
Epoch 471/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5182 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.          0.          0.         ...  0.         -0.
  0.88760424]
Sparsity at: 0.9892871512875536
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8875611]
Sparsity at: 0.9892871512875536
Epoch 473/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5182 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8876005]
Sparsity at: 0.9892871512875536
Epoch 474/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8875723]
Sparsity at: 0.9892871512875536
Epoch 475/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8875916]
Sparsity at: 0.9892871512875536
Epoch 476/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88760126]
Sparsity at: 0.9892871512875536
Epoch 477/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.          0.          0.         ...  0.         -0.
  0.88760597]
Sparsity at: 0.9892871512875536
Epoch 478/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8876239]
Sparsity at: 0.9892871512875536
Epoch 479/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5148
[ 0.        0.        0.       ...  0.       -0.        0.887567]
Sparsity at: 0.9892871512875536
Epoch 480/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5145
[ 0.         0.         0.        ...  0.        -0.         0.8875106]
Sparsity at: 0.9892871512875536
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5148
[ 0.        0.        0.       ...  0.       -0.        0.887612]
Sparsity at: 0.9892871512875536
Epoch 482/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.         0.         0.        ...  0.        -0.         0.8875263]
Sparsity at: 0.9892871512875536
Epoch 483/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5144
[ 0.         0.         0.        ...  0.        -0.         0.8873811]
Sparsity at: 0.9892871512875536
Epoch 484/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88744867]
Sparsity at: 0.9892871512875536
Epoch 485/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88762844]
Sparsity at: 0.9892871512875536
Epoch 486/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.          0.          0.         ...  0.         -0.
  0.88762647]
Sparsity at: 0.9892871512875536
Epoch 487/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5147
[ 0.         0.         0.        ...  0.        -0.         0.8875807]
Sparsity at: 0.9892871512875536
Epoch 488/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.          0.          0.         ...  0.         -0.
  0.88773155]
Sparsity at: 0.9892871512875536
Epoch 489/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5145
[ 0.         0.         0.        ...  0.        -0.         0.8874146]
Sparsity at: 0.9892871512875536
Epoch 490/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.          0.          0.         ...  0.         -0.
  0.88756335]
Sparsity at: 0.9892871512875536
Epoch 491/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8875651]
Sparsity at: 0.9892871512875536
Epoch 492/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5149
[ 0.         0.         0.        ...  0.        -0.         0.8874644]
Sparsity at: 0.9892871512875536
Epoch 493/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88747734]
Sparsity at: 0.9892871512875536
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5144
[ 0.          0.          0.         ...  0.         -0.
  0.88746727]
Sparsity at: 0.9892871512875536
Epoch 495/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.          0.          0.         ...  0.         -0.
  0.88756764]
Sparsity at: 0.9892871512875536
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.          0.          0.         ...  0.         -0.
  0.88769954]
Sparsity at: 0.9892871512875536
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148
[ 0.         0.         0.        ...  0.        -0.         0.8875874]
Sparsity at: 0.9892871512875536
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.          0.          0.         ...  0.         -0.
  0.88755345]
Sparsity at: 0.9892871512875536
Epoch 499/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5146
[ 0.         0.         0.        ...  0.        -0.         0.8875806]
Sparsity at: 0.9892871512875536
Epoch 500/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147
[ 0.          0.          0.         ...  0.         -0.
  0.88751596]
Sparsity at: 0.9892871512875536
Epoch 1/500
235/235 [==============================] - 4s 9ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.2421 - val_accuracy: 0.9732
[-0.         0.         0.        ... -0.5949296 -0.7967351  0.9044952]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2970e-04 - accuracy: 0.9999 - val_loss: 0.2392 - val_accuracy: 0.9742
[-0.          0.          0.         ... -0.59087783 -0.8018444
  0.9013458 ]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 8.8304e-04 - accuracy: 0.9997 - val_loss: 0.2475 - val_accuracy: 0.9722
[-0.          0.          0.         ... -0.5801735  -0.8080663
  0.90034556]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 0.9990 - val_loss: 0.2524 - val_accuracy: 0.9729
[-0.          0.          0.         ... -0.602204   -0.79381484
  0.9078741 ]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0023 - accuracy: 0.9991 - val_loss: 0.2354 - val_accuracy: 0.9740
[-0.          0.          0.         ... -0.62556905 -0.7988888
  0.92145705]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9111e-04 - accuracy: 0.9999 - val_loss: 0.2329 - val_accuracy: 0.9751
[-0.          0.          0.         ... -0.62521774 -0.796512
  0.9181671 ]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 7.6756e-05 - accuracy: 1.0000 - val_loss: 0.2331 - val_accuracy: 0.9748
[-0.          0.          0.         ... -0.62813056 -0.7966049
  0.919858  ]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3206e-05 - accuracy: 1.0000 - val_loss: 0.2324 - val_accuracy: 0.9748
[-0.          0.          0.         ... -0.62881666 -0.7960898
  0.920007  ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4910e-05 - accuracy: 1.0000 - val_loss: 0.2321 - val_accuracy: 0.9748
[-0.          0.          0.         ... -0.6293421  -0.79582816
  0.92004746]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2867e-05 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9748
[-0.          0.          0.         ... -0.6298372  -0.795629
  0.92011446]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1484e-05 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9748
[-0.          0.          0.         ... -0.63031375 -0.79545784
  0.9201984 ]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0413e-05 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9748
[-0.          0.          0.         ... -0.63079536 -0.795314
  0.9202968 ]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 2s 9ms/step - loss: 9.5318e-06 - accuracy: 1.0000 - val_loss: 0.2312 - val_accuracy: 0.9747
[-0.          0.          0.         ... -0.63127935 -0.79518884
  0.92041177]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 2s 9ms/step - loss: 8.7837e-06 - accuracy: 1.0000 - val_loss: 0.2311 - val_accuracy: 0.9747
[-0.          0.          0.         ... -0.6317732  -0.79508007
  0.92054445]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 2s 9ms/step - loss: 8.1289e-06 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9747
[-0.          0.          0.         ... -0.63228625 -0.7949868
  0.9206967 ]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5469e-06 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.6328092  -0.7949071
  0.92086804]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 7.0246e-06 - accuracy: 1.0000 - val_loss: 0.2308 - val_accuracy: 0.9750
[-0.          0.          0.         ... -0.63334644 -0.7948411
  0.9210628 ]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5508e-06 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.6339009  -0.79478765
  0.9212787 ]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1162e-06 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.63448054 -0.7947449
  0.9215186 ]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7176e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.6350746 -0.7947143  0.9217835]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 2s 9ms/step - loss: 5.3487e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.63569397 -0.79469824
  0.92207736]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0078e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9750
[-0.          0.          0.         ... -0.6363327  -0.79469347
  0.92239517]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6904e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9750
[-0.          0.          0.         ... -0.6369989  -0.79469824
  0.9227408 ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3948e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9751
[-0.          0.          0.         ... -0.6376965  -0.79471636
  0.9231179 ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1186e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.6384237  -0.79474336
  0.9235218 ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 2s 9ms/step - loss: 3.8610e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9753
[-0.          0.          0.         ... -0.63917726 -0.7947878
  0.9239607 ]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6202e-06 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9753
[-0.          0.          0.         ... -0.6399645  -0.794844
  0.92442757]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3936e-06 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9753
[-0.          0.          0.         ... -0.64078605 -0.7949133
  0.9249269 ]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 3.1808e-06 - accuracy: 1.0000 - val_loss: 0.2308 - val_accuracy: 0.9754
[-0.          0.          0.         ... -0.64164996 -0.7949979
  0.92546123]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9819e-06 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9753
[-0.          0.          0.         ... -0.6425526  -0.79509676
  0.9260302 ]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7956e-06 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9753
[-0.          0.          0.         ... -0.64350206 -0.79521537
  0.92663527]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6200e-06 - accuracy: 1.0000 - val_loss: 0.2311 - val_accuracy: 0.9754
[-0.          0.          0.         ... -0.6445055  -0.79535216
  0.92727697]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4542e-06 - accuracy: 1.0000 - val_loss: 0.2312 - val_accuracy: 0.9755
[-0.          0.          0.         ... -0.64555746 -0.7955067
  0.9279571 ]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2978e-06 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9755
[-0.          0.          0.         ... -0.646664   -0.795693
  0.92867565]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 2s 10ms/step - loss: 2.1521e-06 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9757
[-0.          0.          0.         ... -0.6478337  -0.7958971
  0.92943966]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0138e-06 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9757
[-0.          0.          0.         ... -0.64906716 -0.7961287
  0.9302437 ]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8834e-06 - accuracy: 1.0000 - val_loss: 0.2319 - val_accuracy: 0.9756
[-0.          0.          0.         ... -0.65036595 -0.79639417
  0.93109155]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7604e-06 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9756
[-0.          0.          0.         ... -0.6517346  -0.7966881
  0.93198645]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6448e-06 - accuracy: 1.0000 - val_loss: 0.2324 - val_accuracy: 0.9756
[-0.          0.          0.         ... -0.65318346 -0.7970313
  0.9329234 ]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5358e-06 - accuracy: 1.0000 - val_loss: 0.2327 - val_accuracy: 0.9755
[-0.          0.          0.         ... -0.65468884 -0.7974118
  0.9339063 ]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4337e-06 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 0.9754
[-0.          0.          0.         ... -0.656279   -0.79783255
  0.9349413 ]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3364e-06 - accuracy: 1.0000 - val_loss: 0.2333 - val_accuracy: 0.9752
[-0.         0.         0.        ... -0.6579501 -0.7982985  0.9360304]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2455e-06 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.6597102  -0.79880923
  0.93717694]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1596e-06 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.66152585 -0.79937977
  0.9383721 ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0792e-06 - accuracy: 1.0000 - val_loss: 0.2344 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.6634188  -0.80001545
  0.93963134]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0034e-06 - accuracy: 1.0000 - val_loss: 0.2348 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.6653801  -0.80070084
  0.9409624 ]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 2s 9ms/step - loss: 9.3268e-07 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.6674318  -0.80144906
  0.9423237 ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 8.6612e-07 - accuracy: 1.0000 - val_loss: 0.2357 - val_accuracy: 0.9754
[-0.          0.          0.         ... -0.6695709  -0.8022618
  0.94375974]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 8.0352e-07 - accuracy: 1.0000 - val_loss: 0.2362 - val_accuracy: 0.9756
[-0.          0.          0.         ... -0.6717647  -0.80314386
  0.9452477 ]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 7.4525e-07 - accuracy: 1.0000 - val_loss: 0.2367 - val_accuracy: 0.9756
[-0.          0.          0.         ... -0.6740259  -0.8040751
  0.94679165]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0231 - accuracy: 0.9931 - val_loss: 0.1847 - val_accuracy: 0.9731
[-0.          0.          0.         ...  0.         -0.73509145
  0.8957523 ]
Sparsity at: 0.6458724517167382
Epoch 52/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0029 - accuracy: 0.9991 - val_loss: 0.1744 - val_accuracy: 0.9751
[-0.         0.         0.        ...  0.        -0.7311096  0.8890226]
Sparsity at: 0.6458724517167382
Epoch 53/500
235/235 [==============================] - 2s 9ms/step - loss: 6.2802e-04 - accuracy: 0.9999 - val_loss: 0.1748 - val_accuracy: 0.9744
[-0.         0.         0.        ...  0.        -0.7297645  0.8887018]
Sparsity at: 0.6458724517167382
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2520e-04 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9747
[-0.          0.          0.         ...  0.         -0.73272973
  0.88878936]
Sparsity at: 0.6458724517167382
Epoch 55/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5118e-04 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9746
[-0.          0.          0.         ...  0.         -0.7334399
  0.88943857]
Sparsity at: 0.6458724517167382
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 2.1605e-04 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9748
[-0.          0.          0.         ...  0.         -0.73405415
  0.8899287 ]
Sparsity at: 0.6458724517167382
Epoch 57/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9124e-04 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9748
[-0.          0.          0.         ...  0.         -0.7346046
  0.89040333]
Sparsity at: 0.6458724517167382
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7160e-04 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9750
[-0.          0.          0.         ... -0.         -0.73518705
  0.8909222 ]
Sparsity at: 0.6458724517167382
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5545e-04 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9747
[-0.         0.         0.        ...  0.        -0.735778   0.8914976]
Sparsity at: 0.6458724517167382
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4143e-04 - accuracy: 1.0000 - val_loss: 0.1792 - val_accuracy: 0.9747
[-0.         0.         0.        ...  0.        -0.7364397  0.892144 ]
Sparsity at: 0.6458724517167382
Epoch 61/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2928e-04 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9748
[-0.          0.          0.         ...  0.         -0.7371226
  0.89282554]
Sparsity at: 0.6458724517167382
Epoch 62/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1838e-04 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9748
[-0.          0.          0.         ...  0.         -0.73789334
  0.8935804 ]
Sparsity at: 0.6458724517167382
Epoch 63/500
235/235 [==============================] - 2s 10ms/step - loss: 1.0872e-04 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9748
[-0.          0.          0.         ...  0.         -0.73870784
  0.8944164 ]
Sparsity at: 0.6458724517167382
Epoch 64/500
235/235 [==============================] - 2s 10ms/step - loss: 9.9934e-05 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.         -0.7395601
  0.89531624]
Sparsity at: 0.6458724517167382
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 9.1982e-05 - accuracy: 1.0000 - val_loss: 0.1820 - val_accuracy: 0.9750
[-0.         0.         0.        ...  0.        -0.7404641  0.896323 ]
Sparsity at: 0.6458724517167382
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 8.4667e-05 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9750
[-0.          0.          0.         ...  0.         -0.74144167
  0.89741373]
Sparsity at: 0.6458724517167382
Epoch 67/500
235/235 [==============================] - 2s 9ms/step - loss: 7.7973e-05 - accuracy: 1.0000 - val_loss: 0.1831 - val_accuracy: 0.9750
[-0.          0.          0.         ...  0.         -0.74249196
  0.8986089 ]
Sparsity at: 0.6458724517167382
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 7.1826e-05 - accuracy: 1.0000 - val_loss: 0.1837 - val_accuracy: 0.9750
[-0.         0.         0.        ... -0.        -0.7435539  0.899926 ]
Sparsity at: 0.6458724517167382
Epoch 69/500
235/235 [==============================] - 2s 9ms/step - loss: 6.6187e-05 - accuracy: 1.0000 - val_loss: 0.1844 - val_accuracy: 0.9751
[-0.         0.         0.        ...  0.        -0.7447125  0.9012957]
Sparsity at: 0.6458724517167382
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 6.0907e-05 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9751
[-0.         0.         0.        ...  0.        -0.7458928  0.9028403]
Sparsity at: 0.6458724517167382
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 5.6040e-05 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9751
[-0.          0.          0.         ... -0.         -0.7471239
  0.90447205]
Sparsity at: 0.6458724517167382
Epoch 72/500
235/235 [==============================] - 2s 9ms/step - loss: 5.1602e-05 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.        -0.7484403  0.9061902]
Sparsity at: 0.6458724517167382
Epoch 73/500
235/235 [==============================] - 2s 9ms/step - loss: 4.7446e-05 - accuracy: 1.0000 - val_loss: 0.1870 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.        -0.7498223  0.9080335]
Sparsity at: 0.6458724517167382
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3599e-05 - accuracy: 1.0000 - val_loss: 0.1877 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.        -0.7512304  0.9099991]
Sparsity at: 0.6458724517167382
Epoch 75/500
235/235 [==============================] - 2s 9ms/step - loss: 4.0024e-05 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.         -0.75272524
  0.9121028 ]
Sparsity at: 0.6458724517167382
Epoch 76/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6722e-05 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9753
[-0.         0.         0.        ...  0.        -0.7542807  0.9142741]
Sparsity at: 0.6458724517167382
Epoch 77/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3653e-05 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9753
[-0.         0.         0.        ...  0.        -0.7558255  0.9166251]
Sparsity at: 0.6458724517167382
Epoch 78/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0836e-05 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9751
[-0.          0.          0.         ...  0.         -0.7575107
  0.91898817]
Sparsity at: 0.6458724517167382
Epoch 79/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8210e-05 - accuracy: 1.0000 - val_loss: 0.1916 - val_accuracy: 0.9751
[-0.         0.         0.        ... -0.        -0.7592358  0.9214902]
Sparsity at: 0.6458724517167382
Epoch 80/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5803e-05 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.         -0.76105595
  0.9241198 ]
Sparsity at: 0.6458724517167382
Epoch 81/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3605e-05 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9749
[-0.          0.          0.         ...  0.         -0.7629121
  0.92692345]
Sparsity at: 0.6458724517167382
Epoch 82/500
235/235 [==============================] - 2s 9ms/step - loss: 2.1514e-05 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.        -0.7648923  0.9298033]
Sparsity at: 0.6458724517167382
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9612e-05 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.        -0.7670017  0.9327651]
Sparsity at: 0.6458724517167382
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7861e-05 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.         -0.76916486
  0.93593115]
Sparsity at: 0.6458724517167382
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6245e-05 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.         -0.7714767
  0.93912816]
Sparsity at: 0.6458724517167382
Epoch 86/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4757e-05 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9748
[-0.          0.          0.         ... -0.         -0.7738235
  0.94240904]
Sparsity at: 0.6458724517167382
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3421e-05 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9748
[-0.          0.          0.         ... -0.         -0.77635175
  0.94593024]
Sparsity at: 0.6458724517167382
Epoch 88/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2177e-05 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9748
[-0.         0.         0.        ... -0.        -0.778937   0.9494618]
Sparsity at: 0.6458724517167382
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1035e-05 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.         -0.78163123
  0.95317775]
Sparsity at: 0.6458724517167382
Epoch 90/500
235/235 [==============================] - 2s 9ms/step - loss: 9.9931e-06 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.        -0.7843923  0.9569912]
Sparsity at: 0.6458724517167382
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 9.0354e-06 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9749
[-0.          0.          0.         ... -0.         -0.7872921
  0.96092635]
Sparsity at: 0.6458724517167382
Epoch 92/500
235/235 [==============================] - 2s 9ms/step - loss: 8.1656e-06 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.        -0.790321   0.9649001]
Sparsity at: 0.6458724517167382
Epoch 93/500
235/235 [==============================] - 2s 9ms/step - loss: 7.3798e-06 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9749
[-0.         0.         0.        ... -0.        -0.7934454  0.9690302]
Sparsity at: 0.6458724517167382
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 6.6585e-06 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9750
[-0.         0.         0.        ... -0.        -0.7966488  0.9732155]
Sparsity at: 0.6458724517167382
Epoch 95/500
235/235 [==============================] - 2s 9ms/step - loss: 6.0068e-06 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9753
[-0.          0.          0.         ... -0.         -0.79995257
  0.97753745]
Sparsity at: 0.6458724517167382
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 5.4153e-06 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9753
[-0.          0.          0.         ... -0.         -0.803302
  0.98189294]
Sparsity at: 0.6458724517167382
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 4.8784e-06 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9752
[-0.         0.         0.        ... -0.        -0.8067683  0.9863515]
Sparsity at: 0.6458724517167382
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3888e-06 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9752
[-0.         0.         0.        ... -0.        -0.8103193  0.9909247]
Sparsity at: 0.6458724517167382
Epoch 99/500
235/235 [==============================] - 2s 9ms/step - loss: 3.9469e-06 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9752
[-0.         0.         0.        ... -0.        -0.8139793  0.9955227]
Sparsity at: 0.6458724517167382
Epoch 100/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5462e-06 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9752
[-0.          0.          0.         ... -0.         -0.81765753
  1.0002588 ]
Sparsity at: 0.6458724517167382
Epoch 101/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0385 - accuracy: 0.9883 - val_loss: 0.1706 - val_accuracy: 0.9708
[-0.         0.         0.        ... -0.        -0.         1.0135858]
Sparsity at: 0.759438707081545
Epoch 102/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0088 - accuracy: 0.9971 - val_loss: 0.1634 - val_accuracy: 0.9714
[-0.         0.         0.        ... -0.         0.         1.0286574]
Sparsity at: 0.759438707081545
Epoch 103/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0044 - accuracy: 0.9990 - val_loss: 0.1615 - val_accuracy: 0.9722
[-0.        0.        0.       ... -0.        0.        1.041981]
Sparsity at: 0.759438707081545
Epoch 104/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0028 - accuracy: 0.9998 - val_loss: 0.1610 - val_accuracy: 0.9722
[-0.         0.         0.        ... -0.         0.         1.0521779]
Sparsity at: 0.759438707081545
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.1608 - val_accuracy: 0.9729
[-0.        0.        0.       ... -0.        0.        1.062363]
Sparsity at: 0.759438707081545
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1609 - val_accuracy: 0.9732
[-0.         0.         0.        ... -0.        -0.         1.0719025]
Sparsity at: 0.759438707081545
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1613 - val_accuracy: 0.9736
[-0.         0.         0.        ... -0.        -0.         1.0807847]
Sparsity at: 0.759438707081545
Epoch 108/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1617 - val_accuracy: 0.9737
[-0.         0.         0.        ... -0.        -0.         1.0893971]
Sparsity at: 0.759438707081545
Epoch 109/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1623 - val_accuracy: 0.9737
[-0.         0.         0.        ... -0.        -0.         1.0978664]
Sparsity at: 0.759438707081545
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 9.6154e-04 - accuracy: 1.0000 - val_loss: 0.1629 - val_accuracy: 0.9737
[-0.         0.         0.        ... -0.        -0.         1.1060125]
Sparsity at: 0.759438707081545
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 8.5414e-04 - accuracy: 1.0000 - val_loss: 0.1635 - val_accuracy: 0.9741
[-0.         0.         0.        ... -0.        -0.         1.1139268]
Sparsity at: 0.759438707081545
Epoch 112/500
235/235 [==============================] - 2s 9ms/step - loss: 7.6361e-04 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9740
[-0.         0.         0.        ... -0.        -0.         1.1221764]
Sparsity at: 0.759438707081545
Epoch 113/500
235/235 [==============================] - 2s 9ms/step - loss: 6.8692e-04 - accuracy: 1.0000 - val_loss: 0.1648 - val_accuracy: 0.9740
[-0.         0.         0.        ... -0.        -0.         1.1304115]
Sparsity at: 0.759438707081545
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1952e-04 - accuracy: 1.0000 - val_loss: 0.1656 - val_accuracy: 0.9740
[-0.         0.         0.        ... -0.        -0.         1.1386477]
Sparsity at: 0.759438707081545
Epoch 115/500
235/235 [==============================] - 2s 9ms/step - loss: 5.6040e-04 - accuracy: 1.0000 - val_loss: 0.1664 - val_accuracy: 0.9740
[-0.         0.         0.        ... -0.        -0.         1.1467956]
Sparsity at: 0.759438707081545
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0676e-04 - accuracy: 1.0000 - val_loss: 0.1672 - val_accuracy: 0.9740
[-0.         0.         0.        ... -0.        -0.         1.1547996]
Sparsity at: 0.759438707081545
Epoch 117/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6018e-04 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9741
[-0.         0.         0.        ... -0.        -0.         1.1632582]
Sparsity at: 0.759438707081545
Epoch 118/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1757e-04 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9740
[-0.         0.         0.        ... -0.         0.         1.1718184]
Sparsity at: 0.759438707081545
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7923e-04 - accuracy: 1.0000 - val_loss: 0.1701 - val_accuracy: 0.9740
[-0.         0.         0.        ... -0.         0.         1.1802437]
Sparsity at: 0.759438707081545
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4450e-04 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9740
[-0.         0.         0.        ... -0.         0.         1.1888474]
Sparsity at: 0.759438707081545
Epoch 121/500
235/235 [==============================] - 2s 9ms/step - loss: 3.1284e-04 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9742
[-0.         0.         0.        ... -0.         0.         1.1975056]
Sparsity at: 0.759438707081545
Epoch 122/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8426e-04 - accuracy: 1.0000 - val_loss: 0.1733 - val_accuracy: 0.9743
[-0.         0.         0.        ... -0.         0.         1.2064425]
Sparsity at: 0.759438707081545
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5859e-04 - accuracy: 1.0000 - val_loss: 0.1745 - val_accuracy: 0.9743
[-0.         0.         0.        ... -0.        -0.         1.2154092]
Sparsity at: 0.759438707081545
Epoch 124/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3445e-04 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9742
[-0.         0.         0.        ... -0.         0.         1.2244335]
Sparsity at: 0.759438707081545
Epoch 125/500
235/235 [==============================] - 2s 9ms/step - loss: 2.1282e-04 - accuracy: 1.0000 - val_loss: 0.1771 - val_accuracy: 0.9744
[-0.         0.         0.        ... -0.         0.         1.2337043]
Sparsity at: 0.759438707081545
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9352e-04 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9742
[-0.         0.         0.        ... -0.        -0.         1.2427634]
Sparsity at: 0.759438707081545
Epoch 127/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7537e-04 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9743
[-0.        0.        0.       ... -0.        0.        1.252267]
Sparsity at: 0.759438707081545
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5901e-04 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9742
[-0.         0.         0.        ... -0.         0.         1.2615753]
Sparsity at: 0.759438707081545
Epoch 129/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4396e-04 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9742
[-0.        0.        0.       ... -0.        0.        1.271116]
Sparsity at: 0.759438707081545
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3044e-04 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9741
[-0.         0.         0.        ... -0.         0.         1.2802848]
Sparsity at: 0.759438707081545
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1803e-04 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9741
[-0.        0.        0.       ... -0.        0.        1.290245]
Sparsity at: 0.759438707081545
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0670e-04 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9741
[-0.         0.         0.        ... -0.         0.         1.2998358]
Sparsity at: 0.759438707081545
Epoch 133/500
235/235 [==============================] - 2s 9ms/step - loss: 9.6437e-05 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9740
[-0.        0.        0.       ... -0.        0.        1.309438]
Sparsity at: 0.759438707081545
Epoch 134/500
235/235 [==============================] - 2s 9ms/step - loss: 8.7060e-05 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9741
[-0.        0.        0.       ... -0.        0.        1.318891]
Sparsity at: 0.759438707081545
Epoch 135/500
235/235 [==============================] - 2s 9ms/step - loss: 7.8697e-05 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9741
[-0.         0.         0.        ... -0.         0.         1.3285699]
Sparsity at: 0.759438707081545
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 7.0943e-05 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9741
[-0.         0.         0.        ... -0.         0.         1.3384598]
Sparsity at: 0.759438707081545
Epoch 137/500
235/235 [==============================] - 2s 9ms/step - loss: 6.3961e-05 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9739
[-0.         0.         0.        ... -0.         0.         1.3484141]
Sparsity at: 0.759438707081545
Epoch 138/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7812e-05 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9738
[-0.         0.         0.        ... -0.         0.         1.3582155]
Sparsity at: 0.759438707081545
Epoch 139/500
235/235 [==============================] - 2s 9ms/step - loss: 5.2114e-05 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9738
[-0.        0.        0.       ... -0.        0.        1.368312]
Sparsity at: 0.759438707081545
Epoch 140/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6834e-05 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9736
[-0.         0.         0.        ... -0.         0.         1.3782719]
Sparsity at: 0.759438707081545
Epoch 141/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2158e-05 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9736
[-0.         0.         0.        ... -0.         0.         1.3880044]
Sparsity at: 0.759438707081545
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 3.8001e-05 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9733
[-0.         0.         0.        ... -0.         0.         1.3980275]
Sparsity at: 0.759438707081545
Epoch 143/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4147e-05 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9730
[-0.         0.         0.        ... -0.         0.         1.4080955]
Sparsity at: 0.759438707081545
Epoch 144/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0713e-05 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9730
[-0.         0.         0.        ... -0.        -0.         1.4181806]
Sparsity at: 0.759438707081545
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7617e-05 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9733
[-0.         0.         0.        ... -0.        -0.         1.4283594]
Sparsity at: 0.759438707081545
Epoch 146/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4846e-05 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9731
[-0.         0.         0.        ... -0.         0.         1.4382554]
Sparsity at: 0.759438707081545
Epoch 147/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2242e-05 - accuracy: 1.0000 - val_loss: 0.2141 - val_accuracy: 0.9733
[-0.        0.        0.       ... -0.        0.        1.448506]
Sparsity at: 0.759438707081545
Epoch 148/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9989e-05 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9732
[-0.         0.         0.        ... -0.        -0.         1.4585552]
Sparsity at: 0.759438707081545
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8007e-05 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9733
[-0.         0.         0.        ... -0.         0.         1.4686309]
Sparsity at: 0.759438707081545
Epoch 150/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6131e-05 - accuracy: 1.0000 - val_loss: 0.2202 - val_accuracy: 0.9731
[-0.         0.         0.        ... -0.        -0.         1.4790257]
Sparsity at: 0.759438707081545
Epoch 151/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0777 - accuracy: 0.9781 - val_loss: 0.1735 - val_accuracy: 0.9691
[-0.         0.         0.        ... -0.         0.         1.3690395]
Sparsity at: 0.8448229613733905
Epoch 152/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0300 - accuracy: 0.9900 - val_loss: 0.1653 - val_accuracy: 0.9694
[-0.         0.         0.        ... -0.         0.         1.3398262]
Sparsity at: 0.8448229613733905
Epoch 153/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0219 - accuracy: 0.9927 - val_loss: 0.1619 - val_accuracy: 0.9705
[-0.         0.         0.        ... -0.         0.         1.3242564]
Sparsity at: 0.8448229613733905
Epoch 154/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0176 - accuracy: 0.9944 - val_loss: 0.1602 - val_accuracy: 0.9710
[-0.         0.         0.        ... -0.         0.         1.3124429]
Sparsity at: 0.8448229613733905
Epoch 155/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0147 - accuracy: 0.9955 - val_loss: 0.1592 - val_accuracy: 0.9710
[-0.         0.         0.        ... -0.         0.         1.3022195]
Sparsity at: 0.8448229613733905
Epoch 156/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0127 - accuracy: 0.9964 - val_loss: 0.1588 - val_accuracy: 0.9714
[-0.         0.         0.        ... -0.         0.         1.2935442]
Sparsity at: 0.8448229613733905
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0111 - accuracy: 0.9972 - val_loss: 0.1586 - val_accuracy: 0.9716
[-0.         0.         0.        ... -0.         0.         1.2854189]
Sparsity at: 0.8448229613733905
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0099 - accuracy: 0.9979 - val_loss: 0.1590 - val_accuracy: 0.9716
[-0.         0.         0.        ... -0.         0.         1.2781818]
Sparsity at: 0.8448229613733905
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0088 - accuracy: 0.9982 - val_loss: 0.1592 - val_accuracy: 0.9718
[-0.         0.         0.        ... -0.         0.         1.2717553]
Sparsity at: 0.8448229613733905
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0080 - accuracy: 0.9986 - val_loss: 0.1597 - val_accuracy: 0.9719
[-0.         0.         0.        ... -0.         0.         1.2666832]
Sparsity at: 0.8448229613733905
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0073 - accuracy: 0.9989 - val_loss: 0.1601 - val_accuracy: 0.9717
[-0.         0.         0.        ... -0.         0.         1.2621882]
Sparsity at: 0.8448229613733905
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0066 - accuracy: 0.9990 - val_loss: 0.1607 - val_accuracy: 0.9716
[-0.         0.         0.        ... -0.         0.         1.2579225]
Sparsity at: 0.8448229613733905
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0061 - accuracy: 0.9992 - val_loss: 0.1613 - val_accuracy: 0.9717
[-0.         0.         0.        ... -0.         0.         1.2542441]
Sparsity at: 0.8448229613733905
Epoch 164/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0056 - accuracy: 0.9994 - val_loss: 0.1619 - val_accuracy: 0.9715
[-0.         0.         0.        ... -0.         0.         1.2514261]
Sparsity at: 0.8448229613733905
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0052 - accuracy: 0.9995 - val_loss: 0.1625 - val_accuracy: 0.9714
[-0.         0.         0.        ... -0.         0.         1.2488104]
Sparsity at: 0.8448229613733905
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0048 - accuracy: 0.9996 - val_loss: 0.1630 - val_accuracy: 0.9717
[-0.        0.        0.       ... -0.        0.        1.246735]
Sparsity at: 0.8448229613733905
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0044 - accuracy: 0.9997 - val_loss: 0.1636 - val_accuracy: 0.9720
[-0.         0.         0.        ...  0.         0.         1.2449026]
Sparsity at: 0.8448229613733905
Epoch 168/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0041 - accuracy: 0.9998 - val_loss: 0.1642 - val_accuracy: 0.9720
[-0.         0.         0.        ... -0.         0.         1.2436571]
Sparsity at: 0.8448229613733905
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0038 - accuracy: 0.9998 - val_loss: 0.1650 - val_accuracy: 0.9722
[-0.         0.         0.        ... -0.         0.         1.2431991]
Sparsity at: 0.8448229613733905
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0035 - accuracy: 0.9999 - val_loss: 0.1657 - val_accuracy: 0.9721
[-0.         0.         0.        ... -0.         0.         1.2426915]
Sparsity at: 0.8448229613733905
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0033 - accuracy: 0.9999 - val_loss: 0.1664 - val_accuracy: 0.9723
[-0.         0.         0.        ... -0.         0.         1.2427006]
Sparsity at: 0.8448229613733905
Epoch 172/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0031 - accuracy: 0.9999 - val_loss: 0.1671 - val_accuracy: 0.9724
[-0.        0.        0.       ... -0.        0.        1.243142]
Sparsity at: 0.8448229613733905
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.1680 - val_accuracy: 0.9726
[-0.         0.         0.        ... -0.         0.         1.2440412]
Sparsity at: 0.8448229613733905
Epoch 174/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1690 - val_accuracy: 0.9725
[-0.         0.         0.        ... -0.         0.         1.2454926]
Sparsity at: 0.8448229613733905
Epoch 175/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9725
[-0.         0.         0.        ... -0.         0.         1.2470108]
Sparsity at: 0.8448229613733905
Epoch 176/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1709 - val_accuracy: 0.9726
[-0.        0.        0.       ... -0.        0.        1.248603]
Sparsity at: 0.8448229613733905
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9727
[-0.         0.         0.        ... -0.         0.         1.2511225]
Sparsity at: 0.8448229613733905
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9727
[-0.         0.         0.        ... -0.         0.         1.2533319]
Sparsity at: 0.8448229613733905
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1740 - val_accuracy: 0.9726
[-0.         0.         0.        ... -0.         0.         1.2558413]
Sparsity at: 0.8448229613733905
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1749 - val_accuracy: 0.9726
[-0.        0.        0.       ... -0.        0.        1.258688]
Sparsity at: 0.8448229613733905
Epoch 181/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 0.9723
[-0.         0.         0.        ...  0.         0.         1.2622253]
Sparsity at: 0.8448229613733905
Epoch 182/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1772 - val_accuracy: 0.9719
[-0.         0.         0.        ... -0.         0.         1.2659397]
Sparsity at: 0.8448229613733905
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9719
[-0.         0.         0.        ... -0.         0.         1.2693189]
Sparsity at: 0.8448229613733905
Epoch 184/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1799 - val_accuracy: 0.9720
[-0.         0.         0.        ... -0.         0.         1.2732551]
Sparsity at: 0.8448229613733905
Epoch 185/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9723
[-0.         0.         0.        ... -0.         0.         1.2776635]
Sparsity at: 0.8448229613733905
Epoch 186/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9723
[-0.         0.         0.        ... -0.         0.         1.2825797]
Sparsity at: 0.8448229613733905
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9723
[-0.         0.         0.        ... -0.         0.         1.2869611]
Sparsity at: 0.8448229613733905
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9724
[-0.         0.         0.        ... -0.         0.         1.2919506]
Sparsity at: 0.8448229613733905
Epoch 189/500
235/235 [==============================] - 2s 9ms/step - loss: 9.8149e-04 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9722
[-0.         0.         0.        ... -0.         0.         1.2971457]
Sparsity at: 0.8448229613733905
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 9.1682e-04 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9723
[-0.         0.         0.        ... -0.         0.         1.3026649]
Sparsity at: 0.8448229613733905
Epoch 191/500
235/235 [==============================] - 2s 9ms/step - loss: 8.5529e-04 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9722
[-0.        0.        0.       ... -0.        0.        1.308464]
Sparsity at: 0.8448229613733905
Epoch 192/500
235/235 [==============================] - 2s 9ms/step - loss: 8.0176e-04 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9722
[-0.         0.         0.        ... -0.         0.         1.3141136]
Sparsity at: 0.8448229613733905
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 7.4792e-04 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9721
[-0.        0.        0.       ... -0.        0.        1.319991]
Sparsity at: 0.8448229613733905
Epoch 194/500
235/235 [==============================] - 2s 9ms/step - loss: 6.9921e-04 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9721
[-0.         0.         0.        ... -0.         0.         1.3260802]
Sparsity at: 0.8448229613733905
Epoch 195/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5282e-04 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9722
[-0.        0.        0.       ... -0.        0.        1.332319]
Sparsity at: 0.8448229613733905
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1015e-04 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9725
[-0.         0.         0.        ... -0.         0.         1.3383763]
Sparsity at: 0.8448229613733905
Epoch 197/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7188e-04 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9725
[-0.         0.         0.        ... -0.         0.         1.3453194]
Sparsity at: 0.8448229613733905
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 5.3581e-04 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9725
[-0.         0.         0.        ... -0.         0.         1.3522477]
Sparsity at: 0.8448229613733905
Epoch 199/500
235/235 [==============================] - 2s 9ms/step - loss: 4.9731e-04 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9725
[-0.         0.         0.        ... -0.         0.         1.3595047]
Sparsity at: 0.8448229613733905
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6412e-04 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9724
[-0.         0.         0.        ... -0.         0.         1.3669329]
Sparsity at: 0.8448229613733905
Epoch 201/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1938 - accuracy: 0.9481 - val_loss: 0.2282 - val_accuracy: 0.9499
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 202/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1021 - accuracy: 0.9687 - val_loss: 0.2040 - val_accuracy: 0.9540
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 203/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0852 - accuracy: 0.9732 - val_loss: 0.1914 - val_accuracy: 0.9561
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0760 - accuracy: 0.9757 - val_loss: 0.1829 - val_accuracy: 0.9575
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0695 - accuracy: 0.9775 - val_loss: 0.1767 - val_accuracy: 0.9591
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0649 - accuracy: 0.9787 - val_loss: 0.1718 - val_accuracy: 0.9596
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 207/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0612 - accuracy: 0.9796 - val_loss: 0.1680 - val_accuracy: 0.9608
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0581 - accuracy: 0.9804 - val_loss: 0.1648 - val_accuracy: 0.9612
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0556 - accuracy: 0.9812 - val_loss: 0.1623 - val_accuracy: 0.9616
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0535 - accuracy: 0.9821 - val_loss: 0.1599 - val_accuracy: 0.9615
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 211/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0516 - accuracy: 0.9827 - val_loss: 0.1580 - val_accuracy: 0.9629
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 212/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0500 - accuracy: 0.9831 - val_loss: 0.1563 - val_accuracy: 0.9628
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 213/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0485 - accuracy: 0.9833 - val_loss: 0.1549 - val_accuracy: 0.9634
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0471 - accuracy: 0.9838 - val_loss: 0.1536 - val_accuracy: 0.9642
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 215/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0460 - accuracy: 0.9843 - val_loss: 0.1524 - val_accuracy: 0.9640
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 216/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0448 - accuracy: 0.9849 - val_loss: 0.1515 - val_accuracy: 0.9641
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0438 - accuracy: 0.9851 - val_loss: 0.1506 - val_accuracy: 0.9645
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0429 - accuracy: 0.9855 - val_loss: 0.1499 - val_accuracy: 0.9648
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0421 - accuracy: 0.9858 - val_loss: 0.1492 - val_accuracy: 0.9646
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0412 - accuracy: 0.9862 - val_loss: 0.1487 - val_accuracy: 0.9647
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0405 - accuracy: 0.9865 - val_loss: 0.1482 - val_accuracy: 0.9647
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 222/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0398 - accuracy: 0.9868 - val_loss: 0.1478 - val_accuracy: 0.9646
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0391 - accuracy: 0.9870 - val_loss: 0.1475 - val_accuracy: 0.9648
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0384 - accuracy: 0.9873 - val_loss: 0.1472 - val_accuracy: 0.9648
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 225/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0378 - accuracy: 0.9874 - val_loss: 0.1469 - val_accuracy: 0.9650
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 226/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0372 - accuracy: 0.9877 - val_loss: 0.1467 - val_accuracy: 0.9655
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 227/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0367 - accuracy: 0.9877 - val_loss: 0.1465 - val_accuracy: 0.9653
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0362 - accuracy: 0.9880 - val_loss: 0.1464 - val_accuracy: 0.9653
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 229/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0356 - accuracy: 0.9882 - val_loss: 0.1463 - val_accuracy: 0.9654
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 230/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0352 - accuracy: 0.9884 - val_loss: 0.1463 - val_accuracy: 0.9654
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 231/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0347 - accuracy: 0.9888 - val_loss: 0.1463 - val_accuracy: 0.9655
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0343 - accuracy: 0.9890 - val_loss: 0.1463 - val_accuracy: 0.9660
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 233/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0338 - accuracy: 0.9891 - val_loss: 0.1463 - val_accuracy: 0.9661
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0334 - accuracy: 0.9892 - val_loss: 0.1464 - val_accuracy: 0.9662
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0330 - accuracy: 0.9894 - val_loss: 0.1465 - val_accuracy: 0.9663
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 236/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0326 - accuracy: 0.9894 - val_loss: 0.1467 - val_accuracy: 0.9663
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 237/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0322 - accuracy: 0.9895 - val_loss: 0.1468 - val_accuracy: 0.9663
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 238/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0318 - accuracy: 0.9897 - val_loss: 0.1471 - val_accuracy: 0.9664
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 239/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0315 - accuracy: 0.9898 - val_loss: 0.1473 - val_accuracy: 0.9664
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0311 - accuracy: 0.9900 - val_loss: 0.1475 - val_accuracy: 0.9662
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0308 - accuracy: 0.9901 - val_loss: 0.1477 - val_accuracy: 0.9664
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 242/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0304 - accuracy: 0.9902 - val_loss: 0.1480 - val_accuracy: 0.9666
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 243/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0301 - accuracy: 0.9905 - val_loss: 0.1482 - val_accuracy: 0.9666
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0298 - accuracy: 0.9906 - val_loss: 0.1485 - val_accuracy: 0.9668
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 245/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0295 - accuracy: 0.9908 - val_loss: 0.1488 - val_accuracy: 0.9667
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 246/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0292 - accuracy: 0.9909 - val_loss: 0.1491 - val_accuracy: 0.9666
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 247/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0289 - accuracy: 0.9911 - val_loss: 0.1494 - val_accuracy: 0.9666
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 248/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0286 - accuracy: 0.9912 - val_loss: 0.1497 - val_accuracy: 0.9666
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 249/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0283 - accuracy: 0.9913 - val_loss: 0.1501 - val_accuracy: 0.9665
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0281 - accuracy: 0.9915 - val_loss: 0.1504 - val_accuracy: 0.9665
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 251/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4551 - accuracy: 0.8622 - val_loss: 0.3081 - val_accuracy: 0.9078
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2601 - accuracy: 0.9160 - val_loss: 0.2643 - val_accuracy: 0.9209
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 253/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2282 - accuracy: 0.9267 - val_loss: 0.2439 - val_accuracy: 0.9266
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 254/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2111 - accuracy: 0.9327 - val_loss: 0.2313 - val_accuracy: 0.9314
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1999 - accuracy: 0.9361 - val_loss: 0.2226 - val_accuracy: 0.9340
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 256/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1917 - accuracy: 0.9386 - val_loss: 0.2160 - val_accuracy: 0.9355
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 257/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1853 - accuracy: 0.9403 - val_loss: 0.2108 - val_accuracy: 0.9367
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 258/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1802 - accuracy: 0.9423 - val_loss: 0.2065 - val_accuracy: 0.9377
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1759 - accuracy: 0.9444 - val_loss: 0.2029 - val_accuracy: 0.9388
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1722 - accuracy: 0.9456 - val_loss: 0.1999 - val_accuracy: 0.9398
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 261/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1690 - accuracy: 0.9468 - val_loss: 0.1972 - val_accuracy: 0.9400
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 262/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1661 - accuracy: 0.9478 - val_loss: 0.1949 - val_accuracy: 0.9414
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1636 - accuracy: 0.9488 - val_loss: 0.1928 - val_accuracy: 0.9423
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 264/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1613 - accuracy: 0.9495 - val_loss: 0.1910 - val_accuracy: 0.9436
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 265/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1592 - accuracy: 0.9501 - val_loss: 0.1894 - val_accuracy: 0.9445
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1573 - accuracy: 0.9505 - val_loss: 0.1879 - val_accuracy: 0.9446
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 267/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1555 - accuracy: 0.9511 - val_loss: 0.1865 - val_accuracy: 0.9453
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1539 - accuracy: 0.9517 - val_loss: 0.1853 - val_accuracy: 0.9459
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1524 - accuracy: 0.9524 - val_loss: 0.1842 - val_accuracy: 0.9464
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1510 - accuracy: 0.9527 - val_loss: 0.1831 - val_accuracy: 0.9468
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1497 - accuracy: 0.9531 - val_loss: 0.1821 - val_accuracy: 0.9471
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1484 - accuracy: 0.9535 - val_loss: 0.1812 - val_accuracy: 0.9477
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 273/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1473 - accuracy: 0.9537 - val_loss: 0.1804 - val_accuracy: 0.9482
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 274/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1462 - accuracy: 0.9539 - val_loss: 0.1796 - val_accuracy: 0.9479
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 275/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1451 - accuracy: 0.9542 - val_loss: 0.1788 - val_accuracy: 0.9479
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 276/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1442 - accuracy: 0.9547 - val_loss: 0.1781 - val_accuracy: 0.9483
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1432 - accuracy: 0.9547 - val_loss: 0.1775 - val_accuracy: 0.9485
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1423 - accuracy: 0.9550 - val_loss: 0.1768 - val_accuracy: 0.9487
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1415 - accuracy: 0.9552 - val_loss: 0.1762 - val_accuracy: 0.9487
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 280/500
235/235 [==============================] - 2s 10ms/step - loss: 0.1407 - accuracy: 0.9554 - val_loss: 0.1757 - val_accuracy: 0.9489
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1399 - accuracy: 0.9556 - val_loss: 0.1752 - val_accuracy: 0.9487
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 282/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1392 - accuracy: 0.9556 - val_loss: 0.1747 - val_accuracy: 0.9487
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1385 - accuracy: 0.9559 - val_loss: 0.1742 - val_accuracy: 0.9487
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 284/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1378 - accuracy: 0.9561 - val_loss: 0.1737 - val_accuracy: 0.9486
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1371 - accuracy: 0.9564 - val_loss: 0.1733 - val_accuracy: 0.9484
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 286/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1365 - accuracy: 0.9566 - val_loss: 0.1729 - val_accuracy: 0.9483
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 287/500
235/235 [==============================] - 2s 10ms/step - loss: 0.1359 - accuracy: 0.9568 - val_loss: 0.1725 - val_accuracy: 0.9483
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1353 - accuracy: 0.9571 - val_loss: 0.1721 - val_accuracy: 0.9482
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1348 - accuracy: 0.9573 - val_loss: 0.1718 - val_accuracy: 0.9488
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 290/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1342 - accuracy: 0.9574 - val_loss: 0.1715 - val_accuracy: 0.9489
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 291/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1337 - accuracy: 0.9577 - val_loss: 0.1711 - val_accuracy: 0.9488
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1332 - accuracy: 0.9579 - val_loss: 0.1708 - val_accuracy: 0.9488
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1327 - accuracy: 0.9580 - val_loss: 0.1706 - val_accuracy: 0.9492
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 294/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1322 - accuracy: 0.9581 - val_loss: 0.1703 - val_accuracy: 0.9492
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 295/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1318 - accuracy: 0.9583 - val_loss: 0.1701 - val_accuracy: 0.9492
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1313 - accuracy: 0.9584 - val_loss: 0.1698 - val_accuracy: 0.9491
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 297/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1309 - accuracy: 0.9584 - val_loss: 0.1696 - val_accuracy: 0.9494
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 298/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1305 - accuracy: 0.9584 - val_loss: 0.1694 - val_accuracy: 0.9494
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 299/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1301 - accuracy: 0.9585 - val_loss: 0.1692 - val_accuracy: 0.9500
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 300/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1297 - accuracy: 0.9585 - val_loss: 0.1691 - val_accuracy: 0.9502
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 301/500
235/235 [==============================] - 2s 9ms/step - loss: 0.7589 - accuracy: 0.7692 - val_loss: 0.5609 - val_accuracy: 0.8279
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 302/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5612 - accuracy: 0.8243 - val_loss: 0.5208 - val_accuracy: 0.8420
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 303/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5315 - accuracy: 0.8343 - val_loss: 0.5017 - val_accuracy: 0.8481
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 304/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5139 - accuracy: 0.8406 - val_loss: 0.4888 - val_accuracy: 0.8536
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 305/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5014 - accuracy: 0.8444 - val_loss: 0.4793 - val_accuracy: 0.8570
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 306/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4924 - accuracy: 0.8477 - val_loss: 0.4726 - val_accuracy: 0.8586
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 307/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4858 - accuracy: 0.8496 - val_loss: 0.4675 - val_accuracy: 0.8606
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4807 - accuracy: 0.8510 - val_loss: 0.4635 - val_accuracy: 0.8619
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 309/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4763 - accuracy: 0.8522 - val_loss: 0.4600 - val_accuracy: 0.8636
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 310/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4726 - accuracy: 0.8534 - val_loss: 0.4569 - val_accuracy: 0.8650
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 311/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4692 - accuracy: 0.8546 - val_loss: 0.4542 - val_accuracy: 0.8655
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 312/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4660 - accuracy: 0.8553 - val_loss: 0.4516 - val_accuracy: 0.8660
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 313/500
235/235 [==============================] - 3s 11ms/step - loss: 0.4630 - accuracy: 0.8560 - val_loss: 0.4491 - val_accuracy: 0.8671
[-0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 314/500
235/235 [==============================] - 2s 10ms/step - loss: 0.4599 - accuracy: 0.8574 - val_loss: 0.4467 - val_accuracy: 0.8676
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 315/500
235/235 [==============================] - 2s 10ms/step - loss: 0.4570 - accuracy: 0.8583 - val_loss: 0.4444 - val_accuracy: 0.8671
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 316/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4542 - accuracy: 0.8592 - val_loss: 0.4423 - val_accuracy: 0.8683
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 317/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4515 - accuracy: 0.8602 - val_loss: 0.4403 - val_accuracy: 0.8690
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 318/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4490 - accuracy: 0.8609 - val_loss: 0.4384 - val_accuracy: 0.8698
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 319/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4465 - accuracy: 0.8619 - val_loss: 0.4366 - val_accuracy: 0.8703
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 320/500
235/235 [==============================] - 2s 10ms/step - loss: 0.4442 - accuracy: 0.8628 - val_loss: 0.4350 - val_accuracy: 0.8704
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 321/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4418 - accuracy: 0.8637 - val_loss: 0.4335 - val_accuracy: 0.8709
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 322/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4396 - accuracy: 0.8648 - val_loss: 0.4322 - val_accuracy: 0.8713
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 323/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4377 - accuracy: 0.8652 - val_loss: 0.4311 - val_accuracy: 0.8713
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 324/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4361 - accuracy: 0.8656 - val_loss: 0.4302 - val_accuracy: 0.8719
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 325/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4346 - accuracy: 0.8660 - val_loss: 0.4294 - val_accuracy: 0.8719
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 326/500
235/235 [==============================] - 2s 10ms/step - loss: 0.4333 - accuracy: 0.8665 - val_loss: 0.4286 - val_accuracy: 0.8720
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 327/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4321 - accuracy: 0.8668 - val_loss: 0.4278 - val_accuracy: 0.8723
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4308 - accuracy: 0.8673 - val_loss: 0.4271 - val_accuracy: 0.8727
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4296 - accuracy: 0.8677 - val_loss: 0.4264 - val_accuracy: 0.8734
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 330/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4285 - accuracy: 0.8684 - val_loss: 0.4257 - val_accuracy: 0.8739
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4274 - accuracy: 0.8688 - val_loss: 0.4250 - val_accuracy: 0.8743
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 332/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4263 - accuracy: 0.8690 - val_loss: 0.4243 - val_accuracy: 0.8742
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4253 - accuracy: 0.8691 - val_loss: 0.4236 - val_accuracy: 0.8748
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 334/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4242 - accuracy: 0.8694 - val_loss: 0.4230 - val_accuracy: 0.8753
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 335/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4232 - accuracy: 0.8698 - val_loss: 0.4224 - val_accuracy: 0.8759
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 336/500
235/235 [==============================] - 2s 10ms/step - loss: 0.4223 - accuracy: 0.8703 - val_loss: 0.4218 - val_accuracy: 0.8759
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4214 - accuracy: 0.8706 - val_loss: 0.4213 - val_accuracy: 0.8755
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4206 - accuracy: 0.8707 - val_loss: 0.4208 - val_accuracy: 0.8758
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4199 - accuracy: 0.8711 - val_loss: 0.4204 - val_accuracy: 0.8766
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4191 - accuracy: 0.8715 - val_loss: 0.4199 - val_accuracy: 0.8769
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 341/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4185 - accuracy: 0.8715 - val_loss: 0.4196 - val_accuracy: 0.8769
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 342/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4178 - accuracy: 0.8717 - val_loss: 0.4191 - val_accuracy: 0.8767
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 343/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4172 - accuracy: 0.8720 - val_loss: 0.4188 - val_accuracy: 0.8768
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 344/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4166 - accuracy: 0.8721 - val_loss: 0.4184 - val_accuracy: 0.8767
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 345/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4160 - accuracy: 0.8721 - val_loss: 0.4181 - val_accuracy: 0.8771
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4155 - accuracy: 0.8724 - val_loss: 0.4178 - val_accuracy: 0.8773
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 347/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4149 - accuracy: 0.8725 - val_loss: 0.4174 - val_accuracy: 0.8776
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4144 - accuracy: 0.8726 - val_loss: 0.4171 - val_accuracy: 0.8778
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4139 - accuracy: 0.8729 - val_loss: 0.4169 - val_accuracy: 0.8778
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4135 - accuracy: 0.8731 - val_loss: 0.4166 - val_accuracy: 0.8777
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 351/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4278 - accuracy: 0.5314 - val_loss: 1.2303 - val_accuracy: 0.5995
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 352/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2782 - accuracy: 0.5742 - val_loss: 1.2069 - val_accuracy: 0.5901
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 353/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2582 - accuracy: 0.5782 - val_loss: 1.1920 - val_accuracy: 0.5952
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 354/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2414 - accuracy: 0.5774 - val_loss: 1.1757 - val_accuracy: 0.6140
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 355/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2246 - accuracy: 0.5794 - val_loss: 1.1603 - val_accuracy: 0.6154
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 356/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2125 - accuracy: 0.5820 - val_loss: 1.1502 - val_accuracy: 0.6159
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 357/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2053 - accuracy: 0.5844 - val_loss: 1.1443 - val_accuracy: 0.6160
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 358/500
235/235 [==============================] - 2s 10ms/step - loss: 1.2009 - accuracy: 0.5855 - val_loss: 1.1407 - val_accuracy: 0.6175
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1980 - accuracy: 0.5858 - val_loss: 1.1383 - val_accuracy: 0.6175
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 360/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1958 - accuracy: 0.5857 - val_loss: 1.1365 - val_accuracy: 0.6176
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1941 - accuracy: 0.5864 - val_loss: 1.1351 - val_accuracy: 0.6179
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 362/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1926 - accuracy: 0.5867 - val_loss: 1.1338 - val_accuracy: 0.6185
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1914 - accuracy: 0.5868 - val_loss: 1.1327 - val_accuracy: 0.6188
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1901 - accuracy: 0.5870 - val_loss: 1.1317 - val_accuracy: 0.6189
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 365/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1890 - accuracy: 0.5878 - val_loss: 1.1308 - val_accuracy: 0.6192
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1880 - accuracy: 0.5886 - val_loss: 1.1299 - val_accuracy: 0.6197
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1870 - accuracy: 0.5892 - val_loss: 1.1290 - val_accuracy: 0.6200
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1860 - accuracy: 0.5901 - val_loss: 1.1282 - val_accuracy: 0.6206
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1851 - accuracy: 0.5906 - val_loss: 1.1274 - val_accuracy: 0.6211
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1841 - accuracy: 0.5912 - val_loss: 1.1265 - val_accuracy: 0.6213
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1830 - accuracy: 0.5907 - val_loss: 1.1254 - val_accuracy: 0.6215
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 372/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1813 - accuracy: 0.5906 - val_loss: 1.1236 - val_accuracy: 0.6222
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 373/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1779 - accuracy: 0.5918 - val_loss: 1.1194 - val_accuracy: 0.6231
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 374/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1720 - accuracy: 0.5929 - val_loss: 1.1128 - val_accuracy: 0.6250
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1666 - accuracy: 0.5934 - val_loss: 1.1087 - val_accuracy: 0.6267
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 376/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1637 - accuracy: 0.5940 - val_loss: 1.1058 - val_accuracy: 0.6288
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 377/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1610 - accuracy: 0.5943 - val_loss: 1.1032 - val_accuracy: 0.6300
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 378/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1588 - accuracy: 0.5950 - val_loss: 1.1013 - val_accuracy: 0.6305
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1573 - accuracy: 0.5950 - val_loss: 1.0999 - val_accuracy: 0.6315
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1560 - accuracy: 0.5958 - val_loss: 1.0989 - val_accuracy: 0.6313
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 381/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1550 - accuracy: 0.5963 - val_loss: 1.0981 - val_accuracy: 0.6322
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 382/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1540 - accuracy: 0.5969 - val_loss: 1.0973 - val_accuracy: 0.6333
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1531 - accuracy: 0.5969 - val_loss: 1.0967 - val_accuracy: 0.6331
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1523 - accuracy: 0.5970 - val_loss: 1.0960 - val_accuracy: 0.6341
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 385/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1516 - accuracy: 0.5974 - val_loss: 1.0955 - val_accuracy: 0.6342
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1509 - accuracy: 0.5977 - val_loss: 1.0948 - val_accuracy: 0.6344
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 387/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1502 - accuracy: 0.5977 - val_loss: 1.0944 - val_accuracy: 0.6345
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 388/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1496 - accuracy: 0.5980 - val_loss: 1.0939 - val_accuracy: 0.6347
[-0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1490 - accuracy: 0.5984 - val_loss: 1.0934 - val_accuracy: 0.6351
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1484 - accuracy: 0.5984 - val_loss: 1.0931 - val_accuracy: 0.6356
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 391/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1480 - accuracy: 0.5989 - val_loss: 1.0927 - val_accuracy: 0.6361
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 392/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1475 - accuracy: 0.5990 - val_loss: 1.0923 - val_accuracy: 0.6363
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1471 - accuracy: 0.5985 - val_loss: 1.0920 - val_accuracy: 0.6357
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1467 - accuracy: 0.5987 - val_loss: 1.0917 - val_accuracy: 0.6362
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 395/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1463 - accuracy: 0.5989 - val_loss: 1.0914 - val_accuracy: 0.6362
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 396/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1459 - accuracy: 0.5994 - val_loss: 1.0912 - val_accuracy: 0.6359
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1456 - accuracy: 0.5995 - val_loss: 1.0909 - val_accuracy: 0.6361
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1453 - accuracy: 0.5997 - val_loss: 1.0906 - val_accuracy: 0.6363
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1450 - accuracy: 0.5998 - val_loss: 1.0904 - val_accuracy: 0.6370
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1447 - accuracy: 0.5995 - val_loss: 1.0901 - val_accuracy: 0.6372
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 401/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7532 - accuracy: 0.3609 - val_loss: 1.7031 - val_accuracy: 0.3777
[-0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 402/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7150 - accuracy: 0.3575 - val_loss: 1.6967 - val_accuracy: 0.3784
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 403/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7108 - accuracy: 0.3538 - val_loss: 1.6943 - val_accuracy: 0.3788
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 404/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7089 - accuracy: 0.3538 - val_loss: 1.6929 - val_accuracy: 0.3789
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7075 - accuracy: 0.3546 - val_loss: 1.6916 - val_accuracy: 0.3791
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 406/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7065 - accuracy: 0.3551 - val_loss: 1.6907 - val_accuracy: 0.3793
[-0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 407/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7056 - accuracy: 0.3557 - val_loss: 1.6899 - val_accuracy: 0.3797
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 408/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7048 - accuracy: 0.3563 - val_loss: 1.6892 - val_accuracy: 0.3797
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7041 - accuracy: 0.3574 - val_loss: 1.6886 - val_accuracy: 0.3798
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 410/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7035 - accuracy: 0.3589 - val_loss: 1.6881 - val_accuracy: 0.3798
[-0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 411/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7030 - accuracy: 0.3596 - val_loss: 1.6876 - val_accuracy: 0.3801
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7025 - accuracy: 0.3618 - val_loss: 1.6872 - val_accuracy: 0.3801
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 413/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7021 - accuracy: 0.3636 - val_loss: 1.6868 - val_accuracy: 0.3801
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7017 - accuracy: 0.3659 - val_loss: 1.6865 - val_accuracy: 0.3805
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7014 - accuracy: 0.3664 - val_loss: 1.6862 - val_accuracy: 0.3808
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 416/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7011 - accuracy: 0.3679 - val_loss: 1.6860 - val_accuracy: 0.3808
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 417/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7008 - accuracy: 0.3681 - val_loss: 1.6857 - val_accuracy: 0.3808
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 418/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7006 - accuracy: 0.3692 - val_loss: 1.6855 - val_accuracy: 0.3809
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 419/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7003 - accuracy: 0.3697 - val_loss: 1.6853 - val_accuracy: 0.3810
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7001 - accuracy: 0.3700 - val_loss: 1.6852 - val_accuracy: 0.3814
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6999 - accuracy: 0.3711 - val_loss: 1.6850 - val_accuracy: 0.3813
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6997 - accuracy: 0.3717 - val_loss: 1.6848 - val_accuracy: 0.3814
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 423/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6995 - accuracy: 0.3709 - val_loss: 1.6847 - val_accuracy: 0.3817
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6993 - accuracy: 0.3719 - val_loss: 1.6845 - val_accuracy: 0.3818
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6991 - accuracy: 0.3723 - val_loss: 1.6844 - val_accuracy: 0.3818
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 426/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6989 - accuracy: 0.3721 - val_loss: 1.6842 - val_accuracy: 0.3818
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 427/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6988 - accuracy: 0.3726 - val_loss: 1.6842 - val_accuracy: 0.3818
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 428/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6986 - accuracy: 0.3731 - val_loss: 1.6840 - val_accuracy: 0.3817
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 429/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6985 - accuracy: 0.3731 - val_loss: 1.6838 - val_accuracy: 0.3816
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 430/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6983 - accuracy: 0.3731 - val_loss: 1.6838 - val_accuracy: 0.3816
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 431/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6982 - accuracy: 0.3732 - val_loss: 1.6835 - val_accuracy: 0.3817
[-0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6981 - accuracy: 0.3729 - val_loss: 1.6835 - val_accuracy: 0.3817
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6979 - accuracy: 0.3735 - val_loss: 1.6835 - val_accuracy: 0.3817
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6978 - accuracy: 0.3742 - val_loss: 1.6833 - val_accuracy: 0.3816
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 435/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6977 - accuracy: 0.3746 - val_loss: 1.6832 - val_accuracy: 0.3818
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 436/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6976 - accuracy: 0.3752 - val_loss: 1.6831 - val_accuracy: 0.3818
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 437/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6975 - accuracy: 0.3752 - val_loss: 1.6831 - val_accuracy: 0.3819
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6974 - accuracy: 0.3758 - val_loss: 1.6830 - val_accuracy: 0.3819
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 439/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6973 - accuracy: 0.3761 - val_loss: 1.6828 - val_accuracy: 0.3820
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 440/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6972 - accuracy: 0.3764 - val_loss: 1.6827 - val_accuracy: 0.3819
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6971 - accuracy: 0.3771 - val_loss: 1.6827 - val_accuracy: 0.3820
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 442/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6970 - accuracy: 0.3764 - val_loss: 1.6826 - val_accuracy: 0.3820
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 443/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6969 - accuracy: 0.3760 - val_loss: 1.6825 - val_accuracy: 0.3820
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6969 - accuracy: 0.3760 - val_loss: 1.6825 - val_accuracy: 0.3819
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6968 - accuracy: 0.3764 - val_loss: 1.6824 - val_accuracy: 0.3819
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6967 - accuracy: 0.3772 - val_loss: 1.6824 - val_accuracy: 0.3820
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 447/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6966 - accuracy: 0.3764 - val_loss: 1.6822 - val_accuracy: 0.3821
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 448/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6965 - accuracy: 0.3776 - val_loss: 1.6822 - val_accuracy: 0.3822
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 449/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6965 - accuracy: 0.3771 - val_loss: 1.6822 - val_accuracy: 0.3822
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6964 - accuracy: 0.3769 - val_loss: 1.6821 - val_accuracy: 0.3823
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 451/500
235/235 [==============================] - 2s 10ms/step - loss: 1.6963 - accuracy: 0.3770 - val_loss: 1.6819 - val_accuracy: 0.3823
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6963 - accuracy: 0.3772 - val_loss: 1.6819 - val_accuracy: 0.3823
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6962 - accuracy: 0.3774 - val_loss: 1.6818 - val_accuracy: 0.3825
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6961 - accuracy: 0.3778 - val_loss: 1.6819 - val_accuracy: 0.3825
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 455/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6961 - accuracy: 0.3779 - val_loss: 1.6818 - val_accuracy: 0.3825
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 456/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6960 - accuracy: 0.3775 - val_loss: 1.6817 - val_accuracy: 0.3825
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 457/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6960 - accuracy: 0.3774 - val_loss: 1.6817 - val_accuracy: 0.3827
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 458/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6959 - accuracy: 0.3783 - val_loss: 1.6817 - val_accuracy: 0.3827
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 459/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6959 - accuracy: 0.3776 - val_loss: 1.6815 - val_accuracy: 0.3827
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 460/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6958 - accuracy: 0.3776 - val_loss: 1.6815 - val_accuracy: 0.3827
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 461/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6957 - accuracy: 0.3779 - val_loss: 1.6814 - val_accuracy: 0.3826
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 462/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6957 - accuracy: 0.3783 - val_loss: 1.6814 - val_accuracy: 0.3825
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 463/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6956 - accuracy: 0.3774 - val_loss: 1.6813 - val_accuracy: 0.3828
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 464/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6956 - accuracy: 0.3779 - val_loss: 1.6813 - val_accuracy: 0.3827
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 465/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6955 - accuracy: 0.3781 - val_loss: 1.6813 - val_accuracy: 0.3829
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 466/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6955 - accuracy: 0.3777 - val_loss: 1.6812 - val_accuracy: 0.3830
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 467/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6954 - accuracy: 0.3783 - val_loss: 1.6812 - val_accuracy: 0.3832
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 468/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6954 - accuracy: 0.3780 - val_loss: 1.6810 - val_accuracy: 0.3833
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 469/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6954 - accuracy: 0.3783 - val_loss: 1.6811 - val_accuracy: 0.3832
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 470/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6953 - accuracy: 0.3781 - val_loss: 1.6810 - val_accuracy: 0.3832
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 471/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6953 - accuracy: 0.3785 - val_loss: 1.6810 - val_accuracy: 0.3832
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6953 - accuracy: 0.3783 - val_loss: 1.6811 - val_accuracy: 0.3833
[-0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 473/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6952 - accuracy: 0.3783 - val_loss: 1.6810 - val_accuracy: 0.3834
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 474/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6952 - accuracy: 0.3789 - val_loss: 1.6809 - val_accuracy: 0.3833
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 475/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6951 - accuracy: 0.3795 - val_loss: 1.6809 - val_accuracy: 0.3833
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 476/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6951 - accuracy: 0.3786 - val_loss: 1.6809 - val_accuracy: 0.3835
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 477/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6950 - accuracy: 0.3786 - val_loss: 1.6809 - val_accuracy: 0.3833
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 478/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6950 - accuracy: 0.3790 - val_loss: 1.6809 - val_accuracy: 0.3834
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 479/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6950 - accuracy: 0.3785 - val_loss: 1.6808 - val_accuracy: 0.3835
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 480/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6949 - accuracy: 0.3793 - val_loss: 1.6807 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6949 - accuracy: 0.3788 - val_loss: 1.6806 - val_accuracy: 0.3837
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 482/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6949 - accuracy: 0.3787 - val_loss: 1.6806 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 483/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6948 - accuracy: 0.3786 - val_loss: 1.6806 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 484/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6948 - accuracy: 0.3790 - val_loss: 1.6807 - val_accuracy: 0.3837
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 485/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6948 - accuracy: 0.3791 - val_loss: 1.6806 - val_accuracy: 0.3837
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 486/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6947 - accuracy: 0.3790 - val_loss: 1.6805 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 487/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6947 - accuracy: 0.3785 - val_loss: 1.6805 - val_accuracy: 0.3837
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 488/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6947 - accuracy: 0.3800 - val_loss: 1.6805 - val_accuracy: 0.3837
[-0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 489/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6947 - accuracy: 0.3800 - val_loss: 1.6804 - val_accuracy: 0.3835
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 490/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6946 - accuracy: 0.3795 - val_loss: 1.6804 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 491/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6946 - accuracy: 0.3794 - val_loss: 1.6804 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 492/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6946 - accuracy: 0.3786 - val_loss: 1.6803 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 493/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6946 - accuracy: 0.3793 - val_loss: 1.6803 - val_accuracy: 0.3839
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6945 - accuracy: 0.3787 - val_loss: 1.6803 - val_accuracy: 0.3839
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 495/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6945 - accuracy: 0.3788 - val_loss: 1.6803 - val_accuracy: 0.3839
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3790 - val_loss: 1.6802 - val_accuracy: 0.3839
[-0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3787 - val_loss: 1.6803 - val_accuracy: 0.3839
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3788 - val_loss: 1.6802 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 499/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3794 - val_loss: 1.6802 - val_accuracy: 0.3839
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 500/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3794 - val_loss: 1.6800 - val_accuracy: 0.3838
[-0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 1/200
235/235 [==============================] - 4s 15ms/step - loss: 2.1454 - accuracy: 0.9292 - val_loss: 1.5733 - val_accuracy: 0.8402
Epoch 2/200
235/235 [==============================] - 3s 14ms/step - loss: 0.4344 - accuracy: 0.9587 - val_loss: 0.4834 - val_accuracy: 0.9416
Epoch 3/200
235/235 [==============================] - 3s 14ms/step - loss: 0.3107 - accuracy: 0.9630 - val_loss: 0.3306 - val_accuracy: 0.9510
Epoch 4/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2776 - accuracy: 0.9658 - val_loss: 0.2861 - val_accuracy: 0.9585
Epoch 5/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2595 - accuracy: 0.9677 - val_loss: 0.3229 - val_accuracy: 0.9442
Epoch 6/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2490 - accuracy: 0.9685 - val_loss: 0.3016 - val_accuracy: 0.9507
Epoch 7/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2356 - accuracy: 0.9698 - val_loss: 0.2860 - val_accuracy: 0.9521
Epoch 8/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2342 - accuracy: 0.9692 - val_loss: 0.2749 - val_accuracy: 0.9546
Epoch 9/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2272 - accuracy: 0.9701 - val_loss: 0.3155 - val_accuracy: 0.9394
Epoch 10/200
235/235 [==============================] - 3s 15ms/step - loss: 0.2191 - accuracy: 0.9715 - val_loss: 0.2557 - val_accuracy: 0.9580
Epoch 11/200
235/235 [==============================] - 3s 15ms/step - loss: 0.2132 - accuracy: 0.9708 - val_loss: 0.2929 - val_accuracy: 0.9461
Epoch 12/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2107 - accuracy: 0.9707 - val_loss: 0.2492 - val_accuracy: 0.9615
Epoch 13/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2056 - accuracy: 0.9714 - val_loss: 0.2650 - val_accuracy: 0.9516
Epoch 14/200
235/235 [==============================] - 3s 15ms/step - loss: 0.2018 - accuracy: 0.9722 - val_loss: 0.2707 - val_accuracy: 0.9499
Epoch 15/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1976 - accuracy: 0.9724 - val_loss: 0.2322 - val_accuracy: 0.9613
Epoch 16/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1952 - accuracy: 0.9740 - val_loss: 0.2493 - val_accuracy: 0.9553
Epoch 17/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1937 - accuracy: 0.9730 - val_loss: 0.2286 - val_accuracy: 0.9620
Epoch 18/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1929 - accuracy: 0.9732 - val_loss: 0.2577 - val_accuracy: 0.9524
Epoch 19/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1893 - accuracy: 0.9729 - val_loss: 0.2270 - val_accuracy: 0.9597
Epoch 20/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1850 - accuracy: 0.9732 - val_loss: 0.2624 - val_accuracy: 0.9532
Epoch 21/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1820 - accuracy: 0.9741 - val_loss: 0.2538 - val_accuracy: 0.9544
Epoch 22/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1824 - accuracy: 0.9741 - val_loss: 0.2694 - val_accuracy: 0.9493
Epoch 23/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1848 - accuracy: 0.9738 - val_loss: 0.2395 - val_accuracy: 0.9566
Epoch 24/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1793 - accuracy: 0.9747 - val_loss: 0.2493 - val_accuracy: 0.9511
Epoch 25/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1785 - accuracy: 0.9743 - val_loss: 0.2484 - val_accuracy: 0.9512
Epoch 26/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1769 - accuracy: 0.9737 - val_loss: 0.3107 - val_accuracy: 0.9352
Epoch 27/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1776 - accuracy: 0.9739 - val_loss: 0.2413 - val_accuracy: 0.9544
Epoch 28/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1743 - accuracy: 0.9739 - val_loss: 0.3283 - val_accuracy: 0.9286
Epoch 29/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1760 - accuracy: 0.9736 - val_loss: 0.2230 - val_accuracy: 0.9601
Epoch 30/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1758 - accuracy: 0.9734 - val_loss: 0.2404 - val_accuracy: 0.9568
Epoch 31/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1710 - accuracy: 0.9750 - val_loss: 0.2401 - val_accuracy: 0.9529
Epoch 32/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1698 - accuracy: 0.9749 - val_loss: 0.2294 - val_accuracy: 0.9559
Epoch 33/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1674 - accuracy: 0.9751 - val_loss: 0.2717 - val_accuracy: 0.9467
Epoch 34/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1661 - accuracy: 0.9754 - val_loss: 0.2432 - val_accuracy: 0.9539
Epoch 35/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1704 - accuracy: 0.9742 - val_loss: 0.2476 - val_accuracy: 0.9520
Epoch 36/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1688 - accuracy: 0.9742 - val_loss: 0.2607 - val_accuracy: 0.9479
Epoch 37/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1701 - accuracy: 0.9742 - val_loss: 0.2286 - val_accuracy: 0.9570
Epoch 38/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1649 - accuracy: 0.9759 - val_loss: 0.2328 - val_accuracy: 0.9570
Epoch 39/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1640 - accuracy: 0.9758 - val_loss: 0.2212 - val_accuracy: 0.9599
Epoch 40/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1650 - accuracy: 0.9755 - val_loss: 0.2457 - val_accuracy: 0.9516
Epoch 41/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1644 - accuracy: 0.9754 - val_loss: 0.2491 - val_accuracy: 0.9514
Epoch 42/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1654 - accuracy: 0.9756 - val_loss: 0.2391 - val_accuracy: 0.9514
Epoch 43/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1626 - accuracy: 0.9764 - val_loss: 0.2387 - val_accuracy: 0.9552
Epoch 44/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1641 - accuracy: 0.9755 - val_loss: 0.2481 - val_accuracy: 0.9510
Epoch 45/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1594 - accuracy: 0.9768 - val_loss: 0.2200 - val_accuracy: 0.9595
Epoch 46/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1634 - accuracy: 0.9754 - val_loss: 0.2393 - val_accuracy: 0.9543
Epoch 47/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1613 - accuracy: 0.9761 - val_loss: 0.2159 - val_accuracy: 0.9595
Epoch 48/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1647 - accuracy: 0.9747 - val_loss: 0.2254 - val_accuracy: 0.9568
Epoch 49/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1635 - accuracy: 0.9751 - val_loss: 0.2694 - val_accuracy: 0.9426
Epoch 50/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1612 - accuracy: 0.9759 - val_loss: 0.2214 - val_accuracy: 0.9583
Epoch 51/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1601 - accuracy: 0.9759 - val_loss: 0.2061 - val_accuracy: 0.9644
Epoch 52/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1610 - accuracy: 0.9752 - val_loss: 0.2485 - val_accuracy: 0.9499
Epoch 53/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1631 - accuracy: 0.9753 - val_loss: 0.2383 - val_accuracy: 0.9524
Epoch 54/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1612 - accuracy: 0.9754 - val_loss: 0.2578 - val_accuracy: 0.9454
Epoch 55/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1566 - accuracy: 0.9762 - val_loss: 0.2446 - val_accuracy: 0.9484
Epoch 56/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1603 - accuracy: 0.9762 - val_loss: 0.2704 - val_accuracy: 0.9429
Epoch 57/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1574 - accuracy: 0.9762 - val_loss: 0.2118 - val_accuracy: 0.9625
Epoch 58/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1547 - accuracy: 0.9775 - val_loss: 0.1978 - val_accuracy: 0.9657
Epoch 59/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1558 - accuracy: 0.9765 - val_loss: 0.2498 - val_accuracy: 0.9508
Epoch 60/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1561 - accuracy: 0.9757 - val_loss: 0.2385 - val_accuracy: 0.9529
Epoch 61/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1567 - accuracy: 0.9759 - val_loss: 0.2399 - val_accuracy: 0.9491
Epoch 62/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1576 - accuracy: 0.9756 - val_loss: 0.2146 - val_accuracy: 0.9606
Epoch 63/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1542 - accuracy: 0.9772 - val_loss: 0.2141 - val_accuracy: 0.9583
Epoch 64/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1541 - accuracy: 0.9766 - val_loss: 0.2473 - val_accuracy: 0.9496
Epoch 65/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9763 - val_loss: 0.2284 - val_accuracy: 0.9561
Epoch 66/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1557 - accuracy: 0.9765 - val_loss: 0.2047 - val_accuracy: 0.9614
Epoch 67/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1556 - accuracy: 0.9766 - val_loss: 0.2492 - val_accuracy: 0.9483
Epoch 68/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9772 - val_loss: 0.2081 - val_accuracy: 0.9612
Epoch 69/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1523 - accuracy: 0.9771 - val_loss: 0.2365 - val_accuracy: 0.9500
Epoch 70/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1542 - accuracy: 0.9767 - val_loss: 0.2797 - val_accuracy: 0.9389
Epoch 71/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1527 - accuracy: 0.9765 - val_loss: 0.2338 - val_accuracy: 0.9516
Epoch 72/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1556 - accuracy: 0.9764 - val_loss: 0.2479 - val_accuracy: 0.9493
Epoch 73/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1551 - accuracy: 0.9761 - val_loss: 0.2443 - val_accuracy: 0.9508
Epoch 74/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1552 - accuracy: 0.9768 - val_loss: 0.2480 - val_accuracy: 0.9498
Epoch 75/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1494 - accuracy: 0.9775 - val_loss: 0.2251 - val_accuracy: 0.9538
Epoch 76/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1486 - accuracy: 0.9777 - val_loss: 0.2226 - val_accuracy: 0.9582
Epoch 77/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1546 - accuracy: 0.9762 - val_loss: 0.2502 - val_accuracy: 0.9468
Epoch 78/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1532 - accuracy: 0.9773 - val_loss: 0.2542 - val_accuracy: 0.9496
Epoch 79/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1511 - accuracy: 0.9776 - val_loss: 0.2119 - val_accuracy: 0.9593
Epoch 80/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9788 - val_loss: 0.2458 - val_accuracy: 0.9505
Epoch 81/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1529 - accuracy: 0.9768 - val_loss: 0.2041 - val_accuracy: 0.9616
Epoch 82/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1510 - accuracy: 0.9771 - val_loss: 0.2390 - val_accuracy: 0.9509
Epoch 83/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1547 - accuracy: 0.9764 - val_loss: 0.2209 - val_accuracy: 0.9595
Epoch 84/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1482 - accuracy: 0.9776 - val_loss: 0.2052 - val_accuracy: 0.9619
Epoch 85/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1504 - accuracy: 0.9779 - val_loss: 0.2250 - val_accuracy: 0.9558
Epoch 86/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1514 - accuracy: 0.9770 - val_loss: 0.2591 - val_accuracy: 0.9439
Epoch 87/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1472 - accuracy: 0.9786 - val_loss: 0.2446 - val_accuracy: 0.9498
Epoch 88/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1489 - accuracy: 0.9776 - val_loss: 0.2769 - val_accuracy: 0.9383
Epoch 89/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1526 - accuracy: 0.9767 - val_loss: 0.2197 - val_accuracy: 0.9561
Epoch 90/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1492 - accuracy: 0.9780 - val_loss: 0.2546 - val_accuracy: 0.9445
Epoch 91/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1479 - accuracy: 0.9780 - val_loss: 0.2169 - val_accuracy: 0.9590
Epoch 92/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1492 - accuracy: 0.9779 - val_loss: 0.2458 - val_accuracy: 0.9514
Epoch 93/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1499 - accuracy: 0.9775 - val_loss: 0.2426 - val_accuracy: 0.9517
Epoch 94/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1532 - accuracy: 0.9767 - val_loss: 0.2327 - val_accuracy: 0.9566
Epoch 95/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1528 - accuracy: 0.9768 - val_loss: 0.2457 - val_accuracy: 0.9495
Epoch 96/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1509 - accuracy: 0.9768 - val_loss: 0.2344 - val_accuracy: 0.9545
Epoch 97/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1492 - accuracy: 0.9780 - val_loss: 0.2101 - val_accuracy: 0.9599
Epoch 98/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1499 - accuracy: 0.9775 - val_loss: 0.2216 - val_accuracy: 0.9580
Epoch 99/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1538 - accuracy: 0.9758 - val_loss: 0.2166 - val_accuracy: 0.9592
Epoch 100/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1528 - accuracy: 0.9762 - val_loss: 0.2394 - val_accuracy: 0.9541
Epoch 101/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1484 - accuracy: 0.9775 - val_loss: 0.2232 - val_accuracy: 0.9561
Epoch 102/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1500 - accuracy: 0.9770 - val_loss: 0.2295 - val_accuracy: 0.9521
Epoch 103/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9769 - val_loss: 0.2177 - val_accuracy: 0.9548
Epoch 104/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1463 - accuracy: 0.9775 - val_loss: 0.2079 - val_accuracy: 0.9599
Epoch 105/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1490 - accuracy: 0.9770 - val_loss: 0.2246 - val_accuracy: 0.9572
Epoch 106/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1463 - accuracy: 0.9780 - val_loss: 0.2344 - val_accuracy: 0.9522
Epoch 107/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1489 - accuracy: 0.9777 - val_loss: 0.2412 - val_accuracy: 0.9512
Epoch 108/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9770 - val_loss: 0.2176 - val_accuracy: 0.9569
Epoch 109/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9772 - val_loss: 0.2274 - val_accuracy: 0.9543
Epoch 110/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1505 - accuracy: 0.9762 - val_loss: 0.1942 - val_accuracy: 0.9644
Epoch 111/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1473 - accuracy: 0.9782 - val_loss: 0.2001 - val_accuracy: 0.9625
Epoch 112/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9790 - val_loss: 0.2319 - val_accuracy: 0.9524
Epoch 113/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1452 - accuracy: 0.9780 - val_loss: 0.2360 - val_accuracy: 0.9524
Epoch 114/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1465 - accuracy: 0.9779 - val_loss: 0.2237 - val_accuracy: 0.9567
Epoch 115/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9771 - val_loss: 0.1986 - val_accuracy: 0.9617
Epoch 116/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1438 - accuracy: 0.9783 - val_loss: 0.2101 - val_accuracy: 0.9596
Epoch 117/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9778 - val_loss: 0.2186 - val_accuracy: 0.9567
Epoch 118/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1456 - accuracy: 0.9781 - val_loss: 0.2434 - val_accuracy: 0.9476
Epoch 119/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1454 - accuracy: 0.9778 - val_loss: 0.2260 - val_accuracy: 0.9543
Epoch 120/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1473 - accuracy: 0.9773 - val_loss: 0.2305 - val_accuracy: 0.9521
Epoch 121/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1465 - accuracy: 0.9776 - val_loss: 0.2227 - val_accuracy: 0.9558
Epoch 122/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1468 - accuracy: 0.9776 - val_loss: 0.2129 - val_accuracy: 0.9596
Epoch 123/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1465 - accuracy: 0.9774 - val_loss: 0.2051 - val_accuracy: 0.9610
Epoch 124/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1457 - accuracy: 0.9780 - val_loss: 0.2657 - val_accuracy: 0.9438
Epoch 125/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1448 - accuracy: 0.9785 - val_loss: 0.2179 - val_accuracy: 0.9563
Epoch 126/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1447 - accuracy: 0.9782 - val_loss: 0.2275 - val_accuracy: 0.9540
Epoch 127/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9784 - val_loss: 0.2255 - val_accuracy: 0.9562
Epoch 128/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1442 - accuracy: 0.9782 - val_loss: 0.2351 - val_accuracy: 0.9556
Epoch 129/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1445 - accuracy: 0.9787 - val_loss: 0.1881 - val_accuracy: 0.9660
Epoch 130/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1454 - accuracy: 0.9775 - val_loss: 0.2121 - val_accuracy: 0.9586
Epoch 131/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1447 - accuracy: 0.9778 - val_loss: 0.2044 - val_accuracy: 0.9612
Epoch 132/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1449 - accuracy: 0.9783 - val_loss: 0.2028 - val_accuracy: 0.9613
Epoch 133/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1419 - accuracy: 0.9785 - val_loss: 0.2153 - val_accuracy: 0.9574
Epoch 134/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1471 - accuracy: 0.9774 - val_loss: 0.2053 - val_accuracy: 0.9593
Epoch 135/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1478 - accuracy: 0.9769 - val_loss: 0.2054 - val_accuracy: 0.9625
Epoch 136/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1392 - accuracy: 0.9793 - val_loss: 0.2158 - val_accuracy: 0.9561
Epoch 137/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1431 - accuracy: 0.9777 - val_loss: 0.2155 - val_accuracy: 0.9577
Epoch 138/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1444 - accuracy: 0.9783 - val_loss: 0.2105 - val_accuracy: 0.9586
Epoch 139/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1437 - accuracy: 0.9782 - val_loss: 0.2069 - val_accuracy: 0.9613
Epoch 140/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1458 - accuracy: 0.9781 - val_loss: 0.2048 - val_accuracy: 0.9597
Epoch 141/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1443 - accuracy: 0.9779 - val_loss: 0.2253 - val_accuracy: 0.9547
Epoch 142/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9793 - val_loss: 0.1924 - val_accuracy: 0.9650
Epoch 143/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1415 - accuracy: 0.9786 - val_loss: 0.1856 - val_accuracy: 0.9668
Epoch 144/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1454 - accuracy: 0.9780 - val_loss: 0.2002 - val_accuracy: 0.9626
Epoch 145/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1435 - accuracy: 0.9778 - val_loss: 0.2282 - val_accuracy: 0.9558
Epoch 146/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1420 - accuracy: 0.9789 - val_loss: 0.2057 - val_accuracy: 0.9629
Epoch 147/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1403 - accuracy: 0.9786 - val_loss: 0.2261 - val_accuracy: 0.9534
Epoch 148/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9792 - val_loss: 0.2279 - val_accuracy: 0.9549
Epoch 149/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9782 - val_loss: 0.1964 - val_accuracy: 0.9647
Epoch 150/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1449 - accuracy: 0.9779 - val_loss: 0.2233 - val_accuracy: 0.9583
Epoch 151/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9792 - val_loss: 0.1941 - val_accuracy: 0.9630
Epoch 152/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1427 - accuracy: 0.9781 - val_loss: 0.2011 - val_accuracy: 0.9619
Epoch 153/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9786 - val_loss: 0.2251 - val_accuracy: 0.9518
Epoch 154/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1412 - accuracy: 0.9784 - val_loss: 0.2162 - val_accuracy: 0.9594
Epoch 155/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9790 - val_loss: 0.1850 - val_accuracy: 0.9654
Epoch 156/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9788 - val_loss: 0.1949 - val_accuracy: 0.9633
Epoch 157/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1456 - accuracy: 0.9771 - val_loss: 0.1856 - val_accuracy: 0.9668
Epoch 158/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9789 - val_loss: 0.1855 - val_accuracy: 0.9664
Epoch 159/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2397 - val_accuracy: 0.9488
Epoch 160/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9785 - val_loss: 0.1933 - val_accuracy: 0.9657
Epoch 161/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2132 - val_accuracy: 0.9591
Epoch 162/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9790 - val_loss: 0.2009 - val_accuracy: 0.9607
Epoch 163/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1420 - accuracy: 0.9784 - val_loss: 0.2454 - val_accuracy: 0.9478
Epoch 164/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9786 - val_loss: 0.1971 - val_accuracy: 0.9617
Epoch 165/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1398 - accuracy: 0.9786 - val_loss: 0.2046 - val_accuracy: 0.9618
Epoch 166/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9788 - val_loss: 0.1899 - val_accuracy: 0.9648
Epoch 167/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9790 - val_loss: 0.1959 - val_accuracy: 0.9634
Epoch 168/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9781 - val_loss: 0.2380 - val_accuracy: 0.9520
Epoch 169/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9791 - val_loss: 0.2065 - val_accuracy: 0.9599
Epoch 170/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1390 - accuracy: 0.9794 - val_loss: 0.2059 - val_accuracy: 0.9628
Epoch 171/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9788 - val_loss: 0.2194 - val_accuracy: 0.9562
Epoch 172/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9785 - val_loss: 0.2068 - val_accuracy: 0.9629
Epoch 173/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1400 - accuracy: 0.9787 - val_loss: 0.2196 - val_accuracy: 0.9562
Epoch 174/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1407 - accuracy: 0.9782 - val_loss: 0.2099 - val_accuracy: 0.9608
Epoch 175/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1439 - accuracy: 0.9776 - val_loss: 0.2251 - val_accuracy: 0.9536
Epoch 176/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9792 - val_loss: 0.2119 - val_accuracy: 0.9573
Epoch 177/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1434 - accuracy: 0.9780 - val_loss: 0.1996 - val_accuracy: 0.9627
Epoch 178/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1402 - accuracy: 0.9792 - val_loss: 0.1966 - val_accuracy: 0.9630
Epoch 179/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1451 - accuracy: 0.9774 - val_loss: 0.2435 - val_accuracy: 0.9473
Epoch 180/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1368 - accuracy: 0.9797 - val_loss: 0.2024 - val_accuracy: 0.9589
Epoch 181/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1427 - accuracy: 0.9784 - val_loss: 0.1977 - val_accuracy: 0.9635
Epoch 182/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1409 - accuracy: 0.9782 - val_loss: 0.2096 - val_accuracy: 0.9580
Epoch 183/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1418 - accuracy: 0.9786 - val_loss: 0.1945 - val_accuracy: 0.9645
Epoch 184/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1403 - accuracy: 0.9787 - val_loss: 0.1930 - val_accuracy: 0.9656
Epoch 185/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1409 - accuracy: 0.9786 - val_loss: 0.2056 - val_accuracy: 0.9606
Epoch 186/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9786 - val_loss: 0.2137 - val_accuracy: 0.9607
Epoch 187/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1407 - accuracy: 0.9782 - val_loss: 0.2217 - val_accuracy: 0.9553
Epoch 188/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1417 - accuracy: 0.9785 - val_loss: 0.1866 - val_accuracy: 0.9662
Epoch 189/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1376 - accuracy: 0.9794 - val_loss: 0.1943 - val_accuracy: 0.9643
Epoch 190/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1408 - accuracy: 0.9786 - val_loss: 0.2086 - val_accuracy: 0.9593
Epoch 191/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9795 - val_loss: 0.2375 - val_accuracy: 0.9539
Epoch 192/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9789 - val_loss: 0.2054 - val_accuracy: 0.9635
Epoch 193/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9775 - val_loss: 0.1843 - val_accuracy: 0.9652
Epoch 194/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9790 - val_loss: 0.1963 - val_accuracy: 0.9619
Epoch 195/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9797 - val_loss: 0.2048 - val_accuracy: 0.9639
Epoch 196/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1429 - accuracy: 0.9785 - val_loss: 0.1878 - val_accuracy: 0.9661
Epoch 197/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1387 - accuracy: 0.9794 - val_loss: 0.1953 - val_accuracy: 0.9618
Epoch 198/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9800 - val_loss: 0.2030 - val_accuracy: 0.9630
Epoch 199/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1399 - accuracy: 0.9787 - val_loss: 0.1882 - val_accuracy: 0.9658
Epoch 200/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1406 - accuracy: 0.9788 - val_loss: 0.2303 - val_accuracy: 0.9534
Epoch 1/200
235/235 [==============================] - 4s 14ms/step - loss: 0.2384 - accuracy: 0.9303 - val_loss: 0.2010 - val_accuracy: 0.9554
Epoch 2/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0871 - accuracy: 0.9750 - val_loss: 0.1023 - val_accuracy: 0.9665
Epoch 3/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0501 - accuracy: 0.9861 - val_loss: 0.0894 - val_accuracy: 0.9705
Epoch 4/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0298 - accuracy: 0.9924 - val_loss: 0.0839 - val_accuracy: 0.9724
Epoch 5/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0200 - accuracy: 0.9951 - val_loss: 0.0908 - val_accuracy: 0.9726
Epoch 6/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0130 - accuracy: 0.9971 - val_loss: 0.0931 - val_accuracy: 0.9723
Epoch 7/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0134 - accuracy: 0.9966 - val_loss: 0.0942 - val_accuracy: 0.9725
Epoch 8/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0115 - accuracy: 0.9969 - val_loss: 0.0914 - val_accuracy: 0.9741
Epoch 9/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0103 - accuracy: 0.9972 - val_loss: 0.1034 - val_accuracy: 0.9734
Epoch 10/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0116 - accuracy: 0.9966 - val_loss: 0.1107 - val_accuracy: 0.9722
Epoch 11/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0103 - accuracy: 0.9968 - val_loss: 0.1069 - val_accuracy: 0.9740
Epoch 12/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0072 - accuracy: 0.9980 - val_loss: 0.0904 - val_accuracy: 0.9765
Epoch 13/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.0980 - val_accuracy: 0.9767
Epoch 14/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 0.0875 - val_accuracy: 0.9795
Epoch 15/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.0960 - val_accuracy: 0.9771
Epoch 16/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0079 - accuracy: 0.9974 - val_loss: 0.1077 - val_accuracy: 0.9734
Epoch 17/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0120 - accuracy: 0.9959 - val_loss: 0.1104 - val_accuracy: 0.9750
Epoch 18/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0068 - accuracy: 0.9979 - val_loss: 0.0943 - val_accuracy: 0.9773
Epoch 19/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0049 - accuracy: 0.9987 - val_loss: 0.0864 - val_accuracy: 0.9796
Epoch 20/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 0.0743 - val_accuracy: 0.9816
Epoch 21/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0851 - val_accuracy: 0.9806
Epoch 22/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.0884 - val_accuracy: 0.9794
Epoch 23/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.0971 - val_accuracy: 0.9786
Epoch 24/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0099 - accuracy: 0.9965 - val_loss: 0.1217 - val_accuracy: 0.9747
Epoch 25/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 0.1144 - val_accuracy: 0.9746
Epoch 26/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0062 - accuracy: 0.9980 - val_loss: 0.0859 - val_accuracy: 0.9788
Epoch 27/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.0804 - val_accuracy: 0.9826
Epoch 28/200
235/235 [==============================] - 3s 14ms/step - loss: 9.8967e-04 - accuracy: 0.9998 - val_loss: 0.0820 - val_accuracy: 0.9831
Epoch 29/200
235/235 [==============================] - 3s 14ms/step - loss: 6.4159e-04 - accuracy: 0.9999 - val_loss: 0.0772 - val_accuracy: 0.9846
Epoch 30/200
235/235 [==============================] - 3s 14ms/step - loss: 2.2164e-04 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9845
Epoch 31/200
235/235 [==============================] - 4s 16ms/step - loss: 3.0036e-04 - accuracy: 1.0000 - val_loss: 0.0815 - val_accuracy: 0.9835
Epoch 32/200
235/235 [==============================] - 4s 16ms/step - loss: 1.4568e-04 - accuracy: 1.0000 - val_loss: 0.0789 - val_accuracy: 0.9846
Epoch 33/200
235/235 [==============================] - 3s 14ms/step - loss: 8.9280e-05 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9848
Epoch 34/200
235/235 [==============================] - 3s 14ms/step - loss: 1.0255e-04 - accuracy: 1.0000 - val_loss: 0.0791 - val_accuracy: 0.9850
Epoch 35/200
235/235 [==============================] - 3s 14ms/step - loss: 9.4754e-05 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9843
Epoch 36/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1177 - val_accuracy: 0.9768
Epoch 37/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0331 - accuracy: 0.9898 - val_loss: 0.1184 - val_accuracy: 0.9743
Epoch 38/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0096 - accuracy: 0.9968 - val_loss: 0.0839 - val_accuracy: 0.9811
Epoch 39/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.0739 - val_accuracy: 0.9848
Epoch 40/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0768 - val_accuracy: 0.9846
Epoch 41/200
235/235 [==============================] - 3s 14ms/step - loss: 3.7544e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9844
Epoch 42/200
235/235 [==============================] - 3s 14ms/step - loss: 2.0662e-04 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9849
Epoch 43/200
235/235 [==============================] - 3s 14ms/step - loss: 1.5944e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9852
Epoch 44/200
235/235 [==============================] - 3s 14ms/step - loss: 1.5341e-04 - accuracy: 1.0000 - val_loss: 0.0766 - val_accuracy: 0.9847
Epoch 45/200
235/235 [==============================] - 3s 14ms/step - loss: 1.0698e-04 - accuracy: 1.0000 - val_loss: 0.0758 - val_accuracy: 0.9846
Epoch 46/200
235/235 [==============================] - 3s 14ms/step - loss: 9.2168e-05 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9848
Epoch 47/200
235/235 [==============================] - 3s 14ms/step - loss: 9.0834e-05 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9856
Epoch 48/200
235/235 [==============================] - 3s 14ms/step - loss: 6.9801e-05 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9853
Epoch 49/200
235/235 [==============================] - 3s 14ms/step - loss: 6.3549e-05 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9849
Epoch 50/200
235/235 [==============================] - 3s 14ms/step - loss: 5.3713e-05 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9851
Epoch 51/200
235/235 [==============================] - 3s 14ms/step - loss: 5.3301e-05 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9850
Epoch 52/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0171 - accuracy: 0.9942 - val_loss: 0.2425 - val_accuracy: 0.9558
Epoch 53/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0211 - accuracy: 0.9932 - val_loss: 0.0956 - val_accuracy: 0.9784
Epoch 54/200
235/235 [==============================] - 4s 16ms/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.0769 - val_accuracy: 0.9837
Epoch 55/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0721 - val_accuracy: 0.9840
Epoch 56/200
235/235 [==============================] - 3s 14ms/step - loss: 4.5208e-04 - accuracy: 1.0000 - val_loss: 0.0742 - val_accuracy: 0.9845
Epoch 57/200
235/235 [==============================] - 3s 14ms/step - loss: 3.0806e-04 - accuracy: 1.0000 - val_loss: 0.0732 - val_accuracy: 0.9851
Epoch 58/200
235/235 [==============================] - 3s 14ms/step - loss: 1.9521e-04 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9848
Epoch 59/200
235/235 [==============================] - 3s 14ms/step - loss: 1.3770e-04 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9853
Epoch 60/200
235/235 [==============================] - 3s 14ms/step - loss: 1.1612e-04 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9851
Epoch 61/200
235/235 [==============================] - 3s 14ms/step - loss: 1.1397e-04 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9848
Epoch 62/200
235/235 [==============================] - 4s 15ms/step - loss: 9.9060e-05 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9852
Epoch 63/200
235/235 [==============================] - 3s 15ms/step - loss: 7.3421e-05 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9851
Epoch 64/200
235/235 [==============================] - 3s 15ms/step - loss: 6.7283e-05 - accuracy: 1.0000 - val_loss: 0.0758 - val_accuracy: 0.9848
Epoch 65/200
235/235 [==============================] - 3s 14ms/step - loss: 6.2857e-05 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9853
Epoch 66/200
235/235 [==============================] - 3s 15ms/step - loss: 8.5222e-05 - accuracy: 1.0000 - val_loss: 0.0800 - val_accuracy: 0.9845
Epoch 67/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0126 - accuracy: 0.9958 - val_loss: 0.1294 - val_accuracy: 0.9720
Epoch 68/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0129 - accuracy: 0.9957 - val_loss: 0.1015 - val_accuracy: 0.9787
Epoch 69/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.0826 - val_accuracy: 0.9823
Epoch 70/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0810 - val_accuracy: 0.9840
Epoch 71/200
235/235 [==============================] - 3s 15ms/step - loss: 4.6130e-04 - accuracy: 0.9999 - val_loss: 0.0782 - val_accuracy: 0.9852
Epoch 72/200
235/235 [==============================] - 3s 15ms/step - loss: 3.7710e-04 - accuracy: 0.9999 - val_loss: 0.0778 - val_accuracy: 0.9852
Epoch 73/200
235/235 [==============================] - 3s 15ms/step - loss: 7.1448e-04 - accuracy: 0.9998 - val_loss: 0.0780 - val_accuracy: 0.9842
Epoch 74/200
235/235 [==============================] - 3s 15ms/step - loss: 3.8315e-04 - accuracy: 0.9999 - val_loss: 0.0807 - val_accuracy: 0.9844
Epoch 75/200
235/235 [==============================] - 3s 15ms/step - loss: 1.6320e-04 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9853
Epoch 76/200
235/235 [==============================] - 3s 15ms/step - loss: 9.9596e-05 - accuracy: 1.0000 - val_loss: 0.0769 - val_accuracy: 0.9854
Epoch 77/200
235/235 [==============================] - 3s 15ms/step - loss: 6.8125e-05 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9853
Epoch 78/200
235/235 [==============================] - 3s 15ms/step - loss: 6.1544e-05 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9856
Epoch 79/200
235/235 [==============================] - 3s 14ms/step - loss: 5.1015e-05 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9859
Epoch 80/200
235/235 [==============================] - 3s 14ms/step - loss: 4.9467e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9853
Epoch 81/200
235/235 [==============================] - 3s 14ms/step - loss: 3.9085e-05 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9854
Epoch 82/200
235/235 [==============================] - 3s 14ms/step - loss: 4.3078e-05 - accuracy: 1.0000 - val_loss: 0.0777 - val_accuracy: 0.9863
Epoch 83/200
235/235 [==============================] - 3s 14ms/step - loss: 3.9389e-05 - accuracy: 1.0000 - val_loss: 0.0783 - val_accuracy: 0.9854
Epoch 84/200
235/235 [==============================] - 3s 14ms/step - loss: 4.2268e-05 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9854
Epoch 85/200
235/235 [==============================] - 3s 14ms/step - loss: 2.9745e-05 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9857
Epoch 86/200
235/235 [==============================] - 3s 14ms/step - loss: 2.3704e-05 - accuracy: 1.0000 - val_loss: 0.0797 - val_accuracy: 0.9855
Epoch 87/200
235/235 [==============================] - 3s 14ms/step - loss: 2.4212e-05 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9855
Epoch 88/200
235/235 [==============================] - 3s 14ms/step - loss: 2.3469e-05 - accuracy: 1.0000 - val_loss: 0.0813 - val_accuracy: 0.9857
Epoch 89/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0085 - accuracy: 0.9972 - val_loss: 0.2356 - val_accuracy: 0.9604
Epoch 90/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0206 - accuracy: 0.9933 - val_loss: 0.0964 - val_accuracy: 0.9806
Epoch 91/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.0872 - val_accuracy: 0.9829
Epoch 92/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0866 - val_accuracy: 0.9849
Epoch 93/200
235/235 [==============================] - 3s 14ms/step - loss: 4.5601e-04 - accuracy: 0.9999 - val_loss: 0.0859 - val_accuracy: 0.9854
Epoch 94/200
235/235 [==============================] - 4s 15ms/step - loss: 3.6784e-04 - accuracy: 0.9999 - val_loss: 0.0862 - val_accuracy: 0.9851
Epoch 95/200
235/235 [==============================] - 3s 14ms/step - loss: 5.1643e-04 - accuracy: 0.9999 - val_loss: 0.0896 - val_accuracy: 0.9841
Epoch 96/200
235/235 [==============================] - 3s 14ms/step - loss: 4.9737e-04 - accuracy: 0.9998 - val_loss: 0.0857 - val_accuracy: 0.9840
Epoch 97/200
235/235 [==============================] - 3s 14ms/step - loss: 2.2418e-04 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9845
Epoch 98/200
235/235 [==============================] - 3s 14ms/step - loss: 1.1549e-04 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9849
Epoch 99/200
235/235 [==============================] - 3s 14ms/step - loss: 1.8224e-04 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9840
Epoch 100/200
235/235 [==============================] - 3s 14ms/step - loss: 1.0491e-04 - accuracy: 1.0000 - val_loss: 0.0859 - val_accuracy: 0.9848
Epoch 101/200
235/235 [==============================] - 3s 14ms/step - loss: 7.2000e-05 - accuracy: 1.0000 - val_loss: 0.0867 - val_accuracy: 0.9847
Epoch 102/200
235/235 [==============================] - 3s 14ms/step - loss: 2.3033e-04 - accuracy: 0.9999 - val_loss: 0.1052 - val_accuracy: 0.9824
Epoch 103/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0061 - accuracy: 0.9982 - val_loss: 0.1388 - val_accuracy: 0.9756
Epoch 104/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0081 - accuracy: 0.9971 - val_loss: 0.1142 - val_accuracy: 0.9796
Epoch 105/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.0971 - val_accuracy: 0.9821
Epoch 106/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0942 - val_accuracy: 0.9838
Epoch 107/200
235/235 [==============================] - 3s 14ms/step - loss: 3.4603e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9839
Epoch 108/200
235/235 [==============================] - 3s 14ms/step - loss: 1.3253e-04 - accuracy: 1.0000 - val_loss: 0.0913 - val_accuracy: 0.9845
Epoch 109/200
235/235 [==============================] - 3s 14ms/step - loss: 8.0258e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9844
Epoch 110/200
235/235 [==============================] - 3s 14ms/step - loss: 6.0365e-05 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9848
Epoch 111/200
235/235 [==============================] - 3s 14ms/step - loss: 5.5904e-05 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9847
Epoch 112/200
235/235 [==============================] - 3s 14ms/step - loss: 4.7209e-05 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9846
Epoch 113/200
235/235 [==============================] - 3s 14ms/step - loss: 3.8688e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9846
Epoch 114/200
235/235 [==============================] - 3s 14ms/step - loss: 3.4847e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9847
Epoch 115/200
235/235 [==============================] - 3s 14ms/step - loss: 3.1833e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9842
Epoch 116/200
235/235 [==============================] - 3s 14ms/step - loss: 3.2284e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9847
Epoch 117/200
235/235 [==============================] - 3s 14ms/step - loss: 8.8095e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9849
Epoch 118/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 0.1481 - val_accuracy: 0.9771
Epoch 119/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0104 - accuracy: 0.9967 - val_loss: 0.1260 - val_accuracy: 0.9777
Epoch 120/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0030 - accuracy: 0.9990 - val_loss: 0.0953 - val_accuracy: 0.9842
Epoch 121/200
235/235 [==============================] - 3s 14ms/step - loss: 7.4479e-04 - accuracy: 0.9999 - val_loss: 0.0909 - val_accuracy: 0.9843
Epoch 122/200
235/235 [==============================] - 3s 14ms/step - loss: 5.4389e-04 - accuracy: 0.9999 - val_loss: 0.0924 - val_accuracy: 0.9848
Epoch 123/200
235/235 [==============================] - 3s 14ms/step - loss: 1.9079e-04 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9840
Epoch 124/200
235/235 [==============================] - 3s 14ms/step - loss: 3.1363e-04 - accuracy: 0.9999 - val_loss: 0.0920 - val_accuracy: 0.9846
Epoch 125/200
235/235 [==============================] - 3s 14ms/step - loss: 9.8832e-05 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9848
Epoch 126/200
235/235 [==============================] - 3s 14ms/step - loss: 7.4385e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9850
Epoch 127/200
235/235 [==============================] - 3s 14ms/step - loss: 1.0119e-04 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9848
Epoch 128/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1018 - val_accuracy: 0.9830
Epoch 129/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1237 - val_accuracy: 0.9785
Epoch 130/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0969 - val_accuracy: 0.9831
Epoch 131/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0954 - val_accuracy: 0.9829
Epoch 132/200
235/235 [==============================] - 3s 14ms/step - loss: 6.9090e-04 - accuracy: 0.9998 - val_loss: 0.0944 - val_accuracy: 0.9841
Epoch 133/200
235/235 [==============================] - 3s 14ms/step - loss: 3.8857e-04 - accuracy: 0.9999 - val_loss: 0.0875 - val_accuracy: 0.9851
Epoch 134/200
235/235 [==============================] - 3s 14ms/step - loss: 1.9984e-04 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9857
Epoch 135/200
235/235 [==============================] - 3s 14ms/step - loss: 1.3966e-04 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9849
Epoch 136/200
235/235 [==============================] - 3s 14ms/step - loss: 1.5426e-04 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9852
Epoch 137/200
235/235 [==============================] - 3s 14ms/step - loss: 3.9932e-05 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9851
Epoch 138/200
235/235 [==============================] - 3s 14ms/step - loss: 7.6793e-05 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9848
Epoch 139/200
235/235 [==============================] - 3s 14ms/step - loss: 4.4028e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9852
Epoch 140/200
235/235 [==============================] - 3s 14ms/step - loss: 2.4411e-05 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9856
Epoch 141/200
235/235 [==============================] - 3s 14ms/step - loss: 2.0525e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9857
Epoch 142/200
235/235 [==============================] - 3s 14ms/step - loss: 1.5722e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9857
Epoch 143/200
235/235 [==============================] - 3s 14ms/step - loss: 4.3466e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9858
Epoch 144/200
235/235 [==============================] - 3s 14ms/step - loss: 6.1547e-04 - accuracy: 0.9999 - val_loss: 0.0944 - val_accuracy: 0.9838
Epoch 145/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0120 - accuracy: 0.9965 - val_loss: 0.1296 - val_accuracy: 0.9772
Epoch 146/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0047 - accuracy: 0.9983 - val_loss: 0.0920 - val_accuracy: 0.9833
Epoch 147/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0891 - val_accuracy: 0.9847
Epoch 148/200
235/235 [==============================] - 3s 14ms/step - loss: 3.8984e-04 - accuracy: 0.9999 - val_loss: 0.0888 - val_accuracy: 0.9850
Epoch 149/200
235/235 [==============================] - 3s 14ms/step - loss: 3.1552e-04 - accuracy: 0.9999 - val_loss: 0.0900 - val_accuracy: 0.9838
Epoch 150/200
235/235 [==============================] - 3s 14ms/step - loss: 3.1804e-04 - accuracy: 0.9999 - val_loss: 0.0899 - val_accuracy: 0.9845
Epoch 151/200
235/235 [==============================] - 3s 14ms/step - loss: 8.4239e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9843
Epoch 152/200
235/235 [==============================] - 4s 15ms/step - loss: 5.2126e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9847
Epoch 153/200
235/235 [==============================] - 3s 15ms/step - loss: 3.9892e-05 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9848
Epoch 154/200
235/235 [==============================] - 3s 15ms/step - loss: 4.0656e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9853
Epoch 155/200
235/235 [==============================] - 3s 15ms/step - loss: 2.8978e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9851
Epoch 156/200
235/235 [==============================] - 3s 15ms/step - loss: 6.8577e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9850
Epoch 157/200
235/235 [==============================] - 3s 15ms/step - loss: 4.0521e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9849
Epoch 158/200
235/235 [==============================] - 3s 15ms/step - loss: 5.2547e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9852
Epoch 159/200
235/235 [==============================] - 3s 15ms/step - loss: 3.3802e-05 - accuracy: 1.0000 - val_loss: 0.0922 - val_accuracy: 0.9848
Epoch 160/200
235/235 [==============================] - 4s 15ms/step - loss: 2.2985e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9845
Epoch 161/200
235/235 [==============================] - 3s 15ms/step - loss: 1.6751e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9846
Epoch 162/200
235/235 [==============================] - 3s 15ms/step - loss: 1.3570e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9850
Epoch 163/200
235/235 [==============================] - 3s 15ms/step - loss: 1.2749e-05 - accuracy: 1.0000 - val_loss: 0.0928 - val_accuracy: 0.9848
Epoch 164/200
235/235 [==============================] - 3s 15ms/step - loss: 1.0201e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9850
Epoch 165/200
235/235 [==============================] - 3s 15ms/step - loss: 9.3159e-06 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9850
Epoch 166/200
235/235 [==============================] - 4s 15ms/step - loss: 1.1591e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9853
Epoch 167/200
235/235 [==============================] - 4s 15ms/step - loss: 9.3892e-06 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9854
Epoch 168/200
235/235 [==============================] - 3s 15ms/step - loss: 7.1178e-06 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9854
Epoch 169/200
235/235 [==============================] - 3s 14ms/step - loss: 5.9696e-06 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9856
Epoch 170/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1682 - val_accuracy: 0.9740
Epoch 171/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0106 - accuracy: 0.9965 - val_loss: 0.1030 - val_accuracy: 0.9819
Epoch 172/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0997 - val_accuracy: 0.9827
Epoch 173/200
235/235 [==============================] - 3s 14ms/step - loss: 8.0874e-04 - accuracy: 0.9998 - val_loss: 0.0946 - val_accuracy: 0.9841
Epoch 174/200
235/235 [==============================] - 3s 14ms/step - loss: 1.9233e-04 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9844
Epoch 175/200
235/235 [==============================] - 3s 14ms/step - loss: 1.3721e-04 - accuracy: 1.0000 - val_loss: 0.0914 - val_accuracy: 0.9852
Epoch 176/200
235/235 [==============================] - 3s 14ms/step - loss: 6.8103e-05 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9849
Epoch 177/200
235/235 [==============================] - 3s 14ms/step - loss: 6.0059e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9851
Epoch 178/200
235/235 [==============================] - 3s 14ms/step - loss: 7.3392e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9856
Epoch 179/200
235/235 [==============================] - 3s 14ms/step - loss: 2.5223e-04 - accuracy: 0.9999 - val_loss: 0.0937 - val_accuracy: 0.9851
Epoch 180/200
235/235 [==============================] - 3s 14ms/step - loss: 9.0932e-04 - accuracy: 0.9998 - val_loss: 0.1063 - val_accuracy: 0.9825
Epoch 181/200
235/235 [==============================] - 3s 14ms/step - loss: 8.9146e-04 - accuracy: 0.9998 - val_loss: 0.1189 - val_accuracy: 0.9815
Epoch 182/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.1149 - val_accuracy: 0.9814
Epoch 183/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1123 - val_accuracy: 0.9827
Epoch 184/200
235/235 [==============================] - 3s 14ms/step - loss: 8.5703e-04 - accuracy: 0.9997 - val_loss: 0.1159 - val_accuracy: 0.9817
Epoch 185/200
235/235 [==============================] - 3s 14ms/step - loss: 7.5192e-04 - accuracy: 0.9998 - val_loss: 0.1070 - val_accuracy: 0.9843
Epoch 186/200
235/235 [==============================] - 3s 14ms/step - loss: 6.4805e-04 - accuracy: 0.9998 - val_loss: 0.1091 - val_accuracy: 0.9829
Epoch 187/200
235/235 [==============================] - 3s 14ms/step - loss: 9.2249e-04 - accuracy: 0.9997 - val_loss: 0.1023 - val_accuracy: 0.9833
Epoch 188/200
235/235 [==============================] - 3s 14ms/step - loss: 9.0278e-04 - accuracy: 0.9997 - val_loss: 0.1110 - val_accuracy: 0.9810
Epoch 189/200
235/235 [==============================] - 3s 14ms/step - loss: 7.1587e-04 - accuracy: 0.9998 - val_loss: 0.1063 - val_accuracy: 0.9827
Epoch 190/200
235/235 [==============================] - 3s 14ms/step - loss: 8.3858e-04 - accuracy: 0.9999 - val_loss: 0.0980 - val_accuracy: 0.9840
Epoch 191/200
235/235 [==============================] - 3s 14ms/step - loss: 8.0972e-05 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9845
Epoch 192/200
235/235 [==============================] - 3s 14ms/step - loss: 1.4262e-04 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9848
Epoch 193/200
235/235 [==============================] - 3s 14ms/step - loss: 4.3506e-05 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9849
Epoch 194/200
235/235 [==============================] - 3s 14ms/step - loss: 4.1487e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9844
Epoch 195/200
235/235 [==============================] - 3s 14ms/step - loss: 2.3147e-05 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9848
Epoch 196/200
235/235 [==============================] - 3s 14ms/step - loss: 2.1125e-05 - accuracy: 1.0000 - val_loss: 0.0978 - val_accuracy: 0.9847
Epoch 197/200
235/235 [==============================] - 3s 14ms/step - loss: 1.2570e-05 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9849
Epoch 198/200
235/235 [==============================] - 3s 14ms/step - loss: 1.0638e-05 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 0.9848
Epoch 199/200
235/235 [==============================] - 3s 14ms/step - loss: 9.1066e-06 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9851
Epoch 200/200
235/235 [==============================] - 3s 12ms/step - loss: 9.9772e-06 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9851
Epoch 1/200
235/235 [==============================] - 3s 9ms/step - loss: 1.5773 - accuracy: 0.8533 - val_loss: 0.9265 - val_accuracy: 0.9021
Epoch 2/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8749 - accuracy: 0.8969 - val_loss: 0.8292 - val_accuracy: 0.9008
Epoch 3/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8351 - accuracy: 0.8977 - val_loss: 0.8140 - val_accuracy: 0.9004
Epoch 4/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8243 - accuracy: 0.8978 - val_loss: 0.8081 - val_accuracy: 0.8990
Epoch 5/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8195 - accuracy: 0.8974 - val_loss: 0.8038 - val_accuracy: 0.8982
Epoch 6/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.8977 - val_loss: 0.8009 - val_accuracy: 0.8980
Epoch 7/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.8979 - val_loss: 0.7990 - val_accuracy: 0.8985
Epoch 8/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8121 - accuracy: 0.8978 - val_loss: 0.7972 - val_accuracy: 0.8983
Epoch 9/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8110 - accuracy: 0.8977 - val_loss: 0.7958 - val_accuracy: 0.8984
Epoch 10/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8103 - accuracy: 0.8978 - val_loss: 0.7943 - val_accuracy: 0.8988
Epoch 11/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8094 - accuracy: 0.8976 - val_loss: 0.7942 - val_accuracy: 0.8993
Epoch 12/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8090 - accuracy: 0.8977 - val_loss: 0.7938 - val_accuracy: 0.8988
Epoch 13/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8082 - accuracy: 0.8977 - val_loss: 0.7922 - val_accuracy: 0.8999
Epoch 14/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8077 - accuracy: 0.8981 - val_loss: 0.7921 - val_accuracy: 0.8998
Epoch 15/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8075 - accuracy: 0.8981 - val_loss: 0.7922 - val_accuracy: 0.8991
Epoch 16/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8073 - accuracy: 0.8981 - val_loss: 0.7910 - val_accuracy: 0.8999
Epoch 17/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8070 - accuracy: 0.8977 - val_loss: 0.7903 - val_accuracy: 0.9003
Epoch 18/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8068 - accuracy: 0.8982 - val_loss: 0.7908 - val_accuracy: 0.9001
Epoch 19/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8064 - accuracy: 0.8982 - val_loss: 0.7896 - val_accuracy: 0.9008
Epoch 20/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8062 - accuracy: 0.8986 - val_loss: 0.7898 - val_accuracy: 0.9012
Epoch 21/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8061 - accuracy: 0.8985 - val_loss: 0.7890 - val_accuracy: 0.9018
Epoch 22/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8060 - accuracy: 0.8985 - val_loss: 0.7898 - val_accuracy: 0.9011
Epoch 23/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8059 - accuracy: 0.8983 - val_loss: 0.7896 - val_accuracy: 0.9009
Epoch 24/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8060 - accuracy: 0.8983 - val_loss: 0.7897 - val_accuracy: 0.9005
Epoch 25/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8986 - val_loss: 0.7895 - val_accuracy: 0.9013
Epoch 26/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8991 - val_loss: 0.7890 - val_accuracy: 0.9011
Epoch 27/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8983 - val_loss: 0.7897 - val_accuracy: 0.9007
Epoch 28/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8986 - val_loss: 0.7886 - val_accuracy: 0.9014
Epoch 29/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8990 - val_loss: 0.7895 - val_accuracy: 0.9015
Epoch 30/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8987 - val_loss: 0.7902 - val_accuracy: 0.9003
Epoch 31/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.8988 - val_loss: 0.7887 - val_accuracy: 0.9010
Epoch 32/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8986 - val_loss: 0.7886 - val_accuracy: 0.9013
Epoch 33/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8990 - val_loss: 0.7891 - val_accuracy: 0.9012
Epoch 34/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8988 - val_loss: 0.7896 - val_accuracy: 0.9007
Epoch 35/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8049 - accuracy: 0.8990 - val_loss: 0.7885 - val_accuracy: 0.9009
Epoch 36/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8990 - val_loss: 0.7882 - val_accuracy: 0.9014
Epoch 37/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8990 - val_loss: 0.7890 - val_accuracy: 0.9008
Epoch 38/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8995 - val_loss: 0.7893 - val_accuracy: 0.9008
Epoch 39/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8049 - accuracy: 0.8990 - val_loss: 0.7883 - val_accuracy: 0.9017
Epoch 40/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.8991 - val_loss: 0.7890 - val_accuracy: 0.9007
Epoch 41/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.8993 - val_loss: 0.7884 - val_accuracy: 0.9014
Epoch 42/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.8991 - val_loss: 0.7890 - val_accuracy: 0.9009
Epoch 43/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.8994 - val_loss: 0.7890 - val_accuracy: 0.9013
Epoch 44/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.8993 - val_loss: 0.7884 - val_accuracy: 0.9016
Epoch 45/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8990 - val_loss: 0.7891 - val_accuracy: 0.9016
Epoch 46/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8995 - val_loss: 0.7886 - val_accuracy: 0.9007
Epoch 47/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.8994 - val_loss: 0.7885 - val_accuracy: 0.9015
Epoch 48/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.8994 - val_loss: 0.7876 - val_accuracy: 0.9016
Epoch 49/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8997 - val_loss: 0.7873 - val_accuracy: 0.9022
Epoch 50/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8996 - val_loss: 0.7883 - val_accuracy: 0.9014
Epoch 51/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.8997 - val_loss: 0.7880 - val_accuracy: 0.9024
Epoch 52/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.8992 - val_loss: 0.7883 - val_accuracy: 0.9020
Epoch 53/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.8999 - val_loss: 0.7892 - val_accuracy: 0.9016
Epoch 54/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.8996 - val_loss: 0.7880 - val_accuracy: 0.9020
Epoch 55/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8996 - val_loss: 0.7880 - val_accuracy: 0.9024
Epoch 56/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9000 - val_loss: 0.7882 - val_accuracy: 0.9021
Epoch 57/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9000 - val_loss: 0.7883 - val_accuracy: 0.9015
Epoch 58/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9000 - val_loss: 0.7882 - val_accuracy: 0.9017
Epoch 59/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.8998 - val_loss: 0.7886 - val_accuracy: 0.9016
Epoch 60/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9000 - val_loss: 0.7883 - val_accuracy: 0.9017
Epoch 61/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9001 - val_loss: 0.7871 - val_accuracy: 0.9029
Epoch 62/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9002 - val_loss: 0.7880 - val_accuracy: 0.9027
Epoch 63/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9000 - val_loss: 0.7879 - val_accuracy: 0.9020
Epoch 64/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9003 - val_loss: 0.7873 - val_accuracy: 0.9027
Epoch 65/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.8999 - val_loss: 0.7877 - val_accuracy: 0.9026
Epoch 66/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9033
Epoch 67/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9008 - val_loss: 0.7887 - val_accuracy: 0.9024
Epoch 68/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9002 - val_loss: 0.7885 - val_accuracy: 0.9027
Epoch 69/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7892 - val_accuracy: 0.9021
Epoch 70/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9004 - val_loss: 0.7887 - val_accuracy: 0.9019
Epoch 71/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7880 - val_accuracy: 0.9025
Epoch 72/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7880 - val_accuracy: 0.9030
Epoch 73/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7876 - val_accuracy: 0.9027
Epoch 74/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9003 - val_loss: 0.7877 - val_accuracy: 0.9029
Epoch 75/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9004 - val_loss: 0.7880 - val_accuracy: 0.9035
Epoch 76/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7885 - val_accuracy: 0.9029
Epoch 77/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7880 - val_accuracy: 0.9023
Epoch 78/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9004 - val_loss: 0.7877 - val_accuracy: 0.9028
Epoch 79/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7883 - val_accuracy: 0.9023
Epoch 80/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9004 - val_loss: 0.7883 - val_accuracy: 0.9021
Epoch 81/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7881 - val_accuracy: 0.9028
Epoch 82/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7862 - val_accuracy: 0.9034
Epoch 83/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9004 - val_loss: 0.7872 - val_accuracy: 0.9038
Epoch 84/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7878 - val_accuracy: 0.9031
Epoch 85/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7872 - val_accuracy: 0.9036
Epoch 86/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9006 - val_loss: 0.7878 - val_accuracy: 0.9038
Epoch 87/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9004 - val_loss: 0.7870 - val_accuracy: 0.9033
Epoch 88/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7865 - val_accuracy: 0.9038
Epoch 89/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7871 - val_accuracy: 0.9031
Epoch 90/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7875 - val_accuracy: 0.9039
Epoch 91/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9003 - val_loss: 0.7871 - val_accuracy: 0.9026
Epoch 92/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9005 - val_loss: 0.7882 - val_accuracy: 0.9033
Epoch 93/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9003 - val_loss: 0.7872 - val_accuracy: 0.9027
Epoch 94/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7878 - val_accuracy: 0.9036
Epoch 95/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9029
Epoch 96/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7873 - val_accuracy: 0.9030
Epoch 97/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7881 - val_accuracy: 0.9033
Epoch 98/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7863 - val_accuracy: 0.9035
Epoch 99/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9036
Epoch 100/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7872 - val_accuracy: 0.9036
Epoch 101/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9034
Epoch 102/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9037
Epoch 103/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9036
Epoch 104/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9030
Epoch 105/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7870 - val_accuracy: 0.9035
Epoch 106/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7876 - val_accuracy: 0.9031
Epoch 107/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9004 - val_loss: 0.7874 - val_accuracy: 0.9035
Epoch 108/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9005 - val_loss: 0.7880 - val_accuracy: 0.9029
Epoch 109/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9004 - val_loss: 0.7869 - val_accuracy: 0.9040
Epoch 110/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7870 - val_accuracy: 0.9028
Epoch 111/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9010 - val_loss: 0.7866 - val_accuracy: 0.9034
Epoch 112/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7875 - val_accuracy: 0.9032
Epoch 113/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7871 - val_accuracy: 0.9037
Epoch 114/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9031
Epoch 115/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7877 - val_accuracy: 0.9031
Epoch 116/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9008 - val_loss: 0.7865 - val_accuracy: 0.9038
Epoch 117/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7869 - val_accuracy: 0.9038
Epoch 118/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7870 - val_accuracy: 0.9022
Epoch 119/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9030
Epoch 120/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7874 - val_accuracy: 0.9034
Epoch 121/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7877 - val_accuracy: 0.9033
Epoch 122/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7868 - val_accuracy: 0.9036
Epoch 123/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7877 - val_accuracy: 0.9034
Epoch 124/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7879 - val_accuracy: 0.9027
Epoch 125/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9004 - val_loss: 0.7874 - val_accuracy: 0.9032
Epoch 126/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7875 - val_accuracy: 0.9033
Epoch 127/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7865 - val_accuracy: 0.9037
Epoch 128/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7878 - val_accuracy: 0.9032
Epoch 129/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9036
Epoch 130/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7868 - val_accuracy: 0.9034
Epoch 131/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9034
Epoch 132/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7880 - val_accuracy: 0.9030
Epoch 133/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7877 - val_accuracy: 0.9036
Epoch 134/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9037
Epoch 135/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7873 - val_accuracy: 0.9036
Epoch 136/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9031
Epoch 137/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7869 - val_accuracy: 0.9039
Epoch 138/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7867 - val_accuracy: 0.9029
Epoch 139/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9038
Epoch 140/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7876 - val_accuracy: 0.9034
Epoch 141/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9037
Epoch 142/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9005 - val_loss: 0.7869 - val_accuracy: 0.9033
Epoch 143/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9006 - val_loss: 0.7867 - val_accuracy: 0.9038
Epoch 144/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9031
Epoch 145/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9039
Epoch 146/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9005 - val_loss: 0.7880 - val_accuracy: 0.9032
Epoch 147/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9006 - val_loss: 0.7865 - val_accuracy: 0.9041
Epoch 148/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9005 - val_loss: 0.7876 - val_accuracy: 0.9033
Epoch 149/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7879 - val_accuracy: 0.9032
Epoch 150/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7871 - val_accuracy: 0.9041
Epoch 151/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9036
Epoch 152/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9039
Epoch 153/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7877 - val_accuracy: 0.9039
Epoch 154/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9002 - val_loss: 0.7874 - val_accuracy: 0.9038
Epoch 155/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9028
Epoch 156/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7879 - val_accuracy: 0.9035
Epoch 157/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7878 - val_accuracy: 0.9033
Epoch 158/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7876 - val_accuracy: 0.9032
Epoch 159/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7874 - val_accuracy: 0.9038
Epoch 160/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7867 - val_accuracy: 0.9041
Epoch 161/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7873 - val_accuracy: 0.9038
Epoch 162/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9032
Epoch 163/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7876 - val_accuracy: 0.9034
Epoch 164/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9027
Epoch 165/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9007 - val_loss: 0.7877 - val_accuracy: 0.9034
Epoch 166/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9038
Epoch 167/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9033
Epoch 168/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7876 - val_accuracy: 0.9030
Epoch 169/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9004 - val_loss: 0.7860 - val_accuracy: 0.9031
Epoch 170/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9040
Epoch 171/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7870 - val_accuracy: 0.9032
Epoch 172/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9005 - val_loss: 0.7870 - val_accuracy: 0.9034
Epoch 173/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9036
Epoch 174/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9010 - val_loss: 0.7861 - val_accuracy: 0.9041
Epoch 175/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9005 - val_loss: 0.7866 - val_accuracy: 0.9039
Epoch 176/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9029
Epoch 177/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9035
Epoch 178/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7872 - val_accuracy: 0.9036
Epoch 179/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7870 - val_accuracy: 0.9029
Epoch 180/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7876 - val_accuracy: 0.9031
Epoch 181/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9009 - val_loss: 0.7870 - val_accuracy: 0.9041
Epoch 182/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9036
Epoch 183/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9006 - val_loss: 0.7872 - val_accuracy: 0.9033
Epoch 184/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9009 - val_loss: 0.7874 - val_accuracy: 0.9043
Epoch 185/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7871 - val_accuracy: 0.9032
Epoch 186/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7877 - val_accuracy: 0.9035
Epoch 187/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9039
Epoch 188/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7871 - val_accuracy: 0.9040
Epoch 189/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9033
Epoch 190/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7876 - val_accuracy: 0.9032
Epoch 191/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7881 - val_accuracy: 0.9026
Epoch 192/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7881 - val_accuracy: 0.9031
Epoch 193/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9004 - val_loss: 0.7867 - val_accuracy: 0.9043
Epoch 194/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9005 - val_loss: 0.7870 - val_accuracy: 0.9037
Epoch 195/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9036
Epoch 196/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9040
Epoch 197/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7871 - val_accuracy: 0.9036
Epoch 198/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7873 - val_accuracy: 0.9033
Epoch 199/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9007 - val_loss: 0.7876 - val_accuracy: 0.9036
Epoch 200/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7867 - val_accuracy: 0.9037
Epoch 1/200
235/235 [==============================] - 2s 9ms/step - loss: 0.4777 - accuracy: 0.8683 - val_loss: 0.2514 - val_accuracy: 0.9274
Epoch 2/200
235/235 [==============================] - 2s 8ms/step - loss: 0.2277 - accuracy: 0.9330 - val_loss: 0.1910 - val_accuracy: 0.9436
Epoch 3/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1733 - accuracy: 0.9496 - val_loss: 0.1576 - val_accuracy: 0.9535
Epoch 4/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1389 - accuracy: 0.9594 - val_loss: 0.1368 - val_accuracy: 0.9580
Epoch 5/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1145 - accuracy: 0.9663 - val_loss: 0.1230 - val_accuracy: 0.9624
Epoch 6/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0958 - accuracy: 0.9714 - val_loss: 0.1150 - val_accuracy: 0.9638
Epoch 7/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0813 - accuracy: 0.9762 - val_loss: 0.1092 - val_accuracy: 0.9647
Epoch 8/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0692 - accuracy: 0.9801 - val_loss: 0.1065 - val_accuracy: 0.9657
Epoch 9/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0595 - accuracy: 0.9829 - val_loss: 0.1047 - val_accuracy: 0.9665
Epoch 10/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0511 - accuracy: 0.9857 - val_loss: 0.1029 - val_accuracy: 0.9678
Epoch 11/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0443 - accuracy: 0.9880 - val_loss: 0.1032 - val_accuracy: 0.9675
Epoch 12/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0383 - accuracy: 0.9900 - val_loss: 0.1015 - val_accuracy: 0.9684
Epoch 13/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0331 - accuracy: 0.9918 - val_loss: 0.1025 - val_accuracy: 0.9690
Epoch 14/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0286 - accuracy: 0.9933 - val_loss: 0.1036 - val_accuracy: 0.9692
Epoch 15/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0248 - accuracy: 0.9945 - val_loss: 0.1045 - val_accuracy: 0.9703
Epoch 16/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0214 - accuracy: 0.9954 - val_loss: 0.1045 - val_accuracy: 0.9715
Epoch 17/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0183 - accuracy: 0.9963 - val_loss: 0.1058 - val_accuracy: 0.9713
Epoch 18/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.1051 - val_accuracy: 0.9724
Epoch 19/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0141 - accuracy: 0.9974 - val_loss: 0.1099 - val_accuracy: 0.9721
Epoch 20/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0123 - accuracy: 0.9979 - val_loss: 0.1116 - val_accuracy: 0.9716
Epoch 21/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0111 - accuracy: 0.9982 - val_loss: 0.1156 - val_accuracy: 0.9718
Epoch 22/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0103 - accuracy: 0.9980 - val_loss: 0.1214 - val_accuracy: 0.9709
Epoch 23/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0100 - accuracy: 0.9979 - val_loss: 0.1223 - val_accuracy: 0.9701
Epoch 24/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0098 - accuracy: 0.9977 - val_loss: 0.1239 - val_accuracy: 0.9715
Epoch 25/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0099 - accuracy: 0.9972 - val_loss: 0.1301 - val_accuracy: 0.9700
Epoch 26/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0079 - accuracy: 0.9980 - val_loss: 0.1345 - val_accuracy: 0.9701
Epoch 27/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0062 - accuracy: 0.9986 - val_loss: 0.1351 - val_accuracy: 0.9710
Epoch 28/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0053 - accuracy: 0.9991 - val_loss: 0.1498 - val_accuracy: 0.9690
Epoch 29/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0051 - accuracy: 0.9991 - val_loss: 0.1463 - val_accuracy: 0.9704
Epoch 30/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0044 - accuracy: 0.9991 - val_loss: 0.1408 - val_accuracy: 0.9695
Epoch 31/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.1352 - val_accuracy: 0.9713
Epoch 32/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 0.9996 - val_loss: 0.1360 - val_accuracy: 0.9730
Epoch 33/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1378 - val_accuracy: 0.9717
Epoch 34/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 0.9999 - val_loss: 0.1401 - val_accuracy: 0.9716
Epoch 35/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1450 - val_accuracy: 0.9717
Epoch 36/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1418 - val_accuracy: 0.9727
Epoch 37/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9708
Epoch 38/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1573 - val_accuracy: 0.9712
Epoch 39/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0209 - accuracy: 0.9934 - val_loss: 0.1639 - val_accuracy: 0.9692
Epoch 40/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0090 - accuracy: 0.9972 - val_loss: 0.1570 - val_accuracy: 0.9713
Epoch 41/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 0.9995 - val_loss: 0.1566 - val_accuracy: 0.9733
Epoch 42/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1427 - val_accuracy: 0.9754
Epoch 43/200
235/235 [==============================] - 2s 9ms/step - loss: 6.9572e-04 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9750
Epoch 44/200
235/235 [==============================] - 2s 9ms/step - loss: 5.0109e-04 - accuracy: 1.0000 - val_loss: 0.1439 - val_accuracy: 0.9746
Epoch 45/200
235/235 [==============================] - 2s 8ms/step - loss: 4.0017e-04 - accuracy: 1.0000 - val_loss: 0.1444 - val_accuracy: 0.9747
Epoch 46/200
235/235 [==============================] - 2s 8ms/step - loss: 3.4035e-04 - accuracy: 1.0000 - val_loss: 0.1450 - val_accuracy: 0.9746
Epoch 47/200
235/235 [==============================] - 2s 9ms/step - loss: 3.0142e-04 - accuracy: 1.0000 - val_loss: 0.1458 - val_accuracy: 0.9746
Epoch 48/200
235/235 [==============================] - 2s 8ms/step - loss: 2.7058e-04 - accuracy: 1.0000 - val_loss: 0.1467 - val_accuracy: 0.9749
Epoch 49/200
235/235 [==============================] - 2s 9ms/step - loss: 2.4405e-04 - accuracy: 1.0000 - val_loss: 0.1477 - val_accuracy: 0.9748
Epoch 50/200
235/235 [==============================] - 2s 9ms/step - loss: 2.2169e-04 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9748
Epoch 51/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0148e-04 - accuracy: 1.0000 - val_loss: 0.1498 - val_accuracy: 0.9747
Epoch 52/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8346e-04 - accuracy: 1.0000 - val_loss: 0.1510 - val_accuracy: 0.9748
Epoch 53/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6685e-04 - accuracy: 1.0000 - val_loss: 0.1521 - val_accuracy: 0.9748
Epoch 54/200
235/235 [==============================] - 2s 8ms/step - loss: 1.5204e-04 - accuracy: 1.0000 - val_loss: 0.1534 - val_accuracy: 0.9748
Epoch 55/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3855e-04 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9749
Epoch 56/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2598e-04 - accuracy: 1.0000 - val_loss: 0.1561 - val_accuracy: 0.9748
Epoch 57/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1443e-04 - accuracy: 1.0000 - val_loss: 0.1576 - val_accuracy: 0.9748
Epoch 58/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0383e-04 - accuracy: 1.0000 - val_loss: 0.1591 - val_accuracy: 0.9748
Epoch 59/200
235/235 [==============================] - 2s 8ms/step - loss: 9.4306e-05 - accuracy: 1.0000 - val_loss: 0.1606 - val_accuracy: 0.9746
Epoch 60/200
235/235 [==============================] - 2s 8ms/step - loss: 8.5305e-05 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9746
Epoch 61/200
235/235 [==============================] - 2s 8ms/step - loss: 7.7055e-05 - accuracy: 1.0000 - val_loss: 0.1636 - val_accuracy: 0.9746
Epoch 62/200
235/235 [==============================] - 2s 8ms/step - loss: 6.9650e-05 - accuracy: 1.0000 - val_loss: 0.1653 - val_accuracy: 0.9745
Epoch 63/200
235/235 [==============================] - 2s 8ms/step - loss: 6.2657e-05 - accuracy: 1.0000 - val_loss: 0.1669 - val_accuracy: 0.9744
Epoch 64/200
235/235 [==============================] - 2s 8ms/step - loss: 5.6425e-05 - accuracy: 1.0000 - val_loss: 0.1687 - val_accuracy: 0.9743
Epoch 65/200
235/235 [==============================] - 2s 8ms/step - loss: 5.0673e-05 - accuracy: 1.0000 - val_loss: 0.1704 - val_accuracy: 0.9745
Epoch 66/200
235/235 [==============================] - 2s 9ms/step - loss: 4.5437e-05 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9743
Epoch 67/200
235/235 [==============================] - 2s 8ms/step - loss: 4.0690e-05 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9744
Epoch 68/200
235/235 [==============================] - 2s 8ms/step - loss: 3.6409e-05 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9741
Epoch 69/200
235/235 [==============================] - 2s 9ms/step - loss: 3.2474e-05 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9741
Epoch 70/200
235/235 [==============================] - 2s 8ms/step - loss: 2.8983e-05 - accuracy: 1.0000 - val_loss: 0.1795 - val_accuracy: 0.9740
Epoch 71/200
235/235 [==============================] - 2s 8ms/step - loss: 2.5818e-05 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9740
Epoch 72/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2957e-05 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9740
Epoch 73/200
235/235 [==============================] - 2s 9ms/step - loss: 2.0407e-05 - accuracy: 1.0000 - val_loss: 0.1851 - val_accuracy: 0.9740
Epoch 74/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8170e-05 - accuracy: 1.0000 - val_loss: 0.1871 - val_accuracy: 0.9740
Epoch 75/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6094e-05 - accuracy: 1.0000 - val_loss: 0.1890 - val_accuracy: 0.9740
Epoch 76/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4279e-05 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9740
Epoch 77/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2640e-05 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9741
Epoch 78/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1203e-05 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9741
Epoch 79/200
235/235 [==============================] - 2s 8ms/step - loss: 9.9136e-06 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9742
Epoch 80/200
235/235 [==============================] - 2s 8ms/step - loss: 8.7656e-06 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9741
Epoch 81/200
235/235 [==============================] - 2s 8ms/step - loss: 7.7456e-06 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9739
Epoch 82/200
235/235 [==============================] - 2s 8ms/step - loss: 6.8530e-06 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9739
Epoch 83/200
235/235 [==============================] - 2s 9ms/step - loss: 6.0533e-06 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9739
Epoch 84/200
235/235 [==============================] - 2s 8ms/step - loss: 5.3416e-06 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9741
Epoch 85/200
235/235 [==============================] - 2s 9ms/step - loss: 4.7242e-06 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.9740
Epoch 86/200
235/235 [==============================] - 2s 8ms/step - loss: 4.1734e-06 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9739
Epoch 87/200
235/235 [==============================] - 2s 8ms/step - loss: 3.6822e-06 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9739
Epoch 88/200
235/235 [==============================] - 2s 8ms/step - loss: 3.2493e-06 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9737
Epoch 89/200
235/235 [==============================] - 2s 8ms/step - loss: 2.8682e-06 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9738
Epoch 90/200
235/235 [==============================] - 2s 8ms/step - loss: 2.5365e-06 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9738
Epoch 91/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2367e-06 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9739
Epoch 92/200
235/235 [==============================] - 2s 8ms/step - loss: 1.9777e-06 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9738
Epoch 93/200
235/235 [==============================] - 2s 8ms/step - loss: 1.7475e-06 - accuracy: 1.0000 - val_loss: 0.2241 - val_accuracy: 0.9736
Epoch 94/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5433e-06 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9737
Epoch 95/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3678e-06 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9739
Epoch 96/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2074e-06 - accuracy: 1.0000 - val_loss: 0.2300 - val_accuracy: 0.9735
Epoch 97/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0708e-06 - accuracy: 1.0000 - val_loss: 0.2319 - val_accuracy: 0.9739
Epoch 98/200
235/235 [==============================] - 2s 8ms/step - loss: 9.4819e-07 - accuracy: 1.0000 - val_loss: 0.2339 - val_accuracy: 0.9741
Epoch 99/200
235/235 [==============================] - 2s 9ms/step - loss: 8.4005e-07 - accuracy: 1.0000 - val_loss: 0.2356 - val_accuracy: 0.9739
Epoch 100/200
235/235 [==============================] - 2s 8ms/step - loss: 7.4570e-07 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9741
Epoch 101/200
235/235 [==============================] - 2s 8ms/step - loss: 6.6061e-07 - accuracy: 1.0000 - val_loss: 0.2394 - val_accuracy: 0.9740
Epoch 102/200
235/235 [==============================] - 2s 8ms/step - loss: 5.8695e-07 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9739
Epoch 103/200
235/235 [==============================] - 2s 8ms/step - loss: 5.2292e-07 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9739
Epoch 104/200
235/235 [==============================] - 2s 8ms/step - loss: 4.6375e-07 - accuracy: 1.0000 - val_loss: 0.2448 - val_accuracy: 0.9739
Epoch 105/200
235/235 [==============================] - 2s 8ms/step - loss: 4.1361e-07 - accuracy: 1.0000 - val_loss: 0.2466 - val_accuracy: 0.9739
Epoch 106/200
235/235 [==============================] - 2s 10ms/step - loss: 3.6890e-07 - accuracy: 1.0000 - val_loss: 0.2483 - val_accuracy: 0.9739
Epoch 107/200
235/235 [==============================] - 2s 9ms/step - loss: 3.2952e-07 - accuracy: 1.0000 - val_loss: 0.2500 - val_accuracy: 0.9739
Epoch 108/200
235/235 [==============================] - 2s 9ms/step - loss: 2.9483e-07 - accuracy: 1.0000 - val_loss: 0.2519 - val_accuracy: 0.9740
Epoch 109/200
235/235 [==============================] - 2s 9ms/step - loss: 2.6338e-07 - accuracy: 1.0000 - val_loss: 0.2536 - val_accuracy: 0.9740
Epoch 110/200
235/235 [==============================] - 2s 8ms/step - loss: 2.3657e-07 - accuracy: 1.0000 - val_loss: 0.2553 - val_accuracy: 0.9739
Epoch 111/200
235/235 [==============================] - 2s 9ms/step - loss: 2.1258e-07 - accuracy: 1.0000 - val_loss: 0.2568 - val_accuracy: 0.9738
Epoch 112/200
235/235 [==============================] - 2s 8ms/step - loss: 1.9068e-07 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9739
Epoch 113/200
235/235 [==============================] - 2s 8ms/step - loss: 1.7193e-07 - accuracy: 1.0000 - val_loss: 0.2601 - val_accuracy: 0.9738
Epoch 114/200
235/235 [==============================] - 2s 10ms/step - loss: 1.5517e-07 - accuracy: 1.0000 - val_loss: 0.2616 - val_accuracy: 0.9739
Epoch 115/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4024e-07 - accuracy: 1.0000 - val_loss: 0.2631 - val_accuracy: 0.9739
Epoch 116/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2701e-07 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9737
Epoch 117/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1551e-07 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9738
Epoch 118/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0498e-07 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9739
Epoch 119/200
235/235 [==============================] - 2s 8ms/step - loss: 9.5806e-08 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9739
Epoch 120/200
235/235 [==============================] - 2s 9ms/step - loss: 8.7490e-08 - accuracy: 1.0000 - val_loss: 0.2701 - val_accuracy: 0.9740
Epoch 121/200
235/235 [==============================] - 2s 8ms/step - loss: 8.0045e-08 - accuracy: 1.0000 - val_loss: 0.2714 - val_accuracy: 0.9740
Epoch 122/200
235/235 [==============================] - 2s 8ms/step - loss: 7.3725e-08 - accuracy: 1.0000 - val_loss: 0.2726 - val_accuracy: 0.9740
Epoch 123/200
235/235 [==============================] - 2s 8ms/step - loss: 6.7717e-08 - accuracy: 1.0000 - val_loss: 0.2738 - val_accuracy: 0.9738
Epoch 124/200
235/235 [==============================] - 2s 8ms/step - loss: 6.2557e-08 - accuracy: 1.0000 - val_loss: 0.2750 - val_accuracy: 0.9739
Epoch 125/200
235/235 [==============================] - 2s 8ms/step - loss: 5.7844e-08 - accuracy: 1.0000 - val_loss: 0.2760 - val_accuracy: 0.9738
Epoch 126/200
235/235 [==============================] - 2s 8ms/step - loss: 5.3420e-08 - accuracy: 1.0000 - val_loss: 0.2772 - val_accuracy: 0.9738
Epoch 127/200
235/235 [==============================] - 2s 8ms/step - loss: 4.9692e-08 - accuracy: 1.0000 - val_loss: 0.2782 - val_accuracy: 0.9737
Epoch 128/200
235/235 [==============================] - 2s 9ms/step - loss: 4.6269e-08 - accuracy: 1.0000 - val_loss: 0.2793 - val_accuracy: 0.9737
Epoch 129/200
235/235 [==============================] - 2s 9ms/step - loss: 4.3144e-08 - accuracy: 1.0000 - val_loss: 0.2802 - val_accuracy: 0.9737
Epoch 130/200
235/235 [==============================] - 2s 8ms/step - loss: 4.0444e-08 - accuracy: 1.0000 - val_loss: 0.2811 - val_accuracy: 0.9736
Epoch 131/200
235/235 [==============================] - 2s 9ms/step - loss: 3.7827e-08 - accuracy: 1.0000 - val_loss: 0.2821 - val_accuracy: 0.9736
Epoch 132/200
235/235 [==============================] - 2s 8ms/step - loss: 3.5518e-08 - accuracy: 1.0000 - val_loss: 0.2828 - val_accuracy: 0.9737
Epoch 133/200
235/235 [==============================] - 2s 8ms/step - loss: 3.3410e-08 - accuracy: 1.0000 - val_loss: 0.2837 - val_accuracy: 0.9737
Epoch 134/200
235/235 [==============================] - 2s 8ms/step - loss: 3.1543e-08 - accuracy: 1.0000 - val_loss: 0.2843 - val_accuracy: 0.9738
Epoch 135/200
235/235 [==============================] - 2s 8ms/step - loss: 2.9808e-08 - accuracy: 1.0000 - val_loss: 0.2852 - val_accuracy: 0.9738
Epoch 136/200
235/235 [==============================] - 2s 8ms/step - loss: 2.8175e-08 - accuracy: 1.0000 - val_loss: 0.2859 - val_accuracy: 0.9738
Epoch 137/200
235/235 [==============================] - 2s 9ms/step - loss: 2.6733e-08 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9738
Epoch 138/200
235/235 [==============================] - 2s 8ms/step - loss: 2.5499e-08 - accuracy: 1.0000 - val_loss: 0.2873 - val_accuracy: 0.9738
Epoch 139/200
235/235 [==============================] - 2s 9ms/step - loss: 2.4267e-08 - accuracy: 1.0000 - val_loss: 0.2880 - val_accuracy: 0.9737
Epoch 140/200
235/235 [==============================] - 2s 9ms/step - loss: 2.3109e-08 - accuracy: 1.0000 - val_loss: 0.2886 - val_accuracy: 0.9736
Epoch 141/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2127e-08 - accuracy: 1.0000 - val_loss: 0.2892 - val_accuracy: 0.9737
Epoch 142/200
235/235 [==============================] - 2s 8ms/step - loss: 2.1162e-08 - accuracy: 1.0000 - val_loss: 0.2897 - val_accuracy: 0.9736
Epoch 143/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0281e-08 - accuracy: 1.0000 - val_loss: 0.2903 - val_accuracy: 0.9738
Epoch 144/200
235/235 [==============================] - 2s 9ms/step - loss: 1.9501e-08 - accuracy: 1.0000 - val_loss: 0.2909 - val_accuracy: 0.9738
Epoch 145/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8654e-08 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9738
Epoch 146/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7899e-08 - accuracy: 1.0000 - val_loss: 0.2921 - val_accuracy: 0.9739
Epoch 147/200
235/235 [==============================] - 2s 8ms/step - loss: 1.7240e-08 - accuracy: 1.0000 - val_loss: 0.2926 - val_accuracy: 0.9738
Epoch 148/200
235/235 [==============================] - 2s 9ms/step - loss: 1.6620e-08 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9741
Epoch 149/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6069e-08 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9740
Epoch 150/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5501e-08 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9738
Epoch 151/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5046e-08 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9739
Epoch 152/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4522e-08 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737
Epoch 153/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4146e-08 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9737
Epoch 154/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3707e-08 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9737
Epoch 155/200
235/235 [==============================] - 3s 11ms/step - loss: 1.3242e-08 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9736
Epoch 156/200
235/235 [==============================] - 3s 11ms/step - loss: 1.2924e-08 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9736
Epoch 157/200
235/235 [==============================] - 2s 10ms/step - loss: 1.2523e-08 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9736
Epoch 158/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2173e-08 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9736
Epoch 159/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1752e-08 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9736
Epoch 160/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1476e-08 - accuracy: 1.0000 - val_loss: 0.2986 - val_accuracy: 0.9737
Epoch 161/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1212e-08 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9737
Epoch 162/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0908e-08 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9737
Epoch 163/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0697e-08 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9737
Epoch 164/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0391e-08 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9737
Epoch 165/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0155e-08 - accuracy: 1.0000 - val_loss: 0.3005 - val_accuracy: 0.9739
Epoch 166/200
235/235 [==============================] - 2s 8ms/step - loss: 9.9321e-09 - accuracy: 1.0000 - val_loss: 0.3008 - val_accuracy: 0.9739
Epoch 167/200
235/235 [==============================] - 2s 8ms/step - loss: 9.6997e-09 - accuracy: 1.0000 - val_loss: 0.3012 - val_accuracy: 0.9739
Epoch 168/200
235/235 [==============================] - 2s 8ms/step - loss: 9.4533e-09 - accuracy: 1.0000 - val_loss: 0.3014 - val_accuracy: 0.9739
Epoch 169/200
235/235 [==============================] - 2s 8ms/step - loss: 9.2844e-09 - accuracy: 1.0000 - val_loss: 0.3016 - val_accuracy: 0.9739
Epoch 170/200
235/235 [==============================] - 2s 8ms/step - loss: 9.0440e-09 - accuracy: 1.0000 - val_loss: 0.3019 - val_accuracy: 0.9740
Epoch 171/200
235/235 [==============================] - 2s 8ms/step - loss: 8.8374e-09 - accuracy: 1.0000 - val_loss: 0.3021 - val_accuracy: 0.9740
Epoch 172/200
235/235 [==============================] - 2s 8ms/step - loss: 8.6705e-09 - accuracy: 1.0000 - val_loss: 0.3024 - val_accuracy: 0.9739
Epoch 173/200
235/235 [==============================] - 2s 8ms/step - loss: 8.4718e-09 - accuracy: 1.0000 - val_loss: 0.3027 - val_accuracy: 0.9740
Epoch 174/200
235/235 [==============================] - 2s 8ms/step - loss: 8.2990e-09 - accuracy: 1.0000 - val_loss: 0.3028 - val_accuracy: 0.9740
Epoch 175/200
235/235 [==============================] - 2s 8ms/step - loss: 8.1321e-09 - accuracy: 1.0000 - val_loss: 0.3031 - val_accuracy: 0.9741
Epoch 176/200
235/235 [==============================] - 2s 8ms/step - loss: 8.0129e-09 - accuracy: 1.0000 - val_loss: 0.3033 - val_accuracy: 0.9742
Epoch 177/200
235/235 [==============================] - 2s 8ms/step - loss: 7.8003e-09 - accuracy: 1.0000 - val_loss: 0.3035 - val_accuracy: 0.9742
Epoch 178/200
235/235 [==============================] - 2s 8ms/step - loss: 7.6711e-09 - accuracy: 1.0000 - val_loss: 0.3038 - val_accuracy: 0.9742
Epoch 179/200
235/235 [==============================] - 2s 8ms/step - loss: 7.4883e-09 - accuracy: 1.0000 - val_loss: 0.3041 - val_accuracy: 0.9740
Epoch 180/200
235/235 [==============================] - 2s 8ms/step - loss: 7.3910e-09 - accuracy: 1.0000 - val_loss: 0.3043 - val_accuracy: 0.9742
Epoch 181/200
235/235 [==============================] - 2s 8ms/step - loss: 7.2479e-09 - accuracy: 1.0000 - val_loss: 0.3045 - val_accuracy: 0.9740
Epoch 182/200
235/235 [==============================] - 2s 8ms/step - loss: 7.1247e-09 - accuracy: 1.0000 - val_loss: 0.3046 - val_accuracy: 0.9740
Epoch 183/200
235/235 [==============================] - 2s 8ms/step - loss: 7.0333e-09 - accuracy: 1.0000 - val_loss: 0.3049 - val_accuracy: 0.9741
Epoch 184/200
235/235 [==============================] - 2s 8ms/step - loss: 6.8386e-09 - accuracy: 1.0000 - val_loss: 0.3050 - val_accuracy: 0.9741
Epoch 185/200
235/235 [==============================] - 2s 8ms/step - loss: 6.7810e-09 - accuracy: 1.0000 - val_loss: 0.3051 - val_accuracy: 0.9740
Epoch 186/200
235/235 [==============================] - 2s 8ms/step - loss: 6.6737e-09 - accuracy: 1.0000 - val_loss: 0.3054 - val_accuracy: 0.9740
Epoch 187/200
235/235 [==============================] - 2s 8ms/step - loss: 6.5426e-09 - accuracy: 1.0000 - val_loss: 0.3055 - val_accuracy: 0.9739
Epoch 188/200
235/235 [==============================] - 2s 8ms/step - loss: 6.4393e-09 - accuracy: 1.0000 - val_loss: 0.3055 - val_accuracy: 0.9739
Epoch 189/200
235/235 [==============================] - 2s 8ms/step - loss: 6.3181e-09 - accuracy: 1.0000 - val_loss: 0.3057 - val_accuracy: 0.9739
Epoch 190/200
235/235 [==============================] - 2s 8ms/step - loss: 6.1909e-09 - accuracy: 1.0000 - val_loss: 0.3059 - val_accuracy: 0.9739
Epoch 191/200
235/235 [==============================] - 2s 8ms/step - loss: 6.0558e-09 - accuracy: 1.0000 - val_loss: 0.3059 - val_accuracy: 0.9739
Epoch 192/200
235/235 [==============================] - 2s 8ms/step - loss: 5.9684e-09 - accuracy: 1.0000 - val_loss: 0.3060 - val_accuracy: 0.9739
Epoch 193/200
235/235 [==============================] - 2s 8ms/step - loss: 5.8591e-09 - accuracy: 1.0000 - val_loss: 0.3062 - val_accuracy: 0.9740
Epoch 194/200
235/235 [==============================] - 2s 8ms/step - loss: 5.8035e-09 - accuracy: 1.0000 - val_loss: 0.3063 - val_accuracy: 0.9739
Epoch 195/200
235/235 [==============================] - 2s 8ms/step - loss: 5.6644e-09 - accuracy: 1.0000 - val_loss: 0.3064 - val_accuracy: 0.9739
Epoch 196/200
235/235 [==============================] - 2s 8ms/step - loss: 5.6227e-09 - accuracy: 1.0000 - val_loss: 0.3065 - val_accuracy: 0.9739
Epoch 197/200
235/235 [==============================] - 2s 8ms/step - loss: 5.5253e-09 - accuracy: 1.0000 - val_loss: 0.3066 - val_accuracy: 0.9740
Epoch 198/200
235/235 [==============================] - 2s 8ms/step - loss: 5.4340e-09 - accuracy: 1.0000 - val_loss: 0.3068 - val_accuracy: 0.9738
Epoch 199/200
235/235 [==============================] - 2s 8ms/step - loss: 5.3167e-09 - accuracy: 1.0000 - val_loss: 0.3068 - val_accuracy: 0.9737
Epoch 200/200
235/235 [==============================] - 2s 8ms/step - loss: 5.2571e-09 - accuracy: 1.0000 - val_loss: 0.3070 - val_accuracy: 0.9738
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.03707949258387089
Thresholhold -0.04958835244178772
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.06157502718269825
Thresholhold -0.06629646569490433
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.11812010034918785
Thresholhold 0.22601225972175598
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. 0. 1. 0. 1. 0. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 0. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.]
 [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [1. 1. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 0. 1. 0. 0. 1. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [1. 0. 0. 0. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 1. 1. 1. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 1. 0. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [0. 0. 1. 1. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 1. 0. 1. 0. 1. 1.]
 [0. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 0. 1. 0.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 0. 1. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 0. 0. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
  6/235 [..............................] - ETA: 2s - loss: 7.5542 - accuracy: 0.4401     WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0101s vs `on_train_batch_begin` time: 11.3151s). Check your callbacks.
235/235 [==============================] - 72s 12ms/step - loss: 2.1643 - accuracy: 0.9241 - val_loss: 1.5254 - val_accuracy: 0.8979
[ 1.1987437e-07  3.7992351e-07 -1.9329187e-07 ... -1.0124538e-01
 -1.5895198e-01 -1.8158959e-01]
Sparsity at: 0.0018782870022539444
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4412 - accuracy: 0.9591 - val_loss: 0.4845 - val_accuracy: 0.9519
[-2.1197655e-12 -1.3815716e-12 -3.7385017e-12 ... -9.3455590e-02
 -1.2025412e-01 -1.1961582e-01]
Sparsity at: 0.0018782870022539444
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 0.3170 - accuracy: 0.9633 - val_loss: 0.3509 - val_accuracy: 0.9461
[-7.2531770e-18  7.6473021e-18 -5.5772425e-19 ... -8.3159439e-02
 -9.5205635e-02 -8.6928986e-02]
Sparsity at: 0.0018782870022539444
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2865 - accuracy: 0.9651 - val_loss: 0.3236 - val_accuracy: 0.9500
[ 4.1198545e-23  3.3774622e-23 -5.5448831e-23 ... -8.0384046e-02
 -7.4459568e-02 -6.9936521e-02]
Sparsity at: 0.0018782870022539444
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2700 - accuracy: 0.9661 - val_loss: 0.3228 - val_accuracy: 0.9463
[ 2.0431059e-28 -1.4075645e-28  3.9012263e-28 ... -7.3833123e-02
 -6.2158316e-02 -6.0852002e-02]
Sparsity at: 0.0018782870022539444
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2538 - accuracy: 0.9675 - val_loss: 0.2945 - val_accuracy: 0.9534
[-9.8549911e-34 -4.8944090e-34  1.1907741e-33 ... -7.0309490e-02
 -5.6259133e-02 -5.3487781e-02]
Sparsity at: 0.0018782870022539444
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2450 - accuracy: 0.9688 - val_loss: 0.3052 - val_accuracy: 0.9465
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -6.2099099e-02
 -5.3408753e-02 -3.5924356e-02]
Sparsity at: 0.0018858001502629603
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2315 - accuracy: 0.9707 - val_loss: 0.2814 - val_accuracy: 0.9529
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -6.4758100e-02
 -4.8410501e-02 -3.5459254e-02]
Sparsity at: 0.0018858001502629603
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2295 - accuracy: 0.9704 - val_loss: 0.2787 - val_accuracy: 0.9500
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -6.2133372e-02
 -4.7365736e-02 -2.8271766e-02]
Sparsity at: 0.0018858001502629603
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2207 - accuracy: 0.9711 - val_loss: 0.2897 - val_accuracy: 0.9465
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.8439385e-02
 -4.0382292e-02 -2.7753411e-02]
Sparsity at: 0.0018858001502629603
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2193 - accuracy: 0.9710 - val_loss: 0.2570 - val_accuracy: 0.9572
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.8699984e-02
 -3.5502981e-02 -2.1907805e-02]
Sparsity at: 0.0018858001502629603
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2091 - accuracy: 0.9721 - val_loss: 0.2535 - val_accuracy: 0.9577
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.4798331e-02
 -3.5884604e-02 -1.6109383e-02]
Sparsity at: 0.001889556724267468
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2157 - accuracy: 0.9704 - val_loss: 0.2364 - val_accuracy: 0.9623
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.5758931e-02
 -3.0850254e-02 -1.5591446e-02]
Sparsity at: 0.001889556724267468
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2030 - accuracy: 0.9727 - val_loss: 0.2642 - val_accuracy: 0.9527
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.5337116e-02
 -2.1567367e-02 -1.5932798e-02]
Sparsity at: 0.001889556724267468
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2037 - accuracy: 0.9722 - val_loss: 0.2426 - val_accuracy: 0.9557
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.6176368e-02
 -2.9497460e-02 -1.4442829e-02]
Sparsity at: 0.001889556724267468
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2000 - accuracy: 0.9722 - val_loss: 0.2692 - val_accuracy: 0.9502
[-2.56036835e-34 -4.89440904e-34  2.81328974e-34 ... -4.88829091e-02
 -2.79149693e-02 -1.20111285e-02]
Sparsity at: 0.001889556724267468
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1956 - accuracy: 0.9728 - val_loss: 0.3358 - val_accuracy: 0.9243
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.4926699e-02
 -2.4626618e-02 -9.8563870e-03]
Sparsity at: 0.001893313298271976
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1968 - accuracy: 0.9716 - val_loss: 0.2332 - val_accuracy: 0.9595
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.1401673e-02
 -2.8528711e-02 -6.5178238e-03]
Sparsity at: 0.001893313298271976
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1901 - accuracy: 0.9733 - val_loss: 0.2428 - val_accuracy: 0.9563
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.2622562e-02
 -2.7970558e-02  4.9088029e-03]
Sparsity at: 0.0018970698722764838
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1889 - accuracy: 0.9740 - val_loss: 0.2417 - val_accuracy: 0.9556
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.0894061e-02
 -2.6123958e-02 -3.3460316e-04]
Sparsity at: 0.0018970698722764838
Epoch 21/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1880 - accuracy: 0.9734 - val_loss: 0.2934 - val_accuracy: 0.9397
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.2014655e-02
 -2.3893103e-02  3.4353866e-03]
Sparsity at: 0.0019008264462809918
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1858 - accuracy: 0.9732 - val_loss: 0.2359 - val_accuracy: 0.9565
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.8390683e-02
 -2.2767911e-02  3.7614130e-03]
Sparsity at: 0.0019008264462809918
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1815 - accuracy: 0.9746 - val_loss: 0.2112 - val_accuracy: 0.9630
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.1107842e-02
 -1.7501112e-02 -1.4017588e-04]
Sparsity at: 0.0019008264462809918
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1796 - accuracy: 0.9745 - val_loss: 0.2469 - val_accuracy: 0.9508
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.3388225e-02
 -2.5842663e-02  1.1775387e-03]
Sparsity at: 0.0019008264462809918
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1780 - accuracy: 0.9743 - val_loss: 0.2299 - val_accuracy: 0.9574
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.2246209e-02
 -2.5500111e-02 -2.2653832e-05]
Sparsity at: 0.0019008264462809918
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1770 - accuracy: 0.9746 - val_loss: 0.2652 - val_accuracy: 0.9464
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.9731627e-02
 -2.1007957e-02 -1.4751096e-03]
Sparsity at: 0.0019008264462809918
Epoch 27/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1770 - accuracy: 0.9745 - val_loss: 0.2025 - val_accuracy: 0.9663
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.5657299e-02
 -2.3970084e-02 -4.9492614e-03]
Sparsity at: 0.0019008264462809918
Epoch 28/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1768 - accuracy: 0.9741 - val_loss: 0.2299 - val_accuracy: 0.9570
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.4599617e-02
 -2.2288041e-02  4.8921010e-03]
Sparsity at: 0.0019008264462809918
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1787 - accuracy: 0.9735 - val_loss: 0.2743 - val_accuracy: 0.9430
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9804041e-02
 -2.1474227e-02 -4.6588755e-03]
Sparsity at: 0.0019008264462809918
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1722 - accuracy: 0.9750 - val_loss: 0.2227 - val_accuracy: 0.9588
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.1195694e-02
 -1.7941864e-02 -5.5273161e-03]
Sparsity at: 0.0019008264462809918
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1732 - accuracy: 0.9738 - val_loss: 0.2314 - val_accuracy: 0.9567
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.4053003e-02
 -2.2037793e-02 -7.7174329e-03]
Sparsity at: 0.0019008264462809918
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1727 - accuracy: 0.9746 - val_loss: 0.2136 - val_accuracy: 0.9629
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.0067699e-02
 -1.8462192e-02 -1.0878566e-02]
Sparsity at: 0.0019008264462809918
Epoch 33/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1686 - accuracy: 0.9756 - val_loss: 0.2214 - val_accuracy: 0.9597
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.1496612e-02
 -1.6327551e-02 -4.8108818e-03]
Sparsity at: 0.0019008264462809918
Epoch 34/500
235/235 [==============================] - 3s 12ms/step - loss: 0.1679 - accuracy: 0.9760 - val_loss: 0.2186 - val_accuracy: 0.9618
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9948173e-02
 -1.3802285e-02 -7.2569847e-03]
Sparsity at: 0.0019008264462809918
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1724 - accuracy: 0.9741 - val_loss: 0.2133 - val_accuracy: 0.9617
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8432308e-02
 -1.4483869e-02 -1.2326549e-02]
Sparsity at: 0.0019008264462809918
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1676 - accuracy: 0.9760 - val_loss: 0.2201 - val_accuracy: 0.9625
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.1252580e-02
 -1.1529931e-02 -4.5486768e-03]
Sparsity at: 0.0019045830202854997
Epoch 37/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1696 - accuracy: 0.9745 - val_loss: 0.2307 - val_accuracy: 0.9587
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.1104961e-02
 -1.0497442e-02 -9.5685100e-05]
Sparsity at: 0.0019045830202854997
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1675 - accuracy: 0.9750 - val_loss: 0.2200 - val_accuracy: 0.9593
[-2.56036835e-34 -4.89440904e-34  2.81328974e-34 ... -3.56575474e-02
 -1.40698245e-02  4.39966936e-03]
Sparsity at: 0.0019045830202854997
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1707 - accuracy: 0.9748 - val_loss: 0.2483 - val_accuracy: 0.9520
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3602506e-02
 -1.0364095e-02 -6.0723494e-03]
Sparsity at: 0.0019045830202854997
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1682 - accuracy: 0.9754 - val_loss: 0.2481 - val_accuracy: 0.9529
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.0430974e-02
 -1.4037469e-02 -7.6746955e-03]
Sparsity at: 0.0019045830202854997
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1641 - accuracy: 0.9758 - val_loss: 0.2461 - val_accuracy: 0.9501
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2790545e-02
 -1.1138388e-02 -5.5829273e-03]
Sparsity at: 0.0019045830202854997
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1626 - accuracy: 0.9765 - val_loss: 0.1955 - val_accuracy: 0.9667
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3288438e-02
 -1.2605021e-02 -7.6571289e-03]
Sparsity at: 0.0019045830202854997
Epoch 43/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1628 - accuracy: 0.9758 - val_loss: 0.2641 - val_accuracy: 0.9435
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3022948e-02
 -2.0936467e-02  2.2003949e-03]
Sparsity at: 0.0019045830202854997
Epoch 44/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1653 - accuracy: 0.9746 - val_loss: 0.2269 - val_accuracy: 0.9591
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8879354e-02
 -1.7407566e-02  3.0901851e-03]
Sparsity at: 0.0019045830202854997
Epoch 45/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1690 - accuracy: 0.9745 - val_loss: 0.2355 - val_accuracy: 0.9560
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7023574e-02
 -2.0252693e-02 -2.3534123e-03]
Sparsity at: 0.0019045830202854997
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1614 - accuracy: 0.9767 - val_loss: 0.1940 - val_accuracy: 0.9679
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7376150e-02
 -2.0953940e-02  4.7816788e-03]
Sparsity at: 0.0019045830202854997
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1617 - accuracy: 0.9762 - val_loss: 0.2295 - val_accuracy: 0.9561
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.0621266e-02
 -2.4832113e-02 -4.2614494e-03]
Sparsity at: 0.0019045830202854997
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1615 - accuracy: 0.9760 - val_loss: 0.2019 - val_accuracy: 0.9649
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.1797068e-02
 -1.8224919e-02  5.6891359e-04]
Sparsity at: 0.0019045830202854997
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1628 - accuracy: 0.9762 - val_loss: 0.2312 - val_accuracy: 0.9562
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5983011e-02
 -1.4551427e-02 -8.1321830e-03]
Sparsity at: 0.0019045830202854997
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1627 - accuracy: 0.9760 - val_loss: 0.2069 - val_accuracy: 0.9636
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3961698e-02
 -1.6124545e-02  1.4248197e-03]
Sparsity at: 0.0019045830202854997
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 4.1276947719963544e-34
Thresholhold -2.560368352343766e-34
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 6.903632221386818e-05
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.018930538429348776
Thresholhold 0.04323918744921684
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.646
tf.Tensor(
[[1. 1. 1. 0. 1. 0. 1. 0. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 0. 1. 1.]
 [1. 0. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 0. 1. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.]
 [1. 1. 0. 1. 1. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 1. 1.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 1. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.]
 [1. 1. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 1. 0. 1. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 1. 1. 1. 1. 0. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 1. 0. 1. 1. 0.]
 [0. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 1. 0. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [1. 1. 0. 1. 1. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 152s 12ms/step - loss: 0.1639 - accuracy: 0.9750 - val_loss: 0.2418 - val_accuracy: 0.9521
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2285918e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1601 - accuracy: 0.9759 - val_loss: 0.2111 - val_accuracy: 0.9612
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5524461e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1607 - accuracy: 0.9760 - val_loss: 0.2381 - val_accuracy: 0.9534
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4108847e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1576 - accuracy: 0.9766 - val_loss: 0.2205 - val_accuracy: 0.9566
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3357915e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1599 - accuracy: 0.9760 - val_loss: 0.2495 - val_accuracy: 0.9507
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4207929e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1588 - accuracy: 0.9764 - val_loss: 0.2187 - val_accuracy: 0.9592
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8596791e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1643 - accuracy: 0.9749 - val_loss: 0.2103 - val_accuracy: 0.9628
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4256238e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1632 - accuracy: 0.9754 - val_loss: 0.2422 - val_accuracy: 0.9532
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5802297e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1600 - accuracy: 0.9757 - val_loss: 0.2728 - val_accuracy: 0.9426
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7660163e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1583 - accuracy: 0.9770 - val_loss: 0.2436 - val_accuracy: 0.9497
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3370934e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1565 - accuracy: 0.9765 - val_loss: 0.2169 - val_accuracy: 0.9595
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9823163e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1585 - accuracy: 0.9764 - val_loss: 0.2324 - val_accuracy: 0.9541
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8403306e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1588 - accuracy: 0.9761 - val_loss: 0.1999 - val_accuracy: 0.9631
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6232363e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1592 - accuracy: 0.9758 - val_loss: 0.2122 - val_accuracy: 0.9599
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9594892e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1584 - accuracy: 0.9762 - val_loss: 0.2291 - val_accuracy: 0.9571
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.2852249e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1551 - accuracy: 0.9760 - val_loss: 0.3223 - val_accuracy: 0.9341
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8722508e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1611 - accuracy: 0.9762 - val_loss: 0.2204 - val_accuracy: 0.9603
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5154071e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1551 - accuracy: 0.9768 - val_loss: 0.2128 - val_accuracy: 0.9621
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2491148e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1532 - accuracy: 0.9769 - val_loss: 0.2448 - val_accuracy: 0.9500
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7739255e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1544 - accuracy: 0.9771 - val_loss: 0.2481 - val_accuracy: 0.9484
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6205281e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1594 - accuracy: 0.9754 - val_loss: 0.2512 - val_accuracy: 0.9489
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.6063837e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1570 - accuracy: 0.9761 - val_loss: 0.2326 - val_accuracy: 0.9535
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8455185e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1558 - accuracy: 0.9764 - val_loss: 0.2211 - val_accuracy: 0.9577
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5762023e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 74/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1555 - accuracy: 0.9760 - val_loss: 0.2210 - val_accuracy: 0.9569
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8119309e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1594 - accuracy: 0.9750 - val_loss: 0.2384 - val_accuracy: 0.9513
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8524879e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1540 - accuracy: 0.9770 - val_loss: 0.2414 - val_accuracy: 0.9506
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4880884e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1583 - accuracy: 0.9746 - val_loss: 0.2567 - val_accuracy: 0.9464
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0576400e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1505 - accuracy: 0.9772 - val_loss: 0.2079 - val_accuracy: 0.9613
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8642399e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1557 - accuracy: 0.9762 - val_loss: 0.2514 - val_accuracy: 0.9473
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1079827e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1553 - accuracy: 0.9764 - val_loss: 0.2307 - val_accuracy: 0.9537
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7432704e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1512 - accuracy: 0.9770 - val_loss: 0.2104 - val_accuracy: 0.9607
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3468433e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1539 - accuracy: 0.9763 - val_loss: 0.2120 - val_accuracy: 0.9594
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3807237e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1522 - accuracy: 0.9771 - val_loss: 0.2369 - val_accuracy: 0.9487
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8086157e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1528 - accuracy: 0.9766 - val_loss: 0.2111 - val_accuracy: 0.9606
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3310918e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1530 - accuracy: 0.9773 - val_loss: 0.2162 - val_accuracy: 0.9583
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7753489e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1523 - accuracy: 0.9776 - val_loss: 0.2796 - val_accuracy: 0.9385
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0264689e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1540 - accuracy: 0.9766 - val_loss: 0.2305 - val_accuracy: 0.9524
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5169318e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1520 - accuracy: 0.9766 - val_loss: 0.2124 - val_accuracy: 0.9604
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4791123e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1499 - accuracy: 0.9777 - val_loss: 0.2332 - val_accuracy: 0.9510
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7860077e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 90/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1513 - accuracy: 0.9775 - val_loss: 0.2745 - val_accuracy: 0.9423
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6763523e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1549 - accuracy: 0.9760 - val_loss: 0.2417 - val_accuracy: 0.9493
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6572596e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1523 - accuracy: 0.9769 - val_loss: 0.2319 - val_accuracy: 0.9526
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9733367e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1542 - accuracy: 0.9763 - val_loss: 0.2368 - val_accuracy: 0.9521
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6787398e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1548 - accuracy: 0.9758 - val_loss: 0.2631 - val_accuracy: 0.9456
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5368683e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1538 - accuracy: 0.9766 - val_loss: 0.2074 - val_accuracy: 0.9615
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0767329e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1510 - accuracy: 0.9770 - val_loss: 0.2391 - val_accuracy: 0.9502
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8566016e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1477 - accuracy: 0.9783 - val_loss: 0.2190 - val_accuracy: 0.9539
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0767149e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1514 - accuracy: 0.9765 - val_loss: 0.2257 - val_accuracy: 0.9557
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4315633e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 99/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9756 - val_loss: 0.2319 - val_accuracy: 0.9534
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5027970e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1513 - accuracy: 0.9769 - val_loss: 0.1994 - val_accuracy: 0.9631
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3564549e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0024530428249436515
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 4.926172928679925e-34
Thresholhold -2.560368352343766e-34
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.00025495871407268944
Thresholhold -9.990911848944961e-07
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.0264767769331149
Thresholhold 0.043087732046842575
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.]
 [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 147s 12ms/step - loss: 0.1474 - accuracy: 0.9779 - val_loss: 0.2269 - val_accuracy: 0.9548
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7340872e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1516 - accuracy: 0.9770 - val_loss: 0.2108 - val_accuracy: 0.9590
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8165491e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1478 - accuracy: 0.9780 - val_loss: 0.2220 - val_accuracy: 0.9544
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7605828e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1501 - accuracy: 0.9767 - val_loss: 0.2846 - val_accuracy: 0.9380
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1117983e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1499 - accuracy: 0.9768 - val_loss: 0.2269 - val_accuracy: 0.9546
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5094514e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1511 - accuracy: 0.9766 - val_loss: 0.2222 - val_accuracy: 0.9569
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7371099e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 107/500
235/235 [==============================] - 3s 12ms/step - loss: 0.1516 - accuracy: 0.9759 - val_loss: 0.2333 - val_accuracy: 0.9532
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7972121e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1479 - accuracy: 0.9773 - val_loss: 0.2288 - val_accuracy: 0.9540
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1929836e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 109/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1493 - accuracy: 0.9769 - val_loss: 0.2341 - val_accuracy: 0.9534
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5879184e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1516 - accuracy: 0.9768 - val_loss: 0.2011 - val_accuracy: 0.9625
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8913232e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9779 - val_loss: 0.2257 - val_accuracy: 0.9559
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8708929e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 112/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1495 - accuracy: 0.9767 - val_loss: 0.2013 - val_accuracy: 0.9608
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1846374e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.002877535687453043
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1487 - accuracy: 0.9779 - val_loss: 0.2094 - val_accuracy: 0.9602
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3272289e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1489 - accuracy: 0.9766 - val_loss: 0.2233 - val_accuracy: 0.9558
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4960888e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1501 - accuracy: 0.9764 - val_loss: 0.2487 - val_accuracy: 0.9475
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3894960e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 116/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1460 - accuracy: 0.9781 - val_loss: 0.2171 - val_accuracy: 0.9567
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6326598e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1501 - accuracy: 0.9773 - val_loss: 0.2103 - val_accuracy: 0.9584
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6206286e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1488 - accuracy: 0.9772 - val_loss: 0.2459 - val_accuracy: 0.9495
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7402652e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1467 - accuracy: 0.9776 - val_loss: 0.2164 - val_accuracy: 0.9603
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4788672e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1460 - accuracy: 0.9777 - val_loss: 0.2075 - val_accuracy: 0.9603
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1660549e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9774 - val_loss: 0.2362 - val_accuracy: 0.9514
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3427127e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1480 - accuracy: 0.9770 - val_loss: 0.2242 - val_accuracy: 0.9557
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3067230e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1457 - accuracy: 0.9779 - val_loss: 0.1938 - val_accuracy: 0.9631
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7639778e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1478 - accuracy: 0.9771 - val_loss: 0.2203 - val_accuracy: 0.9571
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.6403728e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9787 - val_loss: 0.2200 - val_accuracy: 0.9575
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9369605e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1466 - accuracy: 0.9775 - val_loss: 0.2159 - val_accuracy: 0.9580
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1304657e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1441 - accuracy: 0.9782 - val_loss: 0.2047 - val_accuracy: 0.9620
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1577565e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1486 - accuracy: 0.9767 - val_loss: 0.2132 - val_accuracy: 0.9591
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0260121e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1447 - accuracy: 0.9780 - val_loss: 0.2373 - val_accuracy: 0.9509
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1072462e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9782 - val_loss: 0.2105 - val_accuracy: 0.9593
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0291023e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1448 - accuracy: 0.9775 - val_loss: 0.2108 - val_accuracy: 0.9588
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9057268e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9770 - val_loss: 0.1934 - val_accuracy: 0.9652
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8031303e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1449 - accuracy: 0.9781 - val_loss: 0.2064 - val_accuracy: 0.9614
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8111161e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1488 - accuracy: 0.9773 - val_loss: 0.2267 - val_accuracy: 0.9557
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3993524e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1451 - accuracy: 0.9778 - val_loss: 0.1995 - val_accuracy: 0.9629
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0957274e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1480 - accuracy: 0.9768 - val_loss: 0.1840 - val_accuracy: 0.9678
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5981564e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1452 - accuracy: 0.9775 - val_loss: 0.2049 - val_accuracy: 0.9618
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5725011e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1465 - accuracy: 0.9778 - val_loss: 0.2254 - val_accuracy: 0.9546
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6130387e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9783 - val_loss: 0.2153 - val_accuracy: 0.9561
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6172237e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9780 - val_loss: 0.1862 - val_accuracy: 0.9660
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3258684e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 141/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1461 - accuracy: 0.9772 - val_loss: 0.2048 - val_accuracy: 0.9608
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0771313e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 142/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9773 - val_loss: 0.1780 - val_accuracy: 0.9699
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9594475e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9789 - val_loss: 0.2126 - val_accuracy: 0.9599
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2371365e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1472 - accuracy: 0.9772 - val_loss: 0.2026 - val_accuracy: 0.9607
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5736921e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1466 - accuracy: 0.9780 - val_loss: 0.2110 - val_accuracy: 0.9594
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5024628e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1462 - accuracy: 0.9772 - val_loss: 0.2037 - val_accuracy: 0.9607
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7963957e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1422 - accuracy: 0.9784 - val_loss: 0.2119 - val_accuracy: 0.9601
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4010366e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1457 - accuracy: 0.9772 - val_loss: 0.2077 - val_accuracy: 0.9614
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8241154e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1434 - accuracy: 0.9784 - val_loss: 0.1986 - val_accuracy: 0.9625
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0256540e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 150/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1458 - accuracy: 0.9778 - val_loss: 0.1997 - val_accuracy: 0.9621
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6774714e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 5.619860892015827e-34
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.0018291454454020459
Thresholhold -0.001795717398636043
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.03759050469673397
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.]
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 [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.]
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 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.]
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 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
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 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
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 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
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 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
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 [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.]
 [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 251s 12ms/step - loss: 0.1432 - accuracy: 0.9777 - val_loss: 0.1988 - val_accuracy: 0.9614
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4348849e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 152/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1441 - accuracy: 0.9777 - val_loss: 0.2182 - val_accuracy: 0.9566
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5921263e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9780 - val_loss: 0.2045 - val_accuracy: 0.9600
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8064080e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 154/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1457 - accuracy: 0.9766 - val_loss: 0.2101 - val_accuracy: 0.9581
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9602148e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1456 - accuracy: 0.9772 - val_loss: 0.2207 - val_accuracy: 0.9565
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6086163e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1422 - accuracy: 0.9786 - val_loss: 0.2355 - val_accuracy: 0.9555
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4522109e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1461 - accuracy: 0.9768 - val_loss: 0.1981 - val_accuracy: 0.9643
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1235401e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1469 - accuracy: 0.9770 - val_loss: 0.1932 - val_accuracy: 0.9638
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4198027e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1427 - accuracy: 0.9780 - val_loss: 0.2035 - val_accuracy: 0.9623
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8868539e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1450 - accuracy: 0.9773 - val_loss: 0.2101 - val_accuracy: 0.9600
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3851944e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9776 - val_loss: 0.2023 - val_accuracy: 0.9643
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.3946560e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1433 - accuracy: 0.9776 - val_loss: 0.2012 - val_accuracy: 0.9590
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.1850869e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9779 - val_loss: 0.1866 - val_accuracy: 0.9664
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8175266e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9782 - val_loss: 0.2507 - val_accuracy: 0.9490
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9655440e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9779 - val_loss: 0.2046 - val_accuracy: 0.9616
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8017098e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1446 - accuracy: 0.9777 - val_loss: 0.2014 - val_accuracy: 0.9619
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5049448e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9788 - val_loss: 0.2097 - val_accuracy: 0.9594
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4673475e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9778 - val_loss: 0.2164 - val_accuracy: 0.9592
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3729620e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1437 - accuracy: 0.9780 - val_loss: 0.2034 - val_accuracy: 0.9610
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7897200e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1426 - accuracy: 0.9779 - val_loss: 0.2133 - val_accuracy: 0.9583
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7827686e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1433 - accuracy: 0.9779 - val_loss: 0.2072 - val_accuracy: 0.9618
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0117247e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1458 - accuracy: 0.9772 - val_loss: 0.2201 - val_accuracy: 0.9588
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6057811e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9778 - val_loss: 0.2276 - val_accuracy: 0.9539
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5762463e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9782 - val_loss: 0.1962 - val_accuracy: 0.9630
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8154655e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9781 - val_loss: 0.2007 - val_accuracy: 0.9610
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0377440e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1464 - accuracy: 0.9767 - val_loss: 0.2197 - val_accuracy: 0.9547
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7622933e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9786 - val_loss: 0.2222 - val_accuracy: 0.9567
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2563994e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1434 - accuracy: 0.9782 - val_loss: 0.2116 - val_accuracy: 0.9605
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8479757e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1477 - accuracy: 0.9764 - val_loss: 0.2039 - val_accuracy: 0.9611
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8755266e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9782 - val_loss: 0.2174 - val_accuracy: 0.9566
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8379736e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9776 - val_loss: 0.2027 - val_accuracy: 0.9621
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9237309e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9788 - val_loss: 0.2314 - val_accuracy: 0.9550
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8424794e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1432 - accuracy: 0.9776 - val_loss: 0.1894 - val_accuracy: 0.9650
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9047945e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1434 - accuracy: 0.9779 - val_loss: 0.2096 - val_accuracy: 0.9579
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8282625e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1469 - accuracy: 0.9770 - val_loss: 0.2093 - val_accuracy: 0.9588
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4128387e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1425 - accuracy: 0.9785 - val_loss: 0.2018 - val_accuracy: 0.9641
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5119910e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9774 - val_loss: 0.2186 - val_accuracy: 0.9589
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0939251e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9780 - val_loss: 0.1902 - val_accuracy: 0.9636
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9516870e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9783 - val_loss: 0.2309 - val_accuracy: 0.9532
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4989741e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 190/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1425 - accuracy: 0.9788 - val_loss: 0.2091 - val_accuracy: 0.9595
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2596156e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1419 - accuracy: 0.9782 - val_loss: 0.1975 - val_accuracy: 0.9654
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7473664e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1420 - accuracy: 0.9782 - val_loss: 0.2128 - val_accuracy: 0.9594
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.3646470e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9783 - val_loss: 0.2309 - val_accuracy: 0.9560
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7718984e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9779 - val_loss: 0.1888 - val_accuracy: 0.9662
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.3404366e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1434 - accuracy: 0.9782 - val_loss: 0.2126 - val_accuracy: 0.9581
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5420225e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1420 - accuracy: 0.9782 - val_loss: 0.2172 - val_accuracy: 0.9587
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -9.7393952e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9780 - val_loss: 0.1922 - val_accuracy: 0.9644
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0521255e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1454 - accuracy: 0.9772 - val_loss: 0.2177 - val_accuracy: 0.9582
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4909328e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9776 - val_loss: 0.2097 - val_accuracy: 0.9609
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.1881821e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.2083 - val_accuracy: 0.9597
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7777746e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.0028812922614575506
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.0006630604865468515
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.0027292768771942644
Thresholhold 4.479024210013449e-05
Using suggest threshold.
Applying new mask
Percentage zeros 0.6913667
tf.Tensor(
[[1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.06058478864886663
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
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 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
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 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 283s 12ms/step - loss: 0.1400 - accuracy: 0.9785 - val_loss: 0.2091 - val_accuracy: 0.9584
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0042248e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 202/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9782 - val_loss: 0.2333 - val_accuracy: 0.9531
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8820280e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9778 - val_loss: 0.1991 - val_accuracy: 0.9639
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8870313e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1398 - accuracy: 0.9791 - val_loss: 0.2044 - val_accuracy: 0.9632
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8297318e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1460 - accuracy: 0.9769 - val_loss: 0.2196 - val_accuracy: 0.9584
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -9.9843424e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9784 - val_loss: 0.2234 - val_accuracy: 0.9579
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2245385e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1441 - accuracy: 0.9771 - val_loss: 0.2244 - val_accuracy: 0.9567
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -6.1300495e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9771 - val_loss: 0.1876 - val_accuracy: 0.9659
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4289498e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9778 - val_loss: 0.1960 - val_accuracy: 0.9623
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3784091e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1419 - accuracy: 0.9783 - val_loss: 0.1930 - val_accuracy: 0.9642
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4537725e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9786 - val_loss: 0.1889 - val_accuracy: 0.9637
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5739435e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9790 - val_loss: 0.2110 - val_accuracy: 0.9589
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2721687e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1436 - accuracy: 0.9775 - val_loss: 0.2224 - val_accuracy: 0.9552
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9474670e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9759 - val_loss: 0.2044 - val_accuracy: 0.9613
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0428546e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9780 - val_loss: 0.1994 - val_accuracy: 0.9624
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5611430e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9775 - val_loss: 0.2110 - val_accuracy: 0.9581
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0070813e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9792 - val_loss: 0.2153 - val_accuracy: 0.9559
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6013933e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1451 - accuracy: 0.9762 - val_loss: 0.1989 - val_accuracy: 0.9611
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.1088681e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9780 - val_loss: 0.2091 - val_accuracy: 0.9574
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7631242e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1422 - accuracy: 0.9774 - val_loss: 0.1997 - val_accuracy: 0.9605
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3923267e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9789 - val_loss: 0.2161 - val_accuracy: 0.9569
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9308029e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9778 - val_loss: 0.2388 - val_accuracy: 0.9505
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5355762e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9771 - val_loss: 0.1975 - val_accuracy: 0.9633
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4227325e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9774 - val_loss: 0.1874 - val_accuracy: 0.9650
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0333963e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9780 - val_loss: 0.2132 - val_accuracy: 0.9571
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3218682e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1412 - accuracy: 0.9779 - val_loss: 0.2008 - val_accuracy: 0.9616
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6272334e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9792 - val_loss: 0.2054 - val_accuracy: 0.9590
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5948100e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1427 - accuracy: 0.9774 - val_loss: 0.1971 - val_accuracy: 0.9617
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5657643e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9791 - val_loss: 0.2009 - val_accuracy: 0.9609
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8899333e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9779 - val_loss: 0.2064 - val_accuracy: 0.9623
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2386646e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1437 - accuracy: 0.9780 - val_loss: 0.2467 - val_accuracy: 0.9508
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9658471e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9785 - val_loss: 0.2043 - val_accuracy: 0.9597
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -7.8463554e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9783 - val_loss: 0.2288 - val_accuracy: 0.9550
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8861985e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9784 - val_loss: 0.1950 - val_accuracy: 0.9636
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9484596e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9772 - val_loss: 0.2445 - val_accuracy: 0.9509
[-2.56036835e-34 -4.89440904e-34  2.81328974e-34 ... -1.29702175e-02
  0.00000000e+00 -0.00000000e+00]
Sparsity at: 0.08079263711495116
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9780 - val_loss: 0.2279 - val_accuracy: 0.9557
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5008717e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9786 - val_loss: 0.2311 - val_accuracy: 0.9539
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4113965e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1416 - accuracy: 0.9774 - val_loss: 0.2066 - val_accuracy: 0.9596
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0237274e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9786 - val_loss: 0.2141 - val_accuracy: 0.9589
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.3684629e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9776 - val_loss: 0.2297 - val_accuracy: 0.9529
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4847353e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9772 - val_loss: 0.2199 - val_accuracy: 0.9542
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5585964e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9783 - val_loss: 0.1988 - val_accuracy: 0.9615
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6001774e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9781 - val_loss: 0.1935 - val_accuracy: 0.9604
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8708110e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9784 - val_loss: 0.2526 - val_accuracy: 0.9463
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5572954e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9775 - val_loss: 0.2234 - val_accuracy: 0.9554
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3900634e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1446 - accuracy: 0.9765 - val_loss: 0.1747 - val_accuracy: 0.9709
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -8.7204371e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9790 - val_loss: 0.2070 - val_accuracy: 0.9587
[-2.56036835e-34 -4.89440904e-34  2.81328974e-34 ... -1.15005085e-02
 -0.00000000e+00 -0.00000000e+00]
Sparsity at: 0.08079263711495116
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9776 - val_loss: 0.1859 - val_accuracy: 0.9673
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.1117669e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9787 - val_loss: 0.2471 - val_accuracy: 0.9501
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4919719e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9786 - val_loss: 0.2186 - val_accuracy: 0.9566
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4372821e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.0055774101495831285
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.008124002356425186
Thresholhold -0.00010690836643334478
Using suggest threshold.
Applying new mask
Percentage zeros 0.6913667
tf.Tensor(
[[1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.07689945912113849
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.]
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235/235 [==============================] - 246s 12ms/step - loss: 0.1371 - accuracy: 0.9791 - val_loss: 0.1988 - val_accuracy: 0.9613
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9926574e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 252/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9770 - val_loss: 0.2026 - val_accuracy: 0.9603
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9850316e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9780 - val_loss: 0.2301 - val_accuracy: 0.9538
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9919008e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9783 - val_loss: 0.2038 - val_accuracy: 0.9600
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0868331e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1448 - accuracy: 0.9766 - val_loss: 0.2055 - val_accuracy: 0.9601
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0792633e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9771 - val_loss: 0.2018 - val_accuracy: 0.9598
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0981556e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9778 - val_loss: 0.2395 - val_accuracy: 0.9499
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9541247e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9779 - val_loss: 0.1942 - val_accuracy: 0.9623
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4323111e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9778 - val_loss: 0.2075 - val_accuracy: 0.9595
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.1474449e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9782 - val_loss: 0.2324 - val_accuracy: 0.9508
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9734265e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9785 - val_loss: 0.2277 - val_accuracy: 0.9532
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5383526e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.2512 - val_accuracy: 0.9447
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4590986e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9778 - val_loss: 0.1965 - val_accuracy: 0.9616
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0154109e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9773 - val_loss: 0.2365 - val_accuracy: 0.9491
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.1166678e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9770 - val_loss: 0.1840 - val_accuracy: 0.9667
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5040148e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9773 - val_loss: 0.2208 - val_accuracy: 0.9553
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2368008e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1426 - accuracy: 0.9767 - val_loss: 0.2025 - val_accuracy: 0.9592
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4630641e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9789 - val_loss: 0.2234 - val_accuracy: 0.9539
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2025933e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9782 - val_loss: 0.2465 - val_accuracy: 0.9487
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0954208e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9777 - val_loss: 0.2061 - val_accuracy: 0.9595
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8633414e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9784 - val_loss: 0.1934 - val_accuracy: 0.9643
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8346190e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9779 - val_loss: 0.2181 - val_accuracy: 0.9593
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6156707e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9790 - val_loss: 0.1959 - val_accuracy: 0.9623
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5639837e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9775 - val_loss: 0.2361 - val_accuracy: 0.9540
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5129525e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9788 - val_loss: 0.2401 - val_accuracy: 0.9492
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6113627e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9783 - val_loss: 0.2134 - val_accuracy: 0.9584
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5714562e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9784 - val_loss: 0.2387 - val_accuracy: 0.9511
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.0575075e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9788 - val_loss: 0.2352 - val_accuracy: 0.9513
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.1532914e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9775 - val_loss: 0.2064 - val_accuracy: 0.9608
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3198163e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9788 - val_loss: 0.1887 - val_accuracy: 0.9638
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6746594e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9780 - val_loss: 0.2185 - val_accuracy: 0.9573
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4425127e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9788 - val_loss: 0.2277 - val_accuracy: 0.9544
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1741086e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9775 - val_loss: 0.2080 - val_accuracy: 0.9591
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6630126e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9791 - val_loss: 0.1900 - val_accuracy: 0.9651
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1140881e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9791 - val_loss: 0.2180 - val_accuracy: 0.9556
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1135535e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9798 - val_loss: 0.2132 - val_accuracy: 0.9544
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9423997e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9778 - val_loss: 0.1876 - val_accuracy: 0.9654
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2583512e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9778 - val_loss: 0.1880 - val_accuracy: 0.9656
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5495255e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9788 - val_loss: 0.2170 - val_accuracy: 0.9539
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9497972e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9782 - val_loss: 0.2186 - val_accuracy: 0.9563
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8728999e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9782 - val_loss: 0.1995 - val_accuracy: 0.9610
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9619714e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9772 - val_loss: 0.2231 - val_accuracy: 0.9552
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0126905e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9783 - val_loss: 0.2200 - val_accuracy: 0.9576
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3369575e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9786 - val_loss: 0.2073 - val_accuracy: 0.9588
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5960376e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9784 - val_loss: 0.1943 - val_accuracy: 0.9623
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5018768e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9788 - val_loss: 0.2449 - val_accuracy: 0.9489
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4718892e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1407 - accuracy: 0.9770 - val_loss: 0.2190 - val_accuracy: 0.9555
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9997351e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9779 - val_loss: 0.2061 - val_accuracy: 0.9608
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6326047e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9779 - val_loss: 0.2297 - val_accuracy: 0.9525
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3641780e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9782 - val_loss: 0.2174 - val_accuracy: 0.9585
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0602215e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.011956146164944559
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.02037388842071186
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6913667
tf.Tensor(
[[1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.08851680539852769
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.]
 [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 242s 12ms/step - loss: 0.1399 - accuracy: 0.9776 - val_loss: 0.1869 - val_accuracy: 0.9649
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3109054e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9779 - val_loss: 0.2134 - val_accuracy: 0.9575
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8581019e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9793 - val_loss: 0.1974 - val_accuracy: 0.9620
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2463856e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9787 - val_loss: 0.2645 - val_accuracy: 0.9426
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8818538e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9782 - val_loss: 0.2470 - val_accuracy: 0.9475
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4545429e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1412 - accuracy: 0.9777 - val_loss: 0.2257 - val_accuracy: 0.9550
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1966511e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9772 - val_loss: 0.2315 - val_accuracy: 0.9555
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2895826e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9795 - val_loss: 0.1975 - val_accuracy: 0.9617
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8791126e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9770 - val_loss: 0.2447 - val_accuracy: 0.9516
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5958786e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9784 - val_loss: 0.2152 - val_accuracy: 0.9579
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5610352e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9784 - val_loss: 0.1860 - val_accuracy: 0.9661
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2951361e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9788 - val_loss: 0.2032 - val_accuracy: 0.9618
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7323251e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9782 - val_loss: 0.1876 - val_accuracy: 0.9667
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7108749e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9777 - val_loss: 0.2028 - val_accuracy: 0.9609
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5774030e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9781 - val_loss: 0.2094 - val_accuracy: 0.9573
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.8658585e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9782 - val_loss: 0.2007 - val_accuracy: 0.9611
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7571140e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9782 - val_loss: 0.2196 - val_accuracy: 0.9554
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7734468e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9782 - val_loss: 0.2539 - val_accuracy: 0.9466
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6704507e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9781 - val_loss: 0.1995 - val_accuracy: 0.9642
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.0943608e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9785 - val_loss: 0.2130 - val_accuracy: 0.9578
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4880321e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9781 - val_loss: 0.1809 - val_accuracy: 0.9661
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9023029e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9781 - val_loss: 0.2028 - val_accuracy: 0.9620
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3633858e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9783 - val_loss: 0.2084 - val_accuracy: 0.9589
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6815133e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9780 - val_loss: 0.2016 - val_accuracy: 0.9615
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4838930e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9782 - val_loss: 0.2225 - val_accuracy: 0.9550
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0898102e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9782 - val_loss: 0.1913 - val_accuracy: 0.9647
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.0584430e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9784 - val_loss: 0.2144 - val_accuracy: 0.9591
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7760984e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9798 - val_loss: 0.1966 - val_accuracy: 0.9618
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3543628e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9788 - val_loss: 0.2242 - val_accuracy: 0.9551
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.3523157e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9778 - val_loss: 0.2127 - val_accuracy: 0.9591
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4794744e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9789 - val_loss: 0.2049 - val_accuracy: 0.9588
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7919387e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9785 - val_loss: 0.1929 - val_accuracy: 0.9634
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8479028e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9786 - val_loss: 0.2077 - val_accuracy: 0.9596
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4599774e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9776 - val_loss: 0.1962 - val_accuracy: 0.9631
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9399632e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9800 - val_loss: 0.2228 - val_accuracy: 0.9559
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7051238e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9772 - val_loss: 0.1976 - val_accuracy: 0.9615
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.2344315e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9788 - val_loss: 0.2115 - val_accuracy: 0.9588
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9027084e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9791 - val_loss: 0.2511 - val_accuracy: 0.9489
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5207154e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9785 - val_loss: 0.1916 - val_accuracy: 0.9638
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9786127e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9774 - val_loss: 0.2080 - val_accuracy: 0.9607
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5547180e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9780 - val_loss: 0.2018 - val_accuracy: 0.9621
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5384245e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9791 - val_loss: 0.1923 - val_accuracy: 0.9635
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2875739e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9787 - val_loss: 0.2494 - val_accuracy: 0.9540
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5110142e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9770 - val_loss: 0.2206 - val_accuracy: 0.9574
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9313115e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9772 - val_loss: 0.2196 - val_accuracy: 0.9564
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5039060e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9769 - val_loss: 0.1874 - val_accuracy: 0.9659
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7603485e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9777 - val_loss: 0.1926 - val_accuracy: 0.9642
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2133021e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 348/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1372 - accuracy: 0.9781 - val_loss: 0.2242 - val_accuracy: 0.9563
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4914635e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 349/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1384 - accuracy: 0.9778 - val_loss: 0.2285 - val_accuracy: 0.9553
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1395856e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 350/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1340 - accuracy: 0.9794 - val_loss: 0.1906 - val_accuracy: 0.9651
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5276208e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.01804403414086142
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.0315618626416323
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6913667
tf.Tensor(
[[1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.09637593709484982
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.]
 [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 242s 12ms/step - loss: 0.1374 - accuracy: 0.9779 - val_loss: 0.1978 - val_accuracy: 0.9622
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1191578e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 352/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1354 - accuracy: 0.9783 - val_loss: 0.2059 - val_accuracy: 0.9590
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9156124e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9790 - val_loss: 0.2269 - val_accuracy: 0.9538
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1038186e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9782 - val_loss: 0.2011 - val_accuracy: 0.9625
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9574526e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9784 - val_loss: 0.2029 - val_accuracy: 0.9594
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3136616e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9776 - val_loss: 0.2138 - val_accuracy: 0.9589
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4881342e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9782 - val_loss: 0.1972 - val_accuracy: 0.9625
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4045909e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9785 - val_loss: 0.2229 - val_accuracy: 0.9556
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5729144e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9778 - val_loss: 0.1805 - val_accuracy: 0.9657
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9612677e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9777 - val_loss: 0.2233 - val_accuracy: 0.9552
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7865147e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.2020 - val_accuracy: 0.9614
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7020829e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9786 - val_loss: 0.1841 - val_accuracy: 0.9679
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5996443e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9780 - val_loss: 0.2206 - val_accuracy: 0.9565
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9774617e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9786 - val_loss: 0.2508 - val_accuracy: 0.9461
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4179785e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9789 - val_loss: 0.2080 - val_accuracy: 0.9587
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2145350e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9779 - val_loss: 0.2213 - val_accuracy: 0.9558
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1028211e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 367/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1359 - accuracy: 0.9780 - val_loss: 0.2276 - val_accuracy: 0.9511
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.5201207e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9791 - val_loss: 0.1959 - val_accuracy: 0.9618
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6543187e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9777 - val_loss: 0.1854 - val_accuracy: 0.9642
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1356763e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9785 - val_loss: 0.2127 - val_accuracy: 0.9583
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7537996e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9780 - val_loss: 0.1914 - val_accuracy: 0.9629
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6804922e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9792 - val_loss: 0.3236 - val_accuracy: 0.9280
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.7541758e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9771 - val_loss: 0.1952 - val_accuracy: 0.9615
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.9260179e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9777 - val_loss: 0.2032 - val_accuracy: 0.9604
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6771592e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 375/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9789 - val_loss: 0.2036 - val_accuracy: 0.9651
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -4.3876678e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9795 - val_loss: 0.2113 - val_accuracy: 0.9607
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4705311e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9783 - val_loss: 0.2050 - val_accuracy: 0.9604
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.6289532e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9792 - val_loss: 0.2243 - val_accuracy: 0.9522
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4004416e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9775 - val_loss: 0.1964 - val_accuracy: 0.9627
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6782263e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9794 - val_loss: 0.2277 - val_accuracy: 0.9544
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7520023e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9772 - val_loss: 0.2313 - val_accuracy: 0.9525
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6205799e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9786 - val_loss: 0.2011 - val_accuracy: 0.9614
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0559093e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9776 - val_loss: 0.2230 - val_accuracy: 0.9566
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8286600e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9783 - val_loss: 0.1862 - val_accuracy: 0.9642
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4270076e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9783 - val_loss: 0.1959 - val_accuracy: 0.9605
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0172316e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9788 - val_loss: 0.2137 - val_accuracy: 0.9573
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7580922e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9780 - val_loss: 0.2223 - val_accuracy: 0.9537
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7315075e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 388/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9782 - val_loss: 0.2121 - val_accuracy: 0.9578
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2412516e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9782 - val_loss: 0.2060 - val_accuracy: 0.9619
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.1700323e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9786 - val_loss: 0.1936 - val_accuracy: 0.9633
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3233935e-02
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9797 - val_loss: 0.1966 - val_accuracy: 0.9623
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9340305e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9779 - val_loss: 0.1950 - val_accuracy: 0.9620
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0886743e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9788 - val_loss: 0.2027 - val_accuracy: 0.9600
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.9419363e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9785 - val_loss: 0.1886 - val_accuracy: 0.9635
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.4742918e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9793 - val_loss: 0.2038 - val_accuracy: 0.9621
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1377390e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9786 - val_loss: 0.2240 - val_accuracy: 0.9551
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1118246e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9794 - val_loss: 0.2129 - val_accuracy: 0.9597
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7808692e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9782 - val_loss: 0.2369 - val_accuracy: 0.9519
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.0900404e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9793 - val_loss: 0.1914 - val_accuracy: 0.9631
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5699098e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9772 - val_loss: 0.2032 - val_accuracy: 0.9601
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2026522e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.02167989121451175
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.038286437850723054
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6913667
tf.Tensor(
[[1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.10055417756025697
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.]
 [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.]
 [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 254s 12ms/step - loss: 0.1353 - accuracy: 0.9778 - val_loss: 0.2402 - val_accuracy: 0.9516
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2561865e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 402/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1349 - accuracy: 0.9785 - val_loss: 0.2144 - val_accuracy: 0.9575
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5631556e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9790 - val_loss: 0.2159 - val_accuracy: 0.9578
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0545743e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9784 - val_loss: 0.1870 - val_accuracy: 0.9648
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.1902237e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9782 - val_loss: 0.1930 - val_accuracy: 0.9646
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4474531e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9787 - val_loss: 0.1940 - val_accuracy: 0.9610
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9076265e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9789 - val_loss: 0.1852 - val_accuracy: 0.9669
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6136156e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9780 - val_loss: 0.2041 - val_accuracy: 0.9617
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6337961e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9787 - val_loss: 0.1798 - val_accuracy: 0.9671
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4258608e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9794 - val_loss: 0.1905 - val_accuracy: 0.9644
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7521985e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9785 - val_loss: 0.1829 - val_accuracy: 0.9668
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7858838e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9780 - val_loss: 0.1941 - val_accuracy: 0.9626
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6426958e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9777 - val_loss: 0.2133 - val_accuracy: 0.9562
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6062124e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9795 - val_loss: 0.2031 - val_accuracy: 0.9602
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7624266e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9779 - val_loss: 0.1851 - val_accuracy: 0.9664
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6296059e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9779 - val_loss: 0.1891 - val_accuracy: 0.9641
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5451455e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9784 - val_loss: 0.2133 - val_accuracy: 0.9586
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3117118e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9792 - val_loss: 0.2011 - val_accuracy: 0.9595
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5754463e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9778 - val_loss: 0.1866 - val_accuracy: 0.9640
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0274285e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9783 - val_loss: 0.1712 - val_accuracy: 0.9696
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3650607e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9797 - val_loss: 0.1923 - val_accuracy: 0.9636
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6112022e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9784 - val_loss: 0.1794 - val_accuracy: 0.9650
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.7580397e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9783 - val_loss: 0.1758 - val_accuracy: 0.9680
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6160561e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9780 - val_loss: 0.1786 - val_accuracy: 0.9685
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0713614e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9786 - val_loss: 0.1941 - val_accuracy: 0.9642
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7155344e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9778 - val_loss: 0.1873 - val_accuracy: 0.9634
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.2580068e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9784 - val_loss: 0.1944 - val_accuracy: 0.9624
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6652159e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9798 - val_loss: 0.2037 - val_accuracy: 0.9601
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -8.4677264e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9786 - val_loss: 0.2059 - val_accuracy: 0.9609
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.2019906e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9785 - val_loss: 0.2061 - val_accuracy: 0.9605
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.6731931e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9786 - val_loss: 0.1874 - val_accuracy: 0.9647
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2097006e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9789 - val_loss: 0.1854 - val_accuracy: 0.9653
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0952864e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9789 - val_loss: 0.2111 - val_accuracy: 0.9558
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0105541e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9786 - val_loss: 0.2171 - val_accuracy: 0.9564
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.9947758e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9786 - val_loss: 0.1891 - val_accuracy: 0.9650
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.4919789e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9788 - val_loss: 0.1973 - val_accuracy: 0.9610
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8312830e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9781 - val_loss: 0.1959 - val_accuracy: 0.9637
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5171225e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9789 - val_loss: 0.2489 - val_accuracy: 0.9477
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5629325e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9788 - val_loss: 0.1854 - val_accuracy: 0.9658
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7955912e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9783 - val_loss: 0.2011 - val_accuracy: 0.9615
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4945451e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9797 - val_loss: 0.1845 - val_accuracy: 0.9653
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5203296e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9784 - val_loss: 0.2036 - val_accuracy: 0.9611
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6438249e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9776 - val_loss: 0.2011 - val_accuracy: 0.9619
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4451780e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9783 - val_loss: 0.1796 - val_accuracy: 0.9652
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.3706082e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9782 - val_loss: 0.2072 - val_accuracy: 0.9608
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8338269e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9786 - val_loss: 0.1970 - val_accuracy: 0.9611
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6608400e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9790 - val_loss: 0.1746 - val_accuracy: 0.9690
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -8.2653482e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9777 - val_loss: 0.1752 - val_accuracy: 0.9699
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.2598254e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9789 - val_loss: 0.1783 - val_accuracy: 0.9669
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8088540e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9791 - val_loss: 0.1926 - val_accuracy: 0.9625
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5899008e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9779 - val_loss: 0.1965 - val_accuracy: 0.9619
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.5309747e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9784 - val_loss: 0.2001 - val_accuracy: 0.9608
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -3.1961724e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9786 - val_loss: 0.1856 - val_accuracy: 0.9656
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2286557e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9800 - val_loss: 0.1977 - val_accuracy: 0.9624
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0924710e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9790 - val_loss: 0.2048 - val_accuracy: 0.9613
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2166774e-02
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9786 - val_loss: 0.2011 - val_accuracy: 0.9622
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2945236e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9786 - val_loss: 0.2008 - val_accuracy: 0.9600
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.0608743e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9785 - val_loss: 0.2054 - val_accuracy: 0.9620
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5700335e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9773 - val_loss: 0.1971 - val_accuracy: 0.9629
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -8.8292509e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9783 - val_loss: 0.1698 - val_accuracy: 0.9689
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.2636602e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9789 - val_loss: 0.1854 - val_accuracy: 0.9653
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -5.7639237e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9791 - val_loss: 0.1976 - val_accuracy: 0.9626
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -8.7813744e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9783 - val_loss: 0.1985 - val_accuracy: 0.9626
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -9.9617587e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9783 - val_loss: 0.1820 - val_accuracy: 0.9675
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -7.8467187e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9801 - val_loss: 0.1751 - val_accuracy: 0.9673
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -9.2773698e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9793 - val_loss: 0.1912 - val_accuracy: 0.9631
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5330495e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9784 - val_loss: 0.2094 - val_accuracy: 0.9596
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0809384e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9787 - val_loss: 0.1750 - val_accuracy: 0.9687
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.0330668e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9786 - val_loss: 0.1946 - val_accuracy: 0.9643
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4218373e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9790 - val_loss: 0.1831 - val_accuracy: 0.9648
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.2071697e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9790 - val_loss: 0.2001 - val_accuracy: 0.9632
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.1375859e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9784 - val_loss: 0.1850 - val_accuracy: 0.9650
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4297594e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 473/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1342 - accuracy: 0.9783 - val_loss: 0.1902 - val_accuracy: 0.9649
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5889972e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9788 - val_loss: 0.1854 - val_accuracy: 0.9653
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -6.1205067e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9793 - val_loss: 0.2552 - val_accuracy: 0.9487
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -6.5993476e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9791 - val_loss: 0.1984 - val_accuracy: 0.9635
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -8.3087897e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9787 - val_loss: 0.2130 - val_accuracy: 0.9572
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -7.6934975e-03
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9784 - val_loss: 0.1914 - val_accuracy: 0.9643
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5138582e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9782 - val_loss: 0.2211 - val_accuracy: 0.9564
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5106151e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9797 - val_loss: 0.1789 - val_accuracy: 0.9660
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7198199e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9788 - val_loss: 0.2028 - val_accuracy: 0.9614
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.8683754e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9786 - val_loss: 0.2030 - val_accuracy: 0.9600
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.8432110e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9778 - val_loss: 0.2010 - val_accuracy: 0.9622
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5458489e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9783 - val_loss: 0.1836 - val_accuracy: 0.9670
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5059527e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9805 - val_loss: 0.2164 - val_accuracy: 0.9569
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2640936e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9784 - val_loss: 0.2170 - val_accuracy: 0.9587
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2943117e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9786 - val_loss: 0.1909 - val_accuracy: 0.9641
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.4153975e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9788 - val_loss: 0.1860 - val_accuracy: 0.9677
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.5981451e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1299 - accuracy: 0.9796 - val_loss: 0.1828 - val_accuracy: 0.9663
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.3238861e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9784 - val_loss: 0.2124 - val_accuracy: 0.9611
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -7.9829395e-03
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9769 - val_loss: 0.1993 - val_accuracy: 0.9631
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.1587327e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9794 - val_loss: 0.2212 - val_accuracy: 0.9555
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.2829201e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9788 - val_loss: 0.1967 - val_accuracy: 0.9636
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.6314967e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9782 - val_loss: 0.1730 - val_accuracy: 0.9692
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -2.0970883e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9780 - val_loss: 0.1899 - val_accuracy: 0.9645
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.3608278e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9790 - val_loss: 0.2224 - val_accuracy: 0.9553
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.3582623e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9790 - val_loss: 0.1897 - val_accuracy: 0.9644
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7563354e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 498/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9784 - val_loss: 0.2028 - val_accuracy: 0.9617
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.7810099e-02
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9784 - val_loss: 0.1976 - val_accuracy: 0.9628
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.1768318e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9800 - val_loss: 0.2011 - val_accuracy: 0.9610
[-2.5603684e-34 -4.8944090e-34  2.8132897e-34 ... -1.2480950e-02
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.08079263711495116
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.03723478130996227
Thresholhold -0.05253946781158447
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.06117202341556549
Thresholhold 0.03103388100862503
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.1142112985253334
Thresholhold -0.009608536958694458
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
  6/235 [..............................] - ETA: 2s - loss: 1.5184 - accuracy: 0.5339     WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0100s vs `on_train_batch_begin` time: 11.3089s). Check your callbacks.
235/235 [==============================] - 71s 12ms/step - loss: 0.2422 - accuracy: 0.9297 - val_loss: 0.2058 - val_accuracy: 0.9573
[-0.05253947 -0.00531845 -0.04093379 ...  0.1630142  -0.22765085
 -0.1300145 ]
Sparsity at: 0.028493613824192337
Epoch 2/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0874 - accuracy: 0.9751 - val_loss: 0.0991 - val_accuracy: 0.9686
[-0.05253947 -0.00531845 -0.04093379 ...  0.18007904 -0.24375299
 -0.14138964]
Sparsity at: 0.028493613824192337
Epoch 3/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0511 - accuracy: 0.9857 - val_loss: 0.0899 - val_accuracy: 0.9708
[-0.05253947 -0.00531845 -0.04093379 ...  0.19417247 -0.25554326
 -0.14446865]
Sparsity at: 0.028493613824192337
Epoch 4/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0315 - accuracy: 0.9918 - val_loss: 0.0844 - val_accuracy: 0.9737
[-0.05253947 -0.00531845 -0.04093379 ...  0.21089199 -0.26832888
 -0.14684612]
Sparsity at: 0.028493613824192337
Epoch 5/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0189 - accuracy: 0.9957 - val_loss: 0.0873 - val_accuracy: 0.9731
[-0.05253947 -0.00531845 -0.04093379 ...  0.22065996 -0.2767522
 -0.14960967]
Sparsity at: 0.028493613824192337
Epoch 6/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0150 - accuracy: 0.9964 - val_loss: 0.0962 - val_accuracy: 0.9718
[-0.05253947 -0.00531845 -0.04093379 ...  0.23070483 -0.286835
 -0.15536873]
Sparsity at: 0.028493613824192337
Epoch 7/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0121 - accuracy: 0.9970 - val_loss: 0.0874 - val_accuracy: 0.9748
[-0.05253947 -0.00531845 -0.04093379 ...  0.24042109 -0.2943962
 -0.16160455]
Sparsity at: 0.028493613824192337
Epoch 8/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0085 - accuracy: 0.9979 - val_loss: 0.0886 - val_accuracy: 0.9741
[-0.05253947 -0.00531845 -0.04093379 ...  0.24257672 -0.29986876
 -0.16383283]
Sparsity at: 0.028493613824192337
Epoch 9/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0096 - accuracy: 0.9975 - val_loss: 0.1026 - val_accuracy: 0.9738
[-0.05253947 -0.00531845 -0.04093379 ...  0.24477692 -0.30399007
 -0.16431989]
Sparsity at: 0.028493613824192337
Epoch 10/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0109 - accuracy: 0.9968 - val_loss: 0.1040 - val_accuracy: 0.9726
[-0.05253947 -0.00531845 -0.04093379 ...  0.25685436 -0.3004289
 -0.16854183]
Sparsity at: 0.028493613824192337
Epoch 11/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.1192 - val_accuracy: 0.9690
[-0.05253947 -0.00531845 -0.04093379 ...  0.25699055 -0.30823794
 -0.17546538]
Sparsity at: 0.028493613824192337
Epoch 12/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0099 - accuracy: 0.9969 - val_loss: 0.1018 - val_accuracy: 0.9734
[-0.05253947 -0.00531845 -0.04093379 ...  0.25446513 -0.32200634
 -0.18417396]
Sparsity at: 0.028493613824192337
Epoch 13/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0072 - accuracy: 0.9980 - val_loss: 0.0846 - val_accuracy: 0.9766
[-0.05253947 -0.00531845 -0.04093379 ...  0.27181855 -0.32726783
 -0.20164146]
Sparsity at: 0.028493613824192337
Epoch 14/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0065 - accuracy: 0.9981 - val_loss: 0.0982 - val_accuracy: 0.9774
[-0.05253947 -0.00531845 -0.04093379 ...  0.28313655 -0.3361384
 -0.18875337]
Sparsity at: 0.028493613824192337
Epoch 15/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0052 - accuracy: 0.9985 - val_loss: 0.0882 - val_accuracy: 0.9787
[-0.05253947 -0.00531845 -0.04093379 ...  0.296955   -0.33465752
 -0.19792363]
Sparsity at: 0.028493613824192337
Epoch 16/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0057 - accuracy: 0.9981 - val_loss: 0.0912 - val_accuracy: 0.9765
[-0.05253947 -0.00531845 -0.04093379 ...  0.2887571  -0.34283307
 -0.19516125]
Sparsity at: 0.028493613824192337
Epoch 17/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0047 - accuracy: 0.9986 - val_loss: 0.0873 - val_accuracy: 0.9791
[-0.05253947 -0.00531845 -0.04093379 ...  0.2946831  -0.34421405
 -0.19301544]
Sparsity at: 0.028493613824192337
Epoch 18/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.0932 - val_accuracy: 0.9769
[-0.05253947 -0.00531845 -0.04093379 ...  0.29551652 -0.35016304
 -0.19330052]
Sparsity at: 0.028493613824192337
Epoch 19/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.1012 - val_accuracy: 0.9782
[-0.05253947 -0.00531845 -0.04093379 ...  0.30277315 -0.35761258
 -0.18982498]
Sparsity at: 0.028493613824192337
Epoch 20/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0096 - accuracy: 0.9967 - val_loss: 0.1269 - val_accuracy: 0.9711
[-0.05253947 -0.00531845 -0.04093379 ...  0.2966441  -0.36450076
 -0.19756731]
Sparsity at: 0.028493613824192337
Epoch 21/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0092 - accuracy: 0.9969 - val_loss: 0.0969 - val_accuracy: 0.9786
[-0.05253947 -0.00531845 -0.04093379 ...  0.30469945 -0.3748371
 -0.2106808 ]
Sparsity at: 0.028493613824192337
Epoch 22/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0070 - accuracy: 0.9976 - val_loss: 0.0994 - val_accuracy: 0.9783
[-0.05253947 -0.00531845 -0.04093379 ...  0.29983428 -0.37453938
 -0.20567834]
Sparsity at: 0.028493613824192337
Epoch 23/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0047 - accuracy: 0.9987 - val_loss: 0.0820 - val_accuracy: 0.9807
[-0.05253947 -0.00531845 -0.04093379 ...  0.30382502 -0.37038073
 -0.2122615 ]
Sparsity at: 0.028493613824192337
Epoch 24/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9997 - val_loss: 0.0796 - val_accuracy: 0.9815
[-0.05253947 -0.00531845 -0.04093379 ...  0.3083112  -0.37620506
 -0.21654834]
Sparsity at: 0.028493613824192337
Epoch 25/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0756 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.3149418  -0.38134745
 -0.22139305]
Sparsity at: 0.028493613824192337
Epoch 26/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3848e-04 - accuracy: 1.0000 - val_loss: 0.0724 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.3180193  -0.38515773
 -0.22014071]
Sparsity at: 0.028493613824192337
Epoch 27/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7906e-04 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9849
[-0.05253947 -0.00531845 -0.04093379 ...  0.31800076 -0.386262
 -0.2203167 ]
Sparsity at: 0.028493613824192337
Epoch 28/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3312e-04 - accuracy: 1.0000 - val_loss: 0.0724 - val_accuracy: 0.9852
[-0.05253947 -0.00531845 -0.04093379 ...  0.31991935 -0.38648432
 -0.22118808]
Sparsity at: 0.028493613824192337
Epoch 29/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0225e-04 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9849
[-0.05253947 -0.00531845 -0.04093379 ...  0.32088023 -0.38670844
 -0.22152913]
Sparsity at: 0.028493613824192337
Epoch 30/500
235/235 [==============================] - 3s 13ms/step - loss: 8.5092e-05 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9853
[-0.05253947 -0.00531845 -0.04093379 ...  0.32219225 -0.38694754
 -0.22246532]
Sparsity at: 0.028493613824192337
Epoch 31/500
235/235 [==============================] - 3s 13ms/step - loss: 8.6307e-05 - accuracy: 1.0000 - val_loss: 0.0736 - val_accuracy: 0.9851
[-0.05253947 -0.00531845 -0.04093379 ...  0.32347164 -0.3893588
 -0.22330298]
Sparsity at: 0.028493613824192337
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 7.0506e-05 - accuracy: 1.0000 - val_loss: 0.0759 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.32495755 -0.38953918
 -0.22432923]
Sparsity at: 0.028493613824192337
Epoch 33/500
235/235 [==============================] - 3s 13ms/step - loss: 6.4666e-05 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9854
[-0.05253947 -0.00531845 -0.04093379 ...  0.3266862  -0.38976952
 -0.22464   ]
Sparsity at: 0.028493613824192337
Epoch 34/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7277e-05 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9850
[-0.05253947 -0.00531845 -0.04093379 ...  0.32789427 -0.39120758
 -0.22566344]
Sparsity at: 0.028493613824192337
Epoch 35/500
235/235 [==============================] - 3s 13ms/step - loss: 4.2608e-05 - accuracy: 1.0000 - val_loss: 0.0760 - val_accuracy: 0.9853
[-0.05253947 -0.00531845 -0.04093379 ...  0.32914403 -0.39171678
 -0.2270096 ]
Sparsity at: 0.028493613824192337
Epoch 36/500
235/235 [==============================] - 3s 13ms/step - loss: 6.6978e-04 - accuracy: 0.9998 - val_loss: 0.1193 - val_accuracy: 0.9761
[-0.05253947 -0.00531845 -0.04093379 ...  0.3310632  -0.39295506
 -0.22753336]
Sparsity at: 0.028493613824192337
Epoch 37/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0507 - accuracy: 0.9848 - val_loss: 0.1027 - val_accuracy: 0.9743
[-0.05253947 -0.00531845 -0.04093379 ...  0.3008628  -0.37332273
 -0.20221902]
Sparsity at: 0.028493613824192337
Epoch 38/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0126 - accuracy: 0.9957 - val_loss: 0.0775 - val_accuracy: 0.9802
[-0.05253947 -0.00531845 -0.04093379 ...  0.3083167  -0.38094267
 -0.20853265]
Sparsity at: 0.028493613824192337
Epoch 39/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 0.0700 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.31099388 -0.3858391
 -0.21607019]
Sparsity at: 0.028493613824192337
Epoch 40/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0711 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.320148   -0.38993904
 -0.21512288]
Sparsity at: 0.028493613824192337
Epoch 41/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8217e-04 - accuracy: 1.0000 - val_loss: 0.0677 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.32267442 -0.39154312
 -0.21741927]
Sparsity at: 0.028493613824192337
Epoch 42/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6307e-04 - accuracy: 1.0000 - val_loss: 0.0689 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.32570335 -0.3944111
 -0.2203474 ]
Sparsity at: 0.028493613824192337
Epoch 43/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7469e-04 - accuracy: 1.0000 - val_loss: 0.0692 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.32962465 -0.39551395
 -0.22104532]
Sparsity at: 0.028493613824192337
Epoch 44/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1594e-04 - accuracy: 1.0000 - val_loss: 0.0688 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.3319438  -0.39723575
 -0.22241756]
Sparsity at: 0.028493613824192337
Epoch 45/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7908e-04 - accuracy: 1.0000 - val_loss: 0.0694 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.3349972  -0.39817977
 -0.2226066 ]
Sparsity at: 0.028493613824192337
Epoch 46/500
235/235 [==============================] - 3s 11ms/step - loss: 1.5109e-04 - accuracy: 1.0000 - val_loss: 0.0699 - val_accuracy: 0.9849
[-0.05253947 -0.00531845 -0.04093379 ...  0.3373263  -0.39949596
 -0.22429776]
Sparsity at: 0.028493613824192337
Epoch 47/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5256e-04 - accuracy: 1.0000 - val_loss: 0.0705 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.3399573  -0.40099177
 -0.2258579 ]
Sparsity at: 0.028493613824192337
Epoch 48/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2097e-04 - accuracy: 1.0000 - val_loss: 0.0710 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.3425105  -0.40225604
 -0.2273976 ]
Sparsity at: 0.028493613824192337
Epoch 49/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0066e-04 - accuracy: 1.0000 - val_loss: 0.0723 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.34453195 -0.40333092
 -0.22785884]
Sparsity at: 0.028493613824192337
Epoch 50/500
235/235 [==============================] - 3s 13ms/step - loss: 9.2262e-05 - accuracy: 1.0000 - val_loss: 0.0730 - val_accuracy: 0.9846
[-0.05253947 -0.00531845 -0.04093379 ...  0.34796718 -0.4040528
 -0.22923976]
Sparsity at: 0.028493613824192337
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.07339460449208701
Thresholhold -0.05253946781158447
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.08953173582430018
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.2962369120395607
Thresholhold -0.0036686165258288383
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 130s 11ms/step - loss: 7.6591e-05 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9848
[-0.05253947 -0.00531845 -0.04093379 ...  0.34990335 -0.40635416
 -0.23016603]
Sparsity at: 0.028493613824192337
Epoch 52/500
235/235 [==============================] - 3s 12ms/step - loss: 0.0133 - accuracy: 0.9958 - val_loss: 0.1837 - val_accuracy: 0.9603
[-0.05253947 -0.00531845 -0.04093379 ...  0.33878976 -0.4117443
 -0.22430009]
Sparsity at: 0.028493613824192337
Epoch 53/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0244 - accuracy: 0.9919 - val_loss: 0.0917 - val_accuracy: 0.9800
[-0.05253947 -0.00531845 -0.04093379 ...  0.3338583  -0.4257702
 -0.22917296]
Sparsity at: 0.028493613824192337
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0066 - accuracy: 0.9981 - val_loss: 0.0814 - val_accuracy: 0.9807
[-0.05253947 -0.00531845 -0.04093379 ...  0.33153558 -0.427032
 -0.22228569]
Sparsity at: 0.028493613824192337
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.0802 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.33592713 -0.4222424
 -0.22849429]
Sparsity at: 0.028493613824192337
Epoch 56/500
235/235 [==============================] - 3s 13ms/step - loss: 9.5860e-04 - accuracy: 0.9999 - val_loss: 0.0754 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.34417802 -0.4266879
 -0.23742744]
Sparsity at: 0.028493613824192337
Epoch 57/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9887e-04 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.34488225 -0.43084544
 -0.23713864]
Sparsity at: 0.028493613824192337
Epoch 58/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4958e-04 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.34707198 -0.43370712
 -0.23955122]
Sparsity at: 0.028493613824192337
Epoch 59/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7272e-04 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.34847236 -0.43400165
 -0.24058868]
Sparsity at: 0.028493613824192337
Epoch 60/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6249e-04 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.34901622 -0.43694764
 -0.23942278]
Sparsity at: 0.028493613824192337
Epoch 61/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1185e-04 - accuracy: 1.0000 - val_loss: 0.0757 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.3502961  -0.43882793
 -0.23932454]
Sparsity at: 0.028493613824192337
Epoch 62/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5884e-04 - accuracy: 0.9999 - val_loss: 0.0821 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.35202223 -0.44278568
 -0.23950933]
Sparsity at: 0.028493613824192337
Epoch 63/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.1314 - val_accuracy: 0.9744
[-0.05253947 -0.00531845 -0.04093379 ...  0.3562509  -0.44537985
 -0.2400399 ]
Sparsity at: 0.028493613824192337
Epoch 64/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0080 - accuracy: 0.9974 - val_loss: 0.1355 - val_accuracy: 0.9724
[-0.05253947 -0.00531845 -0.04093379 ...  0.35321704 -0.47800648
 -0.23570296]
Sparsity at: 0.028493613824192337
Epoch 65/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0060 - accuracy: 0.9978 - val_loss: 0.0938 - val_accuracy: 0.9800
[-0.05253947 -0.00531845 -0.04093379 ...  0.36568773 -0.46837044
 -0.24137023]
Sparsity at: 0.028493613824192337
Epoch 66/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0842 - val_accuracy: 0.9823
[-0.05253947 -0.00531845 -0.04093379 ...  0.36792254 -0.48225638
 -0.254048  ]
Sparsity at: 0.028493613824192337
Epoch 67/500
235/235 [==============================] - 3s 13ms/step - loss: 8.7240e-04 - accuracy: 0.9998 - val_loss: 0.0840 - val_accuracy: 0.9818
[-0.05253947 -0.00531845 -0.04093379 ...  0.36985406 -0.4843077
 -0.25383377]
Sparsity at: 0.028493613824192337
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3026e-04 - accuracy: 0.9999 - val_loss: 0.0797 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.36635187 -0.4863404
 -0.2503565 ]
Sparsity at: 0.028493613824192337
Epoch 69/500
235/235 [==============================] - 3s 13ms/step - loss: 3.8161e-04 - accuracy: 0.9999 - val_loss: 0.0840 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.3678257  -0.48731622
 -0.25327405]
Sparsity at: 0.028493613824192337
Epoch 70/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8801e-04 - accuracy: 1.0000 - val_loss: 0.0789 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.37018576 -0.483412
 -0.25637156]
Sparsity at: 0.028493613824192337
Epoch 71/500
235/235 [==============================] - 3s 13ms/step - loss: 9.2597e-05 - accuracy: 1.0000 - val_loss: 0.0786 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.3706929  -0.48447233
 -0.25799137]
Sparsity at: 0.028493613824192337
Epoch 72/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0072e-04 - accuracy: 1.0000 - val_loss: 0.0794 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.37732875 -0.4858268
 -0.26530284]
Sparsity at: 0.028493613824192337
Epoch 73/500
235/235 [==============================] - 3s 13ms/step - loss: 6.7013e-05 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.3755851  -0.4867917
 -0.26711783]
Sparsity at: 0.028493613824192337
Epoch 74/500
235/235 [==============================] - 3s 13ms/step - loss: 6.3877e-05 - accuracy: 1.0000 - val_loss: 0.0792 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.37774694 -0.48799488
 -0.26740852]
Sparsity at: 0.028493613824192337
Epoch 75/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.1454 - val_accuracy: 0.9710
[-0.05253947 -0.00531845 -0.04093379 ...  0.3762785  -0.48707724
 -0.25615996]
Sparsity at: 0.028493613824192337
Epoch 76/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 0.1097 - val_accuracy: 0.9786
[-0.05253947 -0.00531845 -0.04093379 ...  0.37672153 -0.4982522
 -0.25450817]
Sparsity at: 0.028493613824192337
Epoch 77/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.0995 - val_accuracy: 0.9804
[-0.05253947 -0.00531845 -0.04093379 ...  0.36842412 -0.5089398
 -0.25765958]
Sparsity at: 0.028493613824192337
Epoch 78/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0929 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.36826342 -0.51967233
 -0.26251867]
Sparsity at: 0.028493613824192337
Epoch 79/500
235/235 [==============================] - 3s 13ms/step - loss: 2.8757e-04 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.37044266 -0.52538264
 -0.2655473 ]
Sparsity at: 0.028493613824192337
Epoch 80/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2037e-04 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.37214622 -0.52664334
 -0.26682445]
Sparsity at: 0.028493613824192337
Epoch 81/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0753e-04 - accuracy: 0.9999 - val_loss: 0.0895 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.3724655  -0.52648777
 -0.27156857]
Sparsity at: 0.028493613824192337
Epoch 82/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1669e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.37302312 -0.5267605
 -0.27142715]
Sparsity at: 0.028493613824192337
Epoch 83/500
235/235 [==============================] - 3s 13ms/step - loss: 7.5810e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.37299564 -0.5269647
 -0.27208534]
Sparsity at: 0.028493613824192337
Epoch 84/500
235/235 [==============================] - 3s 13ms/step - loss: 5.5883e-05 - accuracy: 1.0000 - val_loss: 0.0886 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.3739698  -0.5273404
 -0.2720937 ]
Sparsity at: 0.028493613824192337
Epoch 85/500
235/235 [==============================] - 3s 13ms/step - loss: 5.4062e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9830ss: 5.1
[-0.05253947 -0.00531845 -0.04093379 ...  0.37481597 -0.52749056
 -0.27357888]
Sparsity at: 0.028493613824192337
Epoch 86/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9425e-05 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.3755159  -0.5271866
 -0.27458513]
Sparsity at: 0.028493613824192337
Epoch 87/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4203e-04 - accuracy: 0.9999 - val_loss: 0.0950 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.37696552 -0.52789056
 -0.27556142]
Sparsity at: 0.028493613824192337
Epoch 88/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.1444 - val_accuracy: 0.9714
[-0.05253947 -0.00531845 -0.04093379 ...  0.38525414 -0.52045673
 -0.28974032]
Sparsity at: 0.028493613824192337
Epoch 89/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0081 - accuracy: 0.9976 - val_loss: 0.0925 - val_accuracy: 0.9810
[-0.05253947 -0.00531845 -0.04093379 ...  0.3900753  -0.5366335
 -0.302805  ]
Sparsity at: 0.028493613824192337
Epoch 90/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0899 - val_accuracy: 0.9812
[-0.05253947 -0.00531845 -0.04093379 ...  0.38603365 -0.54318345
 -0.30587658]
Sparsity at: 0.028493613824192337
Epoch 91/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9413e-04 - accuracy: 0.9999 - val_loss: 0.0847 - val_accuracy: 0.9815
[-0.05253947 -0.00531845 -0.04093379 ...  0.38272133 -0.5479905
 -0.30526683]
Sparsity at: 0.028493613824192337
Epoch 92/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8766e-04 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9819
[-0.05253947 -0.00531845 -0.04093379 ...  0.38335004 -0.54550236
 -0.30562896]
Sparsity at: 0.028493613824192337
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1267e-04 - accuracy: 1.0000 - val_loss: 0.0834 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.38616383 -0.5450477
 -0.3061652 ]
Sparsity at: 0.028493613824192337
Epoch 94/500
235/235 [==============================] - 3s 13ms/step - loss: 8.8604e-05 - accuracy: 1.0000 - val_loss: 0.0833 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.38754    -0.54712737
 -0.30576062]
Sparsity at: 0.028493613824192337
Epoch 95/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1384e-04 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9823
[-0.05253947 -0.00531845 -0.04093379 ...  0.39706928 -0.54771453
 -0.31391507]
Sparsity at: 0.028493613824192337
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1328e-04 - accuracy: 1.0000 - val_loss: 0.0886 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.38748395 -0.5468919
 -0.3011827 ]
Sparsity at: 0.028493613824192337
Epoch 97/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2541e-04 - accuracy: 0.9999 - val_loss: 0.0896 - val_accuracy: 0.9816
[-0.05253947 -0.00531845 -0.04093379 ...  0.38861644 -0.5503864
 -0.31004646]
Sparsity at: 0.028493613824192337
Epoch 98/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1136 - val_accuracy: 0.9794
[-0.05253947 -0.00531845 -0.04093379 ...  0.3760282  -0.5527466
 -0.31087378]
Sparsity at: 0.028493613824192337
Epoch 99/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0074 - accuracy: 0.9977 - val_loss: 0.1134 - val_accuracy: 0.9788
[-0.05253947 -0.00531845 -0.04093379 ...  0.37491187 -0.5411652
 -0.30185553]
Sparsity at: 0.028493613824192337
Epoch 100/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0032 - accuracy: 0.9989 - val_loss: 0.0894 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.3793986  -0.5447194
 -0.30860624]
Sparsity at: 0.028493613824192337
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.14127264671771833
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.1556331708409715
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.3954344464909063
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 215s 12ms/step - loss: 9.8961e-04 - accuracy: 0.9998 - val_loss: 0.0901 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.38145888 -0.55757713
 -0.31393695]
Sparsity at: 0.028493613824192337
Epoch 102/500
235/235 [==============================] - 3s 12ms/step - loss: 6.1206e-04 - accuracy: 0.9999 - val_loss: 0.0959 - val_accuracy: 0.9826
[-0.05253947 -0.00531845 -0.04093379 ...  0.3682108  -0.56251776
 -0.30102545]
Sparsity at: 0.028493613824192337
Epoch 103/500
235/235 [==============================] - 3s 13ms/step - loss: 4.3033e-04 - accuracy: 0.9999 - val_loss: 0.0901 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.37462536 -0.5611727
 -0.30626127]
Sparsity at: 0.028493613824192337
Epoch 104/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9533e-04 - accuracy: 0.9999 - val_loss: 0.0914 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.3678348  -0.56070715
 -0.3062491 ]
Sparsity at: 0.028493613824192337
Epoch 105/500
235/235 [==============================] - 3s 13ms/step - loss: 7.3941e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.3697938  -0.5610731
 -0.30813462]
Sparsity at: 0.028493613824192337
Epoch 106/500
235/235 [==============================] - 3s 13ms/step - loss: 5.4402e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.37051615 -0.56158155
 -0.30885583]
Sparsity at: 0.028493613824192337
Epoch 107/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9495e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.3721818  -0.5617873
 -0.30927542]
Sparsity at: 0.028493613824192337
Epoch 108/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4306e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.373222   -0.562404
 -0.30962446]
Sparsity at: 0.028493613824192337
Epoch 109/500
235/235 [==============================] - 3s 13ms/step - loss: 5.2007e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.37452364 -0.5625685
 -0.30957958]
Sparsity at: 0.028493613824192337
Epoch 110/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1390e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.37525836 -0.56317014
 -0.31019965]
Sparsity at: 0.028493613824192337
Epoch 111/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4900e-05 - accuracy: 1.0000 - val_loss: 0.0903 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.37630805 -0.5638951
 -0.31110716]
Sparsity at: 0.028493613824192337
Epoch 112/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7734e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.377371   -0.5643369
 -0.30994532]
Sparsity at: 0.028493613824192337
Epoch 113/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2143e-05 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.378138   -0.56666094
 -0.30996656]
Sparsity at: 0.028493613824192337
Epoch 114/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8295e-05 - accuracy: 1.0000 - val_loss: 0.0907 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.37841523 -0.5664082
 -0.31058016]
Sparsity at: 0.028493613824192337
Epoch 115/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1457e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9816
[-0.05253947 -0.00531845 -0.04093379 ...  0.37838596 -0.5656528
 -0.33028063]
Sparsity at: 0.028493613824192337
Epoch 116/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0138 - accuracy: 0.9960 - val_loss: 0.1399 - val_accuracy: 0.9761
[-0.05253947 -0.00531845 -0.04093379 ...  0.33657083 -0.5955993
 -0.30402714]
Sparsity at: 0.028493613824192337
Epoch 117/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0047 - accuracy: 0.9984 - val_loss: 0.1023 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.33952615 -0.57665896
 -0.30474767]
Sparsity at: 0.028493613824192337
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1023 - val_accuracy: 0.9814
[-0.05253947 -0.00531845 -0.04093379 ...  0.3407409  -0.57557905
 -0.30046615]
Sparsity at: 0.028493613824192337
Epoch 119/500
235/235 [==============================] - 4s 16ms/step - loss: 4.0144e-04 - accuracy: 0.9999 - val_loss: 0.0945 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.33259517 -0.57551533
 -0.30331314]
Sparsity at: 0.028493613824192337
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8018e-04 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9825
[-0.05253947 -0.00531845 -0.04093379 ...  0.33931792 -0.5758037
 -0.30653715]
Sparsity at: 0.028493613824192337
Epoch 121/500
235/235 [==============================] - 3s 13ms/step - loss: 9.7532e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.34146696 -0.57623804
 -0.30718246]
Sparsity at: 0.028493613824192337
Epoch 122/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9860e-04 - accuracy: 0.9999 - val_loss: 0.0983 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.34169415 -0.5773926
 -0.30809715]
Sparsity at: 0.028493613824192337
Epoch 123/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9356e-04 - accuracy: 0.9999 - val_loss: 0.0984 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.34394762 -0.57964325
 -0.30821267]
Sparsity at: 0.028493613824192337
Epoch 124/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8900e-04 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.34731606 -0.5828778
 -0.30701864]
Sparsity at: 0.028493613824192337
Epoch 125/500
235/235 [==============================] - 3s 13ms/step - loss: 7.0822e-05 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.3477226  -0.5828083
 -0.30877218]
Sparsity at: 0.028493613824192337
Epoch 126/500
235/235 [==============================] - 3s 13ms/step - loss: 4.0984e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.34829974 -0.5838559
 -0.3106947 ]
Sparsity at: 0.028493613824192337
Epoch 127/500
235/235 [==============================] - 3s 13ms/step - loss: 9.6074e-05 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.34882772 -0.58108807
 -0.31183174]
Sparsity at: 0.028493613824192337
Epoch 128/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2032e-04 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.34930018 -0.5809999
 -0.31287077]
Sparsity at: 0.028493613824192337
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9414e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.35030168 -0.5804842
 -0.31518677]
Sparsity at: 0.028493613824192337
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7100e-05 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.35123676 -0.5819956
 -0.31653532]
Sparsity at: 0.028493613824192337
Epoch 131/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5069e-05 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.35223976 -0.5799607
 -0.3177871 ]
Sparsity at: 0.028493613824192337
Epoch 132/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2624e-05 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.35430032 -0.57967615
 -0.32100445]
Sparsity at: 0.028493613824192337
Epoch 133/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2510e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.35552028 -0.58010876
 -0.32347208]
Sparsity at: 0.028493613824192337
Epoch 134/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6416e-05 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.3560678  -0.58122295
 -0.3238894 ]
Sparsity at: 0.028493613824192337
Epoch 135/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5135e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.35677943 -0.5820179
 -0.32506236]
Sparsity at: 0.028493613824192337
Epoch 136/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1529e-05 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.35833186 -0.5833351
 -0.32594395]
Sparsity at: 0.028493613824192337
Epoch 137/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1949e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.3582305  -0.58338475
 -0.32669228]
Sparsity at: 0.028493613824192337
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 9.1549e-06 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.3592685  -0.5834057
 -0.32820123]
Sparsity at: 0.028493613824192337
Epoch 139/500
235/235 [==============================] - 3s 13ms/step - loss: 8.9147e-06 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.36024037 -0.5839545
 -0.32955852]
Sparsity at: 0.028493613824192337
Epoch 140/500
235/235 [==============================] - 3s 13ms/step - loss: 7.0990e-06 - accuracy: 1.0000 - val_loss: 0.0958 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.36083782 -0.5843364
 -0.3306772 ]
Sparsity at: 0.028493613824192337
Epoch 141/500
235/235 [==============================] - 3s 13ms/step - loss: 7.4429e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.36180037 -0.5853038
 -0.33166435]
Sparsity at: 0.028493613824192337
Epoch 142/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0061 - accuracy: 0.9984 - val_loss: 0.2037 - val_accuracy: 0.9679
[-0.05253947 -0.00531845 -0.04093379 ...  0.3520364  -0.59700835
 -0.34609294]
Sparsity at: 0.028493613824192337
Epoch 143/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0135 - accuracy: 0.9958 - val_loss: 0.1093 - val_accuracy: 0.9784
[-0.05253947 -0.00531845 -0.04093379 ...  0.35828766 -0.59009224
 -0.32892433]
Sparsity at: 0.028493613824192337
Epoch 144/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.1097 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.35657725 -0.59260356
 -0.3127037 ]
Sparsity at: 0.028493613824192337
Epoch 145/500
235/235 [==============================] - 3s 13ms/step - loss: 4.6933e-04 - accuracy: 0.9999 - val_loss: 0.1035 - val_accuracy: 0.9825
[-0.05253947 -0.00531845 -0.04093379 ...  0.35459682 -0.59434235
 -0.32001728]
Sparsity at: 0.028493613824192337
Epoch 146/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7000e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9826
[-0.05253947 -0.00531845 -0.04093379 ...  0.3553557  -0.59662634
 -0.3226922 ]
Sparsity at: 0.028493613824192337
Epoch 147/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1891e-04 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.35744587 -0.59439105
 -0.3254116 ]
Sparsity at: 0.028493613824192337
Epoch 148/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1000e-04 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.35950604 -0.59500223
 -0.32667148]
Sparsity at: 0.028493613824192337
Epoch 149/500
235/235 [==============================] - 3s 13ms/step - loss: 6.7021e-05 - accuracy: 1.0000 - val_loss: 0.1009 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.359311   -0.59648633
 -0.32731012]
Sparsity at: 0.028493613824192337
Epoch 150/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3437e-05 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.36032385 -0.5967678
 -0.32769474]
Sparsity at: 0.028493613824192337
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.2085662078037709
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.22348551991905907
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.47685135402196366
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 219s 13ms/step - loss: 6.9098e-05 - accuracy: 1.0000 - val_loss: 0.1017 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.36178115 -0.5966983
 -0.3282972 ]
Sparsity at: 0.028493613824192337
Epoch 152/500
235/235 [==============================] - 4s 15ms/step - loss: 4.8256e-05 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.36259553 -0.5968129
 -0.32861254]
Sparsity at: 0.028493613824192337
Epoch 153/500
235/235 [==============================] - 4s 16ms/step - loss: 3.5419e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.3629191  -0.5959407
 -0.32891646]
Sparsity at: 0.028493613824192337
Epoch 154/500
235/235 [==============================] - 3s 15ms/step - loss: 2.9828e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.3638738  -0.595998
 -0.3300056 ]
Sparsity at: 0.028493613824192337
Epoch 155/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0053e-05 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.36476353 -0.5959386
 -0.3305032 ]
Sparsity at: 0.028493613824192337
Epoch 156/500
235/235 [==============================] - 4s 15ms/step - loss: 2.4078e-05 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.3657621  -0.5959385
 -0.33178785]
Sparsity at: 0.028493613824192337
Epoch 157/500
235/235 [==============================] - 4s 16ms/step - loss: 6.8435e-04 - accuracy: 0.9999 - val_loss: 0.1129 - val_accuracy: 0.9821
[-0.05253947 -0.00531845 -0.04093379 ...  0.3668842  -0.6250114
 -0.33427578]
Sparsity at: 0.028493613824192337
Epoch 158/500
235/235 [==============================] - 4s 17ms/step - loss: 0.0085 - accuracy: 0.9971 - val_loss: 0.1241 - val_accuracy: 0.9798
[-0.05253947 -0.00531845 -0.04093379 ...  0.34749416 -0.60533786
 -0.34225455]
Sparsity at: 0.028493613824192337
Epoch 159/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0040 - accuracy: 0.9985 - val_loss: 0.0973 - val_accuracy: 0.9820
[-0.05253947 -0.00531845 -0.04093379 ...  0.3301137  -0.6007582
 -0.34236142]
Sparsity at: 0.028493613824192337
Epoch 160/500
235/235 [==============================] - 4s 17ms/step - loss: 6.3565e-04 - accuracy: 0.9998 - val_loss: 0.0962 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.3334383  -0.59871596
 -0.34255165]
Sparsity at: 0.028493613824192337
Epoch 161/500
235/235 [==============================] - 4s 15ms/step - loss: 2.7321e-04 - accuracy: 0.9999 - val_loss: 0.0933 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.3264051  -0.6047131
 -0.3410839 ]
Sparsity at: 0.028493613824192337
Epoch 162/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0611e-04 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.3279162  -0.60446775
 -0.3414597 ]
Sparsity at: 0.028493613824192337
Epoch 163/500
235/235 [==============================] - 4s 16ms/step - loss: 6.7074e-05 - accuracy: 1.0000 - val_loss: 0.0925 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.32852978 -0.6045377
 -0.34183392]
Sparsity at: 0.028493613824192337
Epoch 164/500
235/235 [==============================] - 4s 15ms/step - loss: 5.3641e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.3280881  -0.60439444
 -0.3414738 ]
Sparsity at: 0.028493613824192337
Epoch 165/500
235/235 [==============================] - 4s 15ms/step - loss: 4.1014e-05 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.32887226 -0.6050874
 -0.3424552 ]
Sparsity at: 0.028493613824192337
Epoch 166/500
235/235 [==============================] - 4s 15ms/step - loss: 3.7888e-05 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.32990924 -0.60555184
 -0.34330603]
Sparsity at: 0.028493613824192337
Epoch 167/500
235/235 [==============================] - 4s 15ms/step - loss: 3.1752e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.33029842 -0.60703707
 -0.34298778]
Sparsity at: 0.028493613824192337
Epoch 168/500
235/235 [==============================] - 4s 15ms/step - loss: 2.7497e-05 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.330209   -0.60737646
 -0.34394518]
Sparsity at: 0.028493613824192337
Epoch 169/500
235/235 [==============================] - 4s 15ms/step - loss: 3.4915e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.33107764 -0.60687226
 -0.34573448]
Sparsity at: 0.028493613824192337
Epoch 170/500
235/235 [==============================] - 4s 15ms/step - loss: 2.4893e-05 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.3321329  -0.6072875
 -0.34455147]
Sparsity at: 0.028493613824192337
Epoch 171/500
235/235 [==============================] - 4s 15ms/step - loss: 2.7950e-04 - accuracy: 0.9999 - val_loss: 0.1059 - val_accuracy: 0.9814
[-0.05253947 -0.00531845 -0.04093379 ...  0.33860028 -0.59648293
 -0.34592125]
Sparsity at: 0.028493613824192337
Epoch 172/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.1260 - val_accuracy: 0.9771
[-0.05253947 -0.00531845 -0.04093379 ...  0.32209408 -0.60915136
 -0.34786475]
Sparsity at: 0.028493613824192337
Epoch 173/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0052 - accuracy: 0.9982 - val_loss: 0.1143 - val_accuracy: 0.9793
[-0.05253947 -0.00531845 -0.04093379 ...  0.33026496 -0.5899857
 -0.34824738]
Sparsity at: 0.028493613824192337
Epoch 174/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.1050 - val_accuracy: 0.9817
[-0.05253947 -0.00531845 -0.04093379 ...  0.31998917 -0.60240155
 -0.33964372]
Sparsity at: 0.028493613824192337
Epoch 175/500
235/235 [==============================] - 4s 15ms/step - loss: 3.6161e-04 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9826
[-0.05253947 -0.00531845 -0.04093379 ...  0.3218198  -0.600925
 -0.3412455 ]
Sparsity at: 0.028493613824192337
Epoch 176/500
235/235 [==============================] - 4s 15ms/step - loss: 9.0039e-05 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.31887797 -0.6008649
 -0.34359157]
Sparsity at: 0.028493613824192337
Epoch 177/500
235/235 [==============================] - 4s 15ms/step - loss: 8.5917e-05 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.32069352 -0.6011213
 -0.34463534]
Sparsity at: 0.028493613824192337
Epoch 178/500
235/235 [==============================] - 4s 15ms/step - loss: 1.8168e-04 - accuracy: 0.9999 - val_loss: 0.0983 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.3215081  -0.59526014
 -0.34602165]
Sparsity at: 0.028493613824192337
Epoch 179/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0558e-04 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.323693   -0.60119265
 -0.34325206]
Sparsity at: 0.028493613824192337
Epoch 180/500
235/235 [==============================] - 4s 16ms/step - loss: 4.0138e-05 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.32525718 -0.6031743
 -0.3434317 ]
Sparsity at: 0.028493613824192337
Epoch 181/500
235/235 [==============================] - 4s 16ms/step - loss: 2.9912e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.3260202  -0.601369
 -0.34387928]
Sparsity at: 0.028493613824192337
Epoch 182/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4209e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.32714602 -0.6014944
 -0.34452263]
Sparsity at: 0.028493613824192337
Epoch 183/500
235/235 [==============================] - 3s 15ms/step - loss: 3.1821e-05 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.32824525 -0.59984046
 -0.3449477 ]
Sparsity at: 0.028493613824192337
Epoch 184/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1044e-05 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.32901123 -0.6020376
 -0.34595457]
Sparsity at: 0.028493613824192337
Epoch 185/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6034e-05 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.3291937  -0.6026963
 -0.3457884 ]
Sparsity at: 0.028493613824192337
Epoch 186/500
235/235 [==============================] - 3s 15ms/step - loss: 1.9241e-05 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.329248   -0.60250777
 -0.3445007 ]
Sparsity at: 0.028493613824192337
Epoch 187/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5084e-05 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.33025324 -0.6019693
 -0.34459484]
Sparsity at: 0.028493613824192337
Epoch 188/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3761e-05 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.3327615  -0.60342944
 -0.3457315 ]
Sparsity at: 0.028493613824192337
Epoch 189/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1049e-05 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.3340987  -0.60358167
 -0.34578973]
Sparsity at: 0.028493613824192337
Epoch 190/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0575e-05 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.33472753 -0.60347146
 -0.34674886]
Sparsity at: 0.028493613824192337
Epoch 191/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0338e-05 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.3360264  -0.60477316
 -0.34731847]
Sparsity at: 0.028493613824192337
Epoch 192/500
235/235 [==============================] - 3s 15ms/step - loss: 8.3158e-06 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.33651447 -0.6046985
 -0.34796482]
Sparsity at: 0.028493613824192337
Epoch 193/500
235/235 [==============================] - 3s 15ms/step - loss: 7.4904e-06 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.33740076 -0.60759985
 -0.3480437 ]
Sparsity at: 0.028493613824192337
Epoch 194/500
235/235 [==============================] - 3s 15ms/step - loss: 7.6834e-06 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.33899307 -0.60821646
 -0.3485056 ]
Sparsity at: 0.028493613824192337
Epoch 195/500
235/235 [==============================] - 3s 15ms/step - loss: 6.8347e-06 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.34085461 -0.6092299
 -0.35041448]
Sparsity at: 0.028493613824192337
Epoch 196/500
235/235 [==============================] - 3s 15ms/step - loss: 6.9707e-06 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.342357   -0.60969734
 -0.3505012 ]
Sparsity at: 0.028493613824192337
Epoch 197/500
235/235 [==============================] - 3s 15ms/step - loss: 2.5213e-04 - accuracy: 0.9999 - val_loss: 0.1386 - val_accuracy: 0.9792
[-0.05253947 -0.00531845 -0.04093379 ...  0.3613027  -0.61012095
 -0.34253436]
Sparsity at: 0.028493613824192337
Epoch 198/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0118 - accuracy: 0.9966 - val_loss: 0.1245 - val_accuracy: 0.9796
[-0.05253947 -0.00531845 -0.04093379 ...  0.31342274 -0.6205524
 -0.31672603]
Sparsity at: 0.028493613824192337
Epoch 199/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0034 - accuracy: 0.9987 - val_loss: 0.1139 - val_accuracy: 0.9809
[-0.05253947 -0.00531845 -0.04093379 ...  0.31106874 -0.626597
 -0.3182148 ]
Sparsity at: 0.028493613824192337
Epoch 200/500
235/235 [==============================] - 4s 15ms/step - loss: 8.4787e-04 - accuracy: 0.9997 - val_loss: 0.1088 - val_accuracy: 0.9814
[-0.05253947 -0.00531845 -0.04093379 ...  0.31580237 -0.6350527
 -0.32656768]
Sparsity at: 0.028493613824192337
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.29162271014998
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.30315527820678767
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.5731419925485284
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 208s 13ms/step - loss: 3.7498e-04 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9814
[-0.05253947 -0.00531845 -0.04093379 ...  0.3282217  -0.63859016
 -0.32564944]
Sparsity at: 0.028493613824192337
Epoch 202/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0873e-04 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.32888654 -0.6358007
 -0.32503048]
Sparsity at: 0.028493613824192337
Epoch 203/500
235/235 [==============================] - 3s 15ms/step - loss: 4.6290e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9825
[-0.05253947 -0.00531845 -0.04093379 ...  0.32969365 -0.63650316
 -0.32515275]
Sparsity at: 0.028493613824192337
Epoch 204/500
235/235 [==============================] - 3s 15ms/step - loss: 4.0478e-05 - accuracy: 1.0000 - val_loss: 0.1031 - val_accuracy: 0.9826
[-0.05253947 -0.00531845 -0.04093379 ...  0.33139792 -0.6365335
 -0.32655036]
Sparsity at: 0.028493613824192337
Epoch 205/500
235/235 [==============================] - 3s 15ms/step - loss: 3.6199e-05 - accuracy: 1.0000 - val_loss: 0.1027 - val_accuracy: 0.9825
[-0.05253947 -0.00531845 -0.04093379 ...  0.3318752  -0.63715
 -0.32727155]
Sparsity at: 0.028493613824192337
Epoch 206/500
235/235 [==============================] - 4s 15ms/step - loss: 3.1553e-05 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.33281848 -0.638031
 -0.32799023]
Sparsity at: 0.028493613824192337
Epoch 207/500
235/235 [==============================] - 4s 15ms/step - loss: 3.0115e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.33477437 -0.63777804
 -0.32738146]
Sparsity at: 0.028493613824192337
Epoch 208/500
235/235 [==============================] - 3s 15ms/step - loss: 8.0001e-05 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.33446297 -0.63103104
 -0.33161157]
Sparsity at: 0.028493613824192337
Epoch 209/500
235/235 [==============================] - 4s 15ms/step - loss: 6.5418e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.33621478 -0.63463354
 -0.33246684]
Sparsity at: 0.028493613824192337
Epoch 210/500
235/235 [==============================] - 3s 15ms/step - loss: 8.3912e-04 - accuracy: 0.9998 - val_loss: 0.1090 - val_accuracy: 0.9812
[-0.05253947 -0.00531845 -0.04093379 ...  0.3418023  -0.6365531
 -0.33621305]
Sparsity at: 0.028493613824192337
Epoch 211/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.1227 - val_accuracy: 0.9817
[-0.05253947 -0.00531845 -0.04093379 ...  0.35438195 -0.6426361
 -0.31660873]
Sparsity at: 0.028493613824192337
Epoch 212/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.1178 - val_accuracy: 0.9823
[-0.05253947 -0.00531845 -0.04093379 ...  0.35451314 -0.6412805
 -0.3111367 ]
Sparsity at: 0.028493613824192337
Epoch 213/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1075 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.35172042 -0.63379884
 -0.33461586]
Sparsity at: 0.028493613824192337
Epoch 214/500
235/235 [==============================] - 3s 15ms/step - loss: 6.2203e-04 - accuracy: 0.9998 - val_loss: 0.1047 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.35517102 -0.6275447
 -0.33985034]
Sparsity at: 0.028493613824192337
Epoch 215/500
235/235 [==============================] - 3s 15ms/step - loss: 1.4366e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.35241088 -0.63185316
 -0.34323204]
Sparsity at: 0.028493613824192337
Epoch 216/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0281e-04 - accuracy: 1.0000 - val_loss: 0.1001 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.35094044 -0.62979823
 -0.3399597 ]
Sparsity at: 0.028493613824192337
Epoch 217/500
235/235 [==============================] - 3s 15ms/step - loss: 5.7318e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.3518195  -0.63357794
 -0.33974054]
Sparsity at: 0.028493613824192337
Epoch 218/500
235/235 [==============================] - 3s 15ms/step - loss: 3.9641e-05 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.3519952  -0.6285311
 -0.34418476]
Sparsity at: 0.028493613824192337
Epoch 219/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0384e-05 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.35185662 -0.6344249
 -0.33983135]
Sparsity at: 0.028493613824192337
Epoch 220/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7531e-05 - accuracy: 1.0000 - val_loss: 0.1012 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.35170797 -0.63443977
 -0.33925202]
Sparsity at: 0.028493613824192337
Epoch 221/500
235/235 [==============================] - 3s 15ms/step - loss: 2.6342e-05 - accuracy: 1.0000 - val_loss: 0.1001 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.35202208 -0.63368285
 -0.3406762 ]
Sparsity at: 0.028493613824192337
Epoch 222/500
235/235 [==============================] - 3s 15ms/step - loss: 3.1716e-05 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.35220933 -0.63387966
 -0.3408207 ]
Sparsity at: 0.028493613824192337
Epoch 223/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5344e-05 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9849
[-0.05253947 -0.00531845 -0.04093379 ...  0.35372785 -0.6339604
 -0.3402826 ]
Sparsity at: 0.028493613824192337
Epoch 224/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3887e-05 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9846
[-0.05253947 -0.00531845 -0.04093379 ...  0.35705182 -0.63547164
 -0.33962247]
Sparsity at: 0.028493613824192337
Epoch 225/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1282e-05 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.3573348  -0.6364416
 -0.33961615]
Sparsity at: 0.028493613824192337
Epoch 226/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0023e-05 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9850
[-0.05253947 -0.00531845 -0.04093379 ...  0.3572184  -0.6374897
 -0.33866256]
Sparsity at: 0.028493613824192337
Epoch 227/500
235/235 [==============================] - 3s 15ms/step - loss: 7.7817e-06 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9851
[-0.05253947 -0.00531845 -0.04093379 ...  0.35717425 -0.63816357
 -0.33911625]
Sparsity at: 0.028493613824192337
Epoch 228/500
235/235 [==============================] - 4s 15ms/step - loss: 7.6519e-06 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9849
[-0.05253947 -0.00531845 -0.04093379 ...  0.35693383 -0.63809127
 -0.33912024]
Sparsity at: 0.028493613824192337
Epoch 229/500
235/235 [==============================] - 3s 15ms/step - loss: 8.0244e-06 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9849
[-0.05253947 -0.00531845 -0.04093379 ...  0.35729587 -0.6381897
 -0.33962682]
Sparsity at: 0.028493613824192337
Epoch 230/500
235/235 [==============================] - 3s 15ms/step - loss: 7.2539e-06 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9850
[-0.05253947 -0.00531845 -0.04093379 ...  0.357503   -0.63829434
 -0.33954144]
Sparsity at: 0.028493613824192337
Epoch 231/500
235/235 [==============================] - 4s 15ms/step - loss: 5.5227e-06 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9849
[-0.05253947 -0.00531845 -0.04093379 ...  0.358909   -0.6389968
 -0.3396948 ]
Sparsity at: 0.028493613824192337
Epoch 232/500
235/235 [==============================] - 3s 15ms/step - loss: 5.0536e-06 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9848
[-0.05253947 -0.00531845 -0.04093379 ...  0.3595098  -0.6404461
 -0.3397753 ]
Sparsity at: 0.028493613824192337
Epoch 233/500
235/235 [==============================] - 3s 15ms/step - loss: 4.5245e-06 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9848
[-0.05253947 -0.00531845 -0.04093379 ...  0.36035746 -0.64044523
 -0.33986604]
Sparsity at: 0.028493613824192337
Epoch 234/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0058 - accuracy: 0.9983 - val_loss: 0.1612 - val_accuracy: 0.9755
[-0.05253947 -0.00531845 -0.04093379 ...  0.32954872 -0.66707844
 -0.2799722 ]
Sparsity at: 0.028493613824192337
Epoch 235/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0064 - accuracy: 0.9981 - val_loss: 0.1232 - val_accuracy: 0.9811
[-0.05253947 -0.00531845 -0.04093379 ...  0.3408525  -0.68324816
 -0.28585318]
Sparsity at: 0.028493613824192337
Epoch 236/500
235/235 [==============================] - 3s 15ms/step - loss: 9.5861e-04 - accuracy: 0.9997 - val_loss: 0.1173 - val_accuracy: 0.9813
[-0.05253947 -0.00531845 -0.04093379 ...  0.33100525 -0.6813621
 -0.28572693]
Sparsity at: 0.028493613824192337
Epoch 237/500
235/235 [==============================] - 3s 15ms/step - loss: 3.6782e-04 - accuracy: 0.9999 - val_loss: 0.1101 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.33048964 -0.6813524
 -0.28304684]
Sparsity at: 0.028493613824192337
Epoch 238/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7246e-04 - accuracy: 0.9999 - val_loss: 0.1128 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.33528364 -0.68132913
 -0.28538585]
Sparsity at: 0.028493613824192337
Epoch 239/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2679e-04 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.33558017 -0.6827939
 -0.28762403]
Sparsity at: 0.028493613824192337
Epoch 240/500
235/235 [==============================] - 3s 15ms/step - loss: 7.7547e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.33385342 -0.6808557
 -0.28706715]
Sparsity at: 0.028493613824192337
Epoch 241/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1973e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.33053195 -0.6851457
 -0.297317  ]
Sparsity at: 0.028493613824192337
Epoch 242/500
235/235 [==============================] - 3s 15ms/step - loss: 4.5545e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.3328235  -0.68240345
 -0.29785013]
Sparsity at: 0.028493613824192337
Epoch 243/500
235/235 [==============================] - 3s 15ms/step - loss: 2.9556e-05 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.33259293 -0.68338627
 -0.29834065]
Sparsity at: 0.028493613824192337
Epoch 244/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4098e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.33774096 -0.68795055
 -0.29890636]
Sparsity at: 0.028493613824192337
Epoch 245/500
235/235 [==============================] - 3s 15ms/step - loss: 2.2414e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.338559   -0.6884915
 -0.30112162]
Sparsity at: 0.028493613824192337
Epoch 246/500
235/235 [==============================] - 3s 15ms/step - loss: 2.5437e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.34398097 -0.68851596
 -0.3012117 ]
Sparsity at: 0.028493613824192337
Epoch 247/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5777e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.34366265 -0.6878952
 -0.30024797]
Sparsity at: 0.028493613824192337
Epoch 248/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1671e-05 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.34403443 -0.6883992
 -0.30053008]
Sparsity at: 0.028493613824192337
Epoch 249/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5884e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.3445571  -0.6888152
 -0.2984268 ]
Sparsity at: 0.028493613824192337
Epoch 250/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1302e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.34471732 -0.68942577
 -0.29928938]
Sparsity at: 0.028493613824192337
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.3769573828169399
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.38761267765780616
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.6553642874088581
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 210s 12ms/step - loss: 9.6954e-06 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.34493896 -0.68995523
 -0.29846534]
Sparsity at: 0.028493613824192337
Epoch 252/500
235/235 [==============================] - 3s 15ms/step - loss: 8.7721e-06 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.3456156  -0.68958616
 -0.2995062 ]
Sparsity at: 0.028493613824192337
Epoch 253/500
235/235 [==============================] - 3s 15ms/step - loss: 7.8525e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.34663764 -0.6899241
 -0.30061305]
Sparsity at: 0.028493613824192337
Epoch 254/500
235/235 [==============================] - 3s 15ms/step - loss: 8.2935e-04 - accuracy: 0.9998 - val_loss: 0.1450 - val_accuracy: 0.9783
[-0.05253947 -0.00531845 -0.04093379 ...  0.3524665  -0.7076545
 -0.31729183]
Sparsity at: 0.028493613824192337
Epoch 255/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0063 - accuracy: 0.9981 - val_loss: 0.1372 - val_accuracy: 0.9791
[-0.05253947 -0.00531845 -0.04093379 ...  0.32999137 -0.7192124
 -0.31541118]
Sparsity at: 0.028493613824192337
Epoch 256/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1126 - val_accuracy: 0.9823
[-0.05253947 -0.00531845 -0.04093379 ...  0.338248   -0.71698594
 -0.3322038 ]
Sparsity at: 0.028493613824192337
Epoch 257/500
235/235 [==============================] - 3s 15ms/step - loss: 7.9587e-04 - accuracy: 0.9997 - val_loss: 0.1109 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.3495278  -0.71792465
 -0.3319064 ]
Sparsity at: 0.028493613824192337
Epoch 258/500
235/235 [==============================] - 3s 15ms/step - loss: 2.5921e-04 - accuracy: 1.0000 - val_loss: 0.1114 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.35124496 -0.71811706
 -0.3319571 ]
Sparsity at: 0.028493613824192337
Epoch 259/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1160e-04 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9826
[-0.05253947 -0.00531845 -0.04093379 ...  0.3535951  -0.7194339
 -0.3314303 ]
Sparsity at: 0.028493613824192337
Epoch 260/500
235/235 [==============================] - 4s 15ms/step - loss: 4.5688e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.3547755  -0.720462
 -0.33150408]
Sparsity at: 0.028493613824192337
Epoch 261/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7777e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.3551533  -0.7210222
 -0.33122313]
Sparsity at: 0.028493613824192337
Epoch 262/500
235/235 [==============================] - 4s 16ms/step - loss: 2.0926e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.35554045 -0.7212071
 -0.33121547]
Sparsity at: 0.028493613824192337
Epoch 263/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1409e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.35599744 -0.72198033
 -0.3305833 ]
Sparsity at: 0.028493613824192337
Epoch 264/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0071e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.35620958 -0.7265441
 -0.33056432]
Sparsity at: 0.028493613824192337
Epoch 265/500
235/235 [==============================] - 3s 15ms/step - loss: 2.0599e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.3562115  -0.72717243
 -0.32982796]
Sparsity at: 0.028493613824192337
Epoch 266/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6300e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.35635972 -0.7277881
 -0.32970777]
Sparsity at: 0.028493613824192337
Epoch 267/500
235/235 [==============================] - 4s 15ms/step - loss: 1.6851e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.35725048 -0.7285392
 -0.33032644]
Sparsity at: 0.028493613824192337
Epoch 268/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0784e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.35789302 -0.7288335
 -0.33073562]
Sparsity at: 0.028493613824192337
Epoch 269/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0321e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.35796896 -0.72834134
 -0.3320301 ]
Sparsity at: 0.028493613824192337
Epoch 270/500
235/235 [==============================] - 4s 15ms/step - loss: 9.1514e-06 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.3580983  -0.7282561
 -0.33198023]
Sparsity at: 0.028493613824192337
Epoch 271/500
235/235 [==============================] - 3s 15ms/step - loss: 4.9602e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.358535   -0.7285919
 -0.33325428]
Sparsity at: 0.028493613824192337
Epoch 272/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.1537 - val_accuracy: 0.9779
[-0.05253947 -0.00531845 -0.04093379 ...  0.35703626 -0.72296417
 -0.32113713]
Sparsity at: 0.028493613824192337
Epoch 273/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.1177 - val_accuracy: 0.9820
[-0.05253947 -0.00531845 -0.04093379 ...  0.36600822 -0.7376247
 -0.32277992]
Sparsity at: 0.028493613824192337
Epoch 274/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1157 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.36370164 -0.7394978
 -0.31989023]
Sparsity at: 0.028493613824192337
Epoch 275/500
235/235 [==============================] - 3s 15ms/step - loss: 2.0157e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.36874124 -0.74058014
 -0.31789494]
Sparsity at: 0.028493613824192337
Epoch 276/500
235/235 [==============================] - 4s 15ms/step - loss: 9.6139e-05 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.37745672 -0.7409098
 -0.32121834]
Sparsity at: 0.028493613824192337
Epoch 277/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0056e-04 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.38016918 -0.74551475
 -0.3231955 ]
Sparsity at: 0.028493613824192337
Epoch 278/500
235/235 [==============================] - 4s 15ms/step - loss: 6.2877e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.37907943 -0.7416529
 -0.3238429 ]
Sparsity at: 0.028493613824192337
Epoch 279/500
235/235 [==============================] - 4s 15ms/step - loss: 2.4420e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.37871495 -0.7408408
 -0.32500654]
Sparsity at: 0.028493613824192337
Epoch 280/500
235/235 [==============================] - 4s 15ms/step - loss: 2.3444e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.37938166 -0.74168515
 -0.32497957]
Sparsity at: 0.028493613824192337
Epoch 281/500
235/235 [==============================] - 4s 15ms/step - loss: 1.9083e-05 - accuracy: 1.0000 - val_loss: 0.1095 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.37948242 -0.741639
 -0.32723856]
Sparsity at: 0.028493613824192337
Epoch 282/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7909e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.37981892 -0.74137765
 -0.32777426]
Sparsity at: 0.028493613824192337
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2744e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.380701   -0.74145734
 -0.32804298]
Sparsity at: 0.028493613824192337
Epoch 284/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2951e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.3805701  -0.7415988
 -0.32718894]
Sparsity at: 0.028493613824192337
Epoch 285/500
235/235 [==============================] - 3s 15ms/step - loss: 9.2786e-06 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.38056248 -0.7415994
 -0.3279441 ]
Sparsity at: 0.028493613824192337
Epoch 286/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0351e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.38137338 -0.7418949
 -0.32993144]
Sparsity at: 0.028493613824192337
Epoch 287/500
235/235 [==============================] - 3s 15ms/step - loss: 7.3427e-06 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.38161573 -0.7420996
 -0.33042267]
Sparsity at: 0.028493613824192337
Epoch 288/500
235/235 [==============================] - 3s 15ms/step - loss: 9.4875e-06 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.3815148  -0.74457246
 -0.3309453 ]
Sparsity at: 0.028493613824192337
Epoch 289/500
235/235 [==============================] - 3s 15ms/step - loss: 9.7482e-06 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.3847198  -0.7448215
 -0.33227092]
Sparsity at: 0.028493613824192337
Epoch 290/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0053 - accuracy: 0.9985 - val_loss: 0.1532 - val_accuracy: 0.9769
[-0.05253947 -0.00531845 -0.04093379 ...  0.32146344 -0.67517334
 -0.32523924]
Sparsity at: 0.028493613824192337
Epoch 291/500
235/235 [==============================] - 4s 17ms/step - loss: 0.0040 - accuracy: 0.9987 - val_loss: 0.1166 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.33885777 -0.6704205
 -0.3550896 ]
Sparsity at: 0.028493613824192337
Epoch 292/500
235/235 [==============================] - 4s 17ms/step - loss: 5.2106e-04 - accuracy: 0.9998 - val_loss: 0.1155 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.3407158  -0.6798178
 -0.34401575]
Sparsity at: 0.028493613824192337
Epoch 293/500
235/235 [==============================] - 4s 15ms/step - loss: 1.3718e-04 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.34371942 -0.6820976
 -0.34668252]
Sparsity at: 0.028493613824192337
Epoch 294/500
235/235 [==============================] - 3s 15ms/step - loss: 2.0842e-04 - accuracy: 0.9999 - val_loss: 0.1160 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.338106   -0.683269
 -0.34831288]
Sparsity at: 0.028493613824192337
Epoch 295/500
235/235 [==============================] - 3s 15ms/step - loss: 3.1565e-04 - accuracy: 0.9999 - val_loss: 0.1214 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.33724385 -0.6833189
 -0.3485224 ]
Sparsity at: 0.028493613824192337
Epoch 296/500
235/235 [==============================] - 4s 15ms/step - loss: 6.0302e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.33977044 -0.6834201
 -0.3491442 ]
Sparsity at: 0.028493613824192337
Epoch 297/500
235/235 [==============================] - 3s 15ms/step - loss: 2.3534e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.33964905 -0.6840145
 -0.34916776]
Sparsity at: 0.028493613824192337
Epoch 298/500
235/235 [==============================] - 3s 15ms/step - loss: 2.3774e-05 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.33950672 -0.6849391
 -0.35041755]
Sparsity at: 0.028493613824192337
Epoch 299/500
235/235 [==============================] - 3s 15ms/step - loss: 1.8210e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.3398405  -0.6844752
 -0.3508929 ]
Sparsity at: 0.028493613824192337
Epoch 300/500
235/235 [==============================] - 3s 15ms/step - loss: 1.8564e-05 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.34099564 -0.6845815
 -0.35192123]
Sparsity at: 0.028493613824192337
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.48870776962442264
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.49260266792279594
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.724636740379438
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 193s 12ms/step - loss: 9.1279e-05 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.34308377 -0.68833977
 -0.35071516]
Sparsity at: 0.028493613824192337
Epoch 302/500
235/235 [==============================] - 3s 15ms/step - loss: 7.0725e-04 - accuracy: 0.9999 - val_loss: 0.1390 - val_accuracy: 0.9813
[-0.05253947 -0.00531845 -0.04093379 ...  0.3389869  -0.68870175
 -0.3536509 ]
Sparsity at: 0.028493613824192337
Epoch 303/500
235/235 [==============================] - 3s 15ms/step - loss: 4.8661e-04 - accuracy: 0.9999 - val_loss: 0.1235 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.34880802 -0.69382083
 -0.3520747 ]
Sparsity at: 0.028493613824192337
Epoch 304/500
235/235 [==============================] - 3s 15ms/step - loss: 6.6366e-04 - accuracy: 0.9998 - val_loss: 0.1358 - val_accuracy: 0.9820
[-0.05253947 -0.00531845 -0.04093379 ...  0.3279773  -0.7143462
 -0.34004807]
Sparsity at: 0.028493613824192337
Epoch 305/500
235/235 [==============================] - 4s 15ms/step - loss: 6.9825e-04 - accuracy: 0.9998 - val_loss: 0.1237 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.32125637 -0.6942915
 -0.3405359 ]
Sparsity at: 0.028493613824192337
Epoch 306/500
235/235 [==============================] - 3s 15ms/step - loss: 4.3108e-04 - accuracy: 0.9998 - val_loss: 0.1150 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.33510882 -0.6942015
 -0.3290351 ]
Sparsity at: 0.028493613824192337
Epoch 307/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4571e-04 - accuracy: 0.9999 - val_loss: 0.1173 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.34337336 -0.695978
 -0.32965708]
Sparsity at: 0.028493613824192337
Epoch 308/500
235/235 [==============================] - 3s 15ms/step - loss: 6.0004e-05 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.33742163 -0.69844997
 -0.31403548]
Sparsity at: 0.028493613824192337
Epoch 309/500
235/235 [==============================] - 3s 15ms/step - loss: 4.7362e-05 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.33906758 -0.6991668
 -0.31716034]
Sparsity at: 0.028493613824192337
Epoch 310/500
235/235 [==============================] - 4s 16ms/step - loss: 5.5784e-05 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.34023178 -0.6957146
 -0.3228236 ]
Sparsity at: 0.028493613824192337
Epoch 311/500
235/235 [==============================] - 4s 15ms/step - loss: 3.5056e-04 - accuracy: 0.9998 - val_loss: 0.1353 - val_accuracy: 0.9814
[-0.05253947 -0.00531845 -0.04093379 ...  0.34032702 -0.6688547
 -0.32275695]
Sparsity at: 0.028493613824192337
Epoch 312/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9995 - val_loss: 0.1312 - val_accuracy: 0.9813
[-0.05253947 -0.00531845 -0.04093379 ...  0.34604037 -0.704933
 -0.32348305]
Sparsity at: 0.028493613824192337
Epoch 313/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1195 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.34613296 -0.70734346
 -0.3392192 ]
Sparsity at: 0.028493613824192337
Epoch 314/500
235/235 [==============================] - 3s 15ms/step - loss: 5.8326e-04 - accuracy: 0.9998 - val_loss: 0.1265 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.33583698 -0.7027653
 -0.3527485 ]
Sparsity at: 0.028493613824192337
Epoch 315/500
235/235 [==============================] - 3s 15ms/step - loss: 3.3301e-04 - accuracy: 0.9999 - val_loss: 0.1143 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.34532002 -0.6986958
 -0.3542171 ]
Sparsity at: 0.028493613824192337
Epoch 316/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3004e-04 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.34780648 -0.7017104
 -0.3535276 ]
Sparsity at: 0.028493613824192337
Epoch 317/500
235/235 [==============================] - 3s 15ms/step - loss: 5.8694e-05 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.34832108 -0.7029398
 -0.35806233]
Sparsity at: 0.028493613824192337
Epoch 318/500
235/235 [==============================] - 4s 15ms/step - loss: 2.8451e-04 - accuracy: 1.0000 - val_loss: 0.1139 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.3497257  -0.70175576
 -0.36120996]
Sparsity at: 0.028493613824192337
Epoch 319/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1306 - val_accuracy: 0.9812
[-0.05253947 -0.00531845 -0.04093379 ...  0.35103017 -0.692668
 -0.36321804]
Sparsity at: 0.028493613824192337
Epoch 320/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1195 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.35971376 -0.7102315
 -0.3783859 ]
Sparsity at: 0.028493613824192337
Epoch 321/500
235/235 [==============================] - 4s 15ms/step - loss: 9.0621e-04 - accuracy: 0.9997 - val_loss: 0.1357 - val_accuracy: 0.9821
[-0.05253947 -0.00531845 -0.04093379 ...  0.35009074 -0.71499467
 -0.35678914]
Sparsity at: 0.028493613824192337
Epoch 322/500
235/235 [==============================] - 4s 16ms/step - loss: 7.5578e-04 - accuracy: 0.9998 - val_loss: 0.1204 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.35772544 -0.7179314
 -0.36076006]
Sparsity at: 0.028493613824192337
Epoch 323/500
235/235 [==============================] - 4s 15ms/step - loss: 1.6202e-04 - accuracy: 0.9999 - val_loss: 0.1212 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.35026088 -0.72350425
 -0.35462517]
Sparsity at: 0.028493613824192337
Epoch 324/500
235/235 [==============================] - 4s 15ms/step - loss: 3.4564e-05 - accuracy: 1.0000 - val_loss: 0.1212 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.35281816 -0.723027
 -0.354968  ]
Sparsity at: 0.028493613824192337
Epoch 325/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5037e-04 - accuracy: 0.9999 - val_loss: 0.1246 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.35527235 -0.7216929
 -0.3561836 ]
Sparsity at: 0.028493613824192337
Epoch 326/500
235/235 [==============================] - 4s 15ms/step - loss: 2.4162e-04 - accuracy: 0.9999 - val_loss: 0.1254 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.36181143 -0.72182786
 -0.35662398]
Sparsity at: 0.028493613824192337
Epoch 327/500
235/235 [==============================] - 4s 15ms/step - loss: 3.4500e-05 - accuracy: 1.0000 - val_loss: 0.1227 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.36059892 -0.720914
 -0.35696462]
Sparsity at: 0.028493613824192337
Epoch 328/500
235/235 [==============================] - 4s 15ms/step - loss: 5.8631e-05 - accuracy: 1.0000 - val_loss: 0.1199 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.36781707 -0.719358
 -0.3546518 ]
Sparsity at: 0.028493613824192337
Epoch 329/500
235/235 [==============================] - 3s 15ms/step - loss: 2.9531e-05 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.36835074 -0.71910983
 -0.3550411 ]
Sparsity at: 0.028493613824192337
Epoch 330/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1966e-05 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.36956447 -0.72084105
 -0.35652372]
Sparsity at: 0.028493613824192337
Epoch 331/500
235/235 [==============================] - 4s 15ms/step - loss: 1.8495e-05 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.37962177 -0.7226904
 -0.35691985]
Sparsity at: 0.028493613824192337
Epoch 332/500
235/235 [==============================] - 3s 15ms/step - loss: 9.1731e-06 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.37871665 -0.72195965
 -0.3568013 ]
Sparsity at: 0.028493613824192337
Epoch 333/500
235/235 [==============================] - 3s 15ms/step - loss: 7.1509e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.3799237  -0.7220545
 -0.35781285]
Sparsity at: 0.028493613824192337
Epoch 334/500
235/235 [==============================] - 3s 15ms/step - loss: 6.7008e-06 - accuracy: 1.0000 - val_loss: 0.1219 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.381627   -0.7225893
 -0.35709974]
Sparsity at: 0.028493613824192337
Epoch 335/500
235/235 [==============================] - 4s 15ms/step - loss: 5.2533e-06 - accuracy: 1.0000 - val_loss: 0.1209 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.38215622 -0.72276706
 -0.3575551 ]
Sparsity at: 0.028493613824192337
Epoch 336/500
235/235 [==============================] - 3s 15ms/step - loss: 4.8165e-06 - accuracy: 1.0000 - val_loss: 0.1212 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.382356   -0.7228786
 -0.35783023]
Sparsity at: 0.028493613824192337
Epoch 337/500
235/235 [==============================] - 3s 15ms/step - loss: 4.2426e-06 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.38277435 -0.72347313
 -0.357764  ]
Sparsity at: 0.028493613824192337
Epoch 338/500
235/235 [==============================] - 3s 15ms/step - loss: 3.9939e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.38326    -0.7238172
 -0.35875434]
Sparsity at: 0.028493613824192337
Epoch 339/500
235/235 [==============================] - 3s 15ms/step - loss: 3.4883e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.3835495  -0.72423416
 -0.35791403]
Sparsity at: 0.028493613824192337
Epoch 340/500
235/235 [==============================] - 3s 15ms/step - loss: 3.1003e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.38385186 -0.7244328
 -0.358441  ]
Sparsity at: 0.028493613824192337
Epoch 341/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5739e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9846
[-0.05253947 -0.00531845 -0.04093379 ...  0.3844345  -0.7248448
 -0.35858193]
Sparsity at: 0.028493613824192337
Epoch 342/500
235/235 [==============================] - 3s 15ms/step - loss: 4.8975e-06 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.3841586  -0.7254179
 -0.3581803 ]
Sparsity at: 0.028493613824192337
Epoch 343/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7493e-06 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.38512635 -0.72603214
 -0.3577346 ]
Sparsity at: 0.028493613824192337
Epoch 344/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1948e-05 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9825
[-0.05253947 -0.00531845 -0.04093379 ...  0.3869214  -0.7258471
 -0.37336922]
Sparsity at: 0.028493613824192337
Epoch 345/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0075 - accuracy: 0.9979 - val_loss: 0.1408 - val_accuracy: 0.9807
[-0.05253947 -0.00531845 -0.04093379 ...  0.39259848 -0.7517546
 -0.31886986]
Sparsity at: 0.028493613824192337
Epoch 346/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.1267 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.41263568 -0.7569819
 -0.33305418]
Sparsity at: 0.028493613824192337
Epoch 347/500
235/235 [==============================] - 3s 15ms/step - loss: 6.0096e-04 - accuracy: 0.9998 - val_loss: 0.1229 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.40937153 -0.75262415
 -0.32613665]
Sparsity at: 0.028493613824192337
Epoch 348/500
235/235 [==============================] - 4s 15ms/step - loss: 1.8938e-04 - accuracy: 0.9999 - val_loss: 0.1210 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.41916695 -0.7529874
 -0.33452314]
Sparsity at: 0.028493613824192337
Epoch 349/500
235/235 [==============================] - 4s 15ms/step - loss: 1.3218e-04 - accuracy: 0.9999 - val_loss: 0.1240 - val_accuracy: 0.9836
[-0.05253947 -0.00531845 -0.04093379 ...  0.4184474  -0.7554464
 -0.33194628]
Sparsity at: 0.028493613824192337
Epoch 350/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0932e-04 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.41884583 -0.7566115
 -0.33458635]
Sparsity at: 0.028493613824192337
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.6035266542716471
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.5966045748912734
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.806086159922458
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 193s 12ms/step - loss: 1.1876e-04 - accuracy: 1.0000 - val_loss: 0.1220 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.40496027 -0.7561971
 -0.33538452]
Sparsity at: 0.028493613824192337
Epoch 352/500
235/235 [==============================] - 3s 15ms/step - loss: 9.1282e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.4057338  -0.7565623
 -0.33624917]
Sparsity at: 0.028493613824192337
Epoch 353/500
235/235 [==============================] - 4s 17ms/step - loss: 3.9320e-05 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.40804118 -0.7553667
 -0.33572304]
Sparsity at: 0.028493613824192337
Epoch 354/500
235/235 [==============================] - 3s 15ms/step - loss: 4.4464e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.40909913 -0.75635564
 -0.33643073]
Sparsity at: 0.028493613824192337
Epoch 355/500
235/235 [==============================] - 3s 15ms/step - loss: 4.6219e-04 - accuracy: 0.9999 - val_loss: 0.1215 - val_accuracy: 0.9846
[-0.05253947 -0.00531845 -0.04093379 ...  0.40938306 -0.76063615
 -0.33669677]
Sparsity at: 0.028493613824192337
Epoch 356/500
235/235 [==============================] - 3s 15ms/step - loss: 7.0665e-05 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.41290727 -0.763049
 -0.3406392 ]
Sparsity at: 0.028493613824192337
Epoch 357/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4436e-05 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.42531255 -0.76381063
 -0.3416379 ]
Sparsity at: 0.028493613824192337
Epoch 358/500
235/235 [==============================] - 4s 15ms/step - loss: 9.9218e-06 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9842
[-0.05253947 -0.00531845 -0.04093379 ...  0.4251939  -0.7650657
 -0.34163862]
Sparsity at: 0.028493613824192337
Epoch 359/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0558e-05 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.4253335  -0.7650991
 -0.34193328]
Sparsity at: 0.028493613824192337
Epoch 360/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0357e-05 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.425843   -0.76449645
 -0.3407809 ]
Sparsity at: 0.028493613824192337
Epoch 361/500
235/235 [==============================] - 3s 15ms/step - loss: 9.0911e-06 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.42608228 -0.76490545
 -0.34106064]
Sparsity at: 0.028493613824192337
Epoch 362/500
235/235 [==============================] - 3s 15ms/step - loss: 9.5807e-06 - accuracy: 1.0000 - val_loss: 0.1219 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.42626745 -0.76388735
 -0.34287342]
Sparsity at: 0.028493613824192337
Epoch 363/500
235/235 [==============================] - 4s 15ms/step - loss: 6.8157e-06 - accuracy: 1.0000 - val_loss: 0.1219 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.42629826 -0.7629148
 -0.34240225]
Sparsity at: 0.028493613824192337
Epoch 364/500
235/235 [==============================] - 3s 15ms/step - loss: 6.3536e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.42753306 -0.7632398
 -0.3426891 ]
Sparsity at: 0.028493613824192337
Epoch 365/500
235/235 [==============================] - 3s 15ms/step - loss: 5.2781e-06 - accuracy: 1.0000 - val_loss: 0.1223 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.42799413 -0.7662989
 -0.34190804]
Sparsity at: 0.028493613824192337
Epoch 366/500
235/235 [==============================] - 4s 15ms/step - loss: 4.2840e-06 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.42803007 -0.7648808
 -0.3418253 ]
Sparsity at: 0.028493613824192337
Epoch 367/500
235/235 [==============================] - 3s 15ms/step - loss: 4.9721e-06 - accuracy: 1.0000 - val_loss: 0.1223 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.42920774 -0.7696367
 -0.34177244]
Sparsity at: 0.028493613824192337
Epoch 368/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1740e-05 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.4470256  -0.76815087
 -0.33886737]
Sparsity at: 0.028493613824192337
Epoch 369/500
235/235 [==============================] - 4s 15ms/step - loss: 7.0091e-06 - accuracy: 1.0000 - val_loss: 0.1202 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.44155318 -0.7667727
 -0.34044573]
Sparsity at: 0.028493613824192337
Epoch 370/500
235/235 [==============================] - 4s 15ms/step - loss: 4.2506e-06 - accuracy: 1.0000 - val_loss: 0.1208 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.44186962 -0.76630586
 -0.3439008 ]
Sparsity at: 0.028493613824192337
Epoch 371/500
235/235 [==============================] - 3s 15ms/step - loss: 3.3281e-06 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9846
[-0.05253947 -0.00531845 -0.04093379 ...  0.44129005 -0.76495844
 -0.34414202]
Sparsity at: 0.028493613824192337
Epoch 372/500
235/235 [==============================] - 4s 15ms/step - loss: 3.1902e-06 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.4415182  -0.76280767
 -0.34461823]
Sparsity at: 0.028493613824192337
Epoch 373/500
235/235 [==============================] - 4s 15ms/step - loss: 2.4547e-06 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9846
[-0.05253947 -0.00531845 -0.04093379 ...  0.44152284 -0.7636279
 -0.3447222 ]
Sparsity at: 0.028493613824192337
Epoch 374/500
235/235 [==============================] - 4s 15ms/step - loss: 1.7559e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.44142306 -0.7631707
 -0.34493458]
Sparsity at: 0.028493613824192337
Epoch 375/500
235/235 [==============================] - 4s 15ms/step - loss: 1.8557e-06 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.44151977 -0.7655093
 -0.34511808]
Sparsity at: 0.028493613824192337
Epoch 376/500
235/235 [==============================] - 4s 15ms/step - loss: 1.6447e-06 - accuracy: 1.0000 - val_loss: 0.1224 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.44126698 -0.76486504
 -0.3454091 ]
Sparsity at: 0.028493613824192337
Epoch 377/500
235/235 [==============================] - 4s 15ms/step - loss: 1.4869e-06 - accuracy: 1.0000 - val_loss: 0.1229 - val_accuracy: 0.9846
[-0.05253947 -0.00531845 -0.04093379 ...  0.4414364  -0.7650728
 -0.34554008]
Sparsity at: 0.028493613824192337
Epoch 378/500
235/235 [==============================] - 3s 15ms/step - loss: 1.4741e-06 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9846
[-0.05253947 -0.00531845 -0.04093379 ...  0.44173425 -0.7651301
 -0.34639227]
Sparsity at: 0.028493613824192337
Epoch 379/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5288e-06 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.43972218 -0.76450163
 -0.34711185]
Sparsity at: 0.028493613824192337
Epoch 380/500
235/235 [==============================] - 4s 15ms/step - loss: 1.2850e-06 - accuracy: 1.0000 - val_loss: 0.1234 - val_accuracy: 0.9848
[-0.05253947 -0.00531845 -0.04093379 ...  0.43977132 -0.7643255
 -0.3479063 ]
Sparsity at: 0.028493613824192337
Epoch 381/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0341e-06 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9848
[-0.05253947 -0.00531845 -0.04093379 ...  0.44075084 -0.7652613
 -0.34915864]
Sparsity at: 0.028493613824192337
Epoch 382/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2714e-06 - accuracy: 1.0000 - val_loss: 0.1229 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.44046453 -0.7654178
 -0.34843224]
Sparsity at: 0.028493613824192337
Epoch 383/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 0.1584 - val_accuracy: 0.9805
[-0.05253947 -0.00531845 -0.04093379 ...  0.37593022 -0.7279469
 -0.34776244]
Sparsity at: 0.028493613824192337
Epoch 384/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1349 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.3572379  -0.7183091
 -0.33100137]
Sparsity at: 0.028493613824192337
Epoch 385/500
235/235 [==============================] - 3s 15ms/step - loss: 6.8026e-04 - accuracy: 0.9998 - val_loss: 0.1348 - val_accuracy: 0.9817
[-0.05253947 -0.00531845 -0.04093379 ...  0.3636522  -0.71742594
 -0.33558244]
Sparsity at: 0.028493613824192337
Epoch 386/500
235/235 [==============================] - 3s 15ms/step - loss: 1.9572e-04 - accuracy: 0.9999 - val_loss: 0.1358 - val_accuracy: 0.9814
[-0.05253947 -0.00531845 -0.04093379 ...  0.36515898 -0.71582264
 -0.344017  ]
Sparsity at: 0.028493613824192337
Epoch 387/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0998e-04 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9820
[-0.05253947 -0.00531845 -0.04093379 ...  0.36479947 -0.7172393
 -0.3438424 ]
Sparsity at: 0.028493613824192337
Epoch 388/500
235/235 [==============================] - 3s 15ms/step - loss: 4.1929e-05 - accuracy: 1.0000 - val_loss: 0.1288 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.3670163  -0.7181966
 -0.34429932]
Sparsity at: 0.028493613824192337
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7746e-05 - accuracy: 1.0000 - val_loss: 0.1267 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.3642123  -0.71691114
 -0.34356996]
Sparsity at: 0.028493613824192337
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5356e-05 - accuracy: 1.0000 - val_loss: 0.1266 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.3649509  -0.7170459
 -0.34395397]
Sparsity at: 0.028493613824192337
Epoch 391/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5297e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.3666767  -0.71747935
 -0.34585875]
Sparsity at: 0.028493613824192337
Epoch 392/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5690e-05 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.36684766 -0.7191423
 -0.3467173 ]
Sparsity at: 0.028493613824192337
Epoch 393/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2209e-05 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.3676588  -0.71904194
 -0.347862  ]
Sparsity at: 0.028493613824192337
Epoch 394/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3318e-05 - accuracy: 1.0000 - val_loss: 0.1278 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.36802164 -0.71884024
 -0.3482263 ]
Sparsity at: 0.028493613824192337
Epoch 395/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1534e-04 - accuracy: 0.9999 - val_loss: 0.1315 - val_accuracy: 0.9818
[-0.05253947 -0.00531845 -0.04093379 ...  0.36987072 -0.7164113
 -0.33295283]
Sparsity at: 0.028493613824192337
Epoch 396/500
235/235 [==============================] - 4s 15ms/step - loss: 6.6981e-04 - accuracy: 0.9998 - val_loss: 0.1337 - val_accuracy: 0.9823
[-0.05253947 -0.00531845 -0.04093379 ...  0.36594144 -0.70562077
 -0.3321083 ]
Sparsity at: 0.028493613824192337
Epoch 397/500
235/235 [==============================] - 4s 15ms/step - loss: 2.8309e-04 - accuracy: 0.9999 - val_loss: 0.1348 - val_accuracy: 0.9821
[-0.05253947 -0.00531845 -0.04093379 ...  0.36539268 -0.7097641
 -0.33957472]
Sparsity at: 0.028493613824192337
Epoch 398/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1821e-04 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9818
[-0.05253947 -0.00531845 -0.04093379 ...  0.36932504 -0.72061545
 -0.3367694 ]
Sparsity at: 0.028493613824192337
Epoch 399/500
235/235 [==============================] - 4s 15ms/step - loss: 7.8329e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.36894634 -0.71312803
 -0.35170096]
Sparsity at: 0.028493613824192337
Epoch 400/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8901e-05 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9825
[-0.05253947 -0.00531845 -0.04093379 ...  0.3707043  -0.71456516
 -0.3518203 ]
Sparsity at: 0.028493613824192337
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.6749689754423756
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.6583286934458954
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25283334
tf.Tensor(
[[1. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.867380159112713
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 194s 13ms/step - loss: 1.0437e-05 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.36941987 -0.7145854
 -0.35241663]
Sparsity at: 0.028493613824192337
Epoch 402/500
235/235 [==============================] - 3s 15ms/step - loss: 6.8259e-06 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.3706934  -0.71546215
 -0.35319513]
Sparsity at: 0.028493613824192337
Epoch 403/500
235/235 [==============================] - 3s 15ms/step - loss: 8.0533e-05 - accuracy: 0.9999 - val_loss: 0.1356 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.37244856 -0.72702944
 -0.35329852]
Sparsity at: 0.028493613824192337
Epoch 404/500
235/235 [==============================] - 3s 15ms/step - loss: 3.7139e-04 - accuracy: 0.9999 - val_loss: 0.1329 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.38429132 -0.709691
 -0.36136505]
Sparsity at: 0.028493613824192337
Epoch 405/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1541 - val_accuracy: 0.9805
[-0.05253947 -0.00531845 -0.04093379 ...  0.37165084 -0.7248993
 -0.37113595]
Sparsity at: 0.028493613824192337
Epoch 406/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.1271 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.388395   -0.72853774
 -0.3750149 ]
Sparsity at: 0.028493613824192337
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1107e-04 - accuracy: 0.9999 - val_loss: 0.1291 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.3936288  -0.7368216
 -0.3659785 ]
Sparsity at: 0.028493613824192337
Epoch 408/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3032e-04 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.39517137 -0.73421955
 -0.37125292]
Sparsity at: 0.028493613824192337
Epoch 409/500
235/235 [==============================] - 3s 15ms/step - loss: 7.0882e-05 - accuracy: 1.0000 - val_loss: 0.1280 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.39350152 -0.73168546
 -0.36969844]
Sparsity at: 0.028493613824192337
Epoch 410/500
235/235 [==============================] - 4s 15ms/step - loss: 1.8395e-05 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.39331073 -0.7326898
 -0.36909068]
Sparsity at: 0.028493613824192337
Epoch 411/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0221e-05 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.39668423 -0.7407682
 -0.3688209 ]
Sparsity at: 0.028493613824192337
Epoch 412/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1756e-04 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.39496773 -0.748358
 -0.36587054]
Sparsity at: 0.028493613824192337
Epoch 413/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0969e-04 - accuracy: 0.9999 - val_loss: 0.1309 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.3937466  -0.7452521
 -0.36350653]
Sparsity at: 0.028493613824192337
Epoch 414/500
235/235 [==============================] - 3s 15ms/step - loss: 5.1131e-05 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9840
[-0.05253947 -0.00531845 -0.04093379 ...  0.39799434 -0.7531699
 -0.35590062]
Sparsity at: 0.028493613824192337
Epoch 415/500
235/235 [==============================] - 3s 15ms/step - loss: 3.2250e-04 - accuracy: 0.9999 - val_loss: 0.1327 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.40281758 -0.7412514
 -0.36725128]
Sparsity at: 0.028493613824192337
Epoch 416/500
235/235 [==============================] - 3s 15ms/step - loss: 2.3050e-04 - accuracy: 0.9999 - val_loss: 0.1401 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.410807   -0.7416229
 -0.3651779 ]
Sparsity at: 0.028493613824192337
Epoch 417/500
235/235 [==============================] - 3s 15ms/step - loss: 3.2940e-04 - accuracy: 0.9999 - val_loss: 0.1339 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.40051156 -0.7577681
 -0.36604753]
Sparsity at: 0.028493613824192337
Epoch 418/500
235/235 [==============================] - 3s 15ms/step - loss: 2.6324e-04 - accuracy: 0.9999 - val_loss: 0.1343 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.3969749  -0.7639135
 -0.36612716]
Sparsity at: 0.028493613824192337
Epoch 419/500
235/235 [==============================] - 3s 15ms/step - loss: 4.2030e-04 - accuracy: 0.9998 - val_loss: 0.1433 - val_accuracy: 0.9819
[-0.05253947 -0.00531845 -0.04093379 ...  0.40755168 -0.7586378
 -0.34532246]
Sparsity at: 0.028493613824192337
Epoch 420/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1443 - val_accuracy: 0.9811
[-0.05253947 -0.00531845 -0.04093379 ...  0.4134971  -0.7450418
 -0.3199105 ]
Sparsity at: 0.028493613824192337
Epoch 421/500
235/235 [==============================] - 4s 15ms/step - loss: 3.4537e-04 - accuracy: 0.9999 - val_loss: 0.1297 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.4034531  -0.7347705
 -0.32510707]
Sparsity at: 0.028493613824192337
Epoch 422/500
235/235 [==============================] - 4s 15ms/step - loss: 3.7446e-04 - accuracy: 0.9999 - val_loss: 0.1259 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.41830692 -0.7428895
 -0.32144773]
Sparsity at: 0.028493613824192337
Epoch 423/500
235/235 [==============================] - 4s 15ms/step - loss: 4.1874e-04 - accuracy: 0.9999 - val_loss: 0.1276 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.40457782 -0.7468607
 -0.3210215 ]
Sparsity at: 0.028493613824192337
Epoch 424/500
235/235 [==============================] - 4s 15ms/step - loss: 5.7915e-05 - accuracy: 1.0000 - val_loss: 0.1250 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.40241107 -0.7481065
 -0.32221124]
Sparsity at: 0.028493613824192337
Epoch 425/500
235/235 [==============================] - 4s 15ms/step - loss: 2.5385e-05 - accuracy: 1.0000 - val_loss: 0.1281 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.402792   -0.75297254
 -0.32371187]
Sparsity at: 0.028493613824192337
Epoch 426/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5728e-05 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.41689238 -0.75832766
 -0.32438895]
Sparsity at: 0.028493613824192337
Epoch 427/500
235/235 [==============================] - 4s 15ms/step - loss: 7.3024e-06 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9830
[-0.05253947 -0.00531845 -0.04093379 ...  0.41739288 -0.7578309
 -0.3249548 ]
Sparsity at: 0.028493613824192337
Epoch 428/500
235/235 [==============================] - 3s 15ms/step - loss: 5.8485e-06 - accuracy: 1.0000 - val_loss: 0.1281 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.41723558 -0.7582679
 -0.32522917]
Sparsity at: 0.028493613824192337
Epoch 429/500
235/235 [==============================] - 4s 15ms/step - loss: 8.5290e-06 - accuracy: 1.0000 - val_loss: 0.1277 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.41852644 -0.7586658
 -0.3277146 ]
Sparsity at: 0.028493613824192337
Epoch 430/500
235/235 [==============================] - 4s 15ms/step - loss: 5.3686e-06 - accuracy: 1.0000 - val_loss: 0.1272 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.4111333  -0.7587286
 -0.327701  ]
Sparsity at: 0.028493613824192337
Epoch 431/500
235/235 [==============================] - 4s 15ms/step - loss: 3.7925e-06 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.4117323  -0.7587867
 -0.32863724]
Sparsity at: 0.028493613824192337
Epoch 432/500
235/235 [==============================] - 3s 15ms/step - loss: 3.9764e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.41311213 -0.7581386
 -0.3299222 ]
Sparsity at: 0.028493613824192337
Epoch 433/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0544e-04 - accuracy: 0.9999 - val_loss: 0.1408 - val_accuracy: 0.9820
[-0.05253947 -0.00531845 -0.04093379 ...  0.42093828 -0.76601624
 -0.33279094]
Sparsity at: 0.028493613824192337
Epoch 434/500
235/235 [==============================] - 3s 15ms/step - loss: 2.6124e-05 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.42030528 -0.75663525
 -0.33137178]
Sparsity at: 0.028493613824192337
Epoch 435/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7915e-04 - accuracy: 0.9999 - val_loss: 0.1350 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.41798434 -0.76034266
 -0.3319928 ]
Sparsity at: 0.028493613824192337
Epoch 436/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1438 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.43176204 -0.77266973
 -0.33652893]
Sparsity at: 0.028493613824192337
Epoch 437/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.1708 - val_accuracy: 0.9784
[-0.05253947 -0.00531845 -0.04093379 ...  0.44342205 -0.75575805
 -0.33219737]
Sparsity at: 0.028493613824192337
Epoch 438/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 0.1501 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.45365125 -0.76816237
 -0.3337154 ]
Sparsity at: 0.028493613824192337
Epoch 439/500
235/235 [==============================] - 4s 16ms/step - loss: 1.7110e-04 - accuracy: 0.9999 - val_loss: 0.1412 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.45614928 -0.7660927
 -0.33307832]
Sparsity at: 0.028493613824192337
Epoch 440/500
235/235 [==============================] - 3s 15ms/step - loss: 6.3487e-05 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9843
[-0.05253947 -0.00531845 -0.04093379 ...  0.45245424 -0.7664272
 -0.3291382 ]
Sparsity at: 0.028493613824192337
Epoch 441/500
235/235 [==============================] - 3s 15ms/step - loss: 2.8139e-05 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.45102564 -0.7675537
 -0.32854694]
Sparsity at: 0.028493613824192337
Epoch 442/500
235/235 [==============================] - 3s 15ms/step - loss: 2.8337e-05 - accuracy: 1.0000 - val_loss: 0.1377 - val_accuracy: 0.9841
[-0.05253947 -0.00531845 -0.04093379 ...  0.46775946 -0.7678541
 -0.34251916]
Sparsity at: 0.028493613824192337
Epoch 443/500
235/235 [==============================] - 3s 15ms/step - loss: 3.3209e-05 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9848
[-0.05253947 -0.00531845 -0.04093379 ...  0.46768463 -0.76743776
 -0.3403029 ]
Sparsity at: 0.028493613824192337
Epoch 444/500
235/235 [==============================] - 4s 15ms/step - loss: 8.6267e-06 - accuracy: 1.0000 - val_loss: 0.1349 - val_accuracy: 0.9847
[-0.05253947 -0.00531845 -0.04093379 ...  0.4667296  -0.7669822
 -0.33983156]
Sparsity at: 0.028493613824192337
Epoch 445/500
235/235 [==============================] - 3s 15ms/step - loss: 9.4922e-06 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.46635967 -0.76714885
 -0.33959824]
Sparsity at: 0.028493613824192337
Epoch 446/500
235/235 [==============================] - 4s 15ms/step - loss: 6.1517e-06 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.4669156  -0.7671888
 -0.33941928]
Sparsity at: 0.028493613824192337
Epoch 447/500
235/235 [==============================] - 3s 15ms/step - loss: 5.0258e-06 - accuracy: 1.0000 - val_loss: 0.1332 - val_accuracy: 0.9845
[-0.05253947 -0.00531845 -0.04093379 ...  0.46693552 -0.7674959
 -0.3403831 ]
Sparsity at: 0.028493613824192337
Epoch 448/500
235/235 [==============================] - 3s 15ms/step - loss: 2.5125e-05 - accuracy: 1.0000 - val_loss: 0.1322 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.46668315 -0.7703708
 -0.34074643]
Sparsity at: 0.028493613824192337
Epoch 449/500
235/235 [==============================] - 3s 15ms/step - loss: 2.8842e-04 - accuracy: 0.9999 - val_loss: 0.1371 - val_accuracy: 0.9837
[-0.05253947 -0.00531845 -0.04093379 ...  0.45929548 -0.76702774
 -0.3376823 ]
Sparsity at: 0.028493613824192337
Epoch 450/500
235/235 [==============================] - 3s 15ms/step - loss: 6.6382e-04 - accuracy: 0.9998 - val_loss: 0.1516 - val_accuracy: 0.9809
[-0.05253947 -0.00531845 -0.04093379 ...  0.4464498  -0.785883
 -0.3476013 ]
Sparsity at: 0.028493613824192337
Epoch 451/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.1479 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.43447194 -0.7841925
 -0.36065876]
Sparsity at: 0.028493613824192337
Epoch 452/500
235/235 [==============================] - 3s 15ms/step - loss: 7.1483e-04 - accuracy: 0.9997 - val_loss: 0.1364 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.4234038  -0.77283496
 -0.35752153]
Sparsity at: 0.028493613824192337
Epoch 453/500
235/235 [==============================] - 3s 15ms/step - loss: 3.7847e-04 - accuracy: 0.9998 - val_loss: 0.1419 - val_accuracy: 0.9822
[-0.05253947 -0.00531845 -0.04093379 ...  0.4540731  -0.7770975
 -0.35945258]
Sparsity at: 0.028493613824192337
Epoch 454/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2748e-04 - accuracy: 1.0000 - val_loss: 0.1379 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.45216873 -0.7857477
 -0.37768865]
Sparsity at: 0.028493613824192337
Epoch 455/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1552e-04 - accuracy: 1.0000 - val_loss: 0.1363 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.45357776 -0.78326845
 -0.37596932]
Sparsity at: 0.028493613824192337
Epoch 456/500
235/235 [==============================] - 4s 15ms/step - loss: 1.4910e-04 - accuracy: 1.0000 - val_loss: 0.1346 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.45296377 -0.7869139
 -0.37749755]
Sparsity at: 0.028493613824192337
Epoch 457/500
235/235 [==============================] - 3s 15ms/step - loss: 4.4171e-05 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.45378244 -0.78569096
 -0.37550688]
Sparsity at: 0.028493613824192337
Epoch 458/500
235/235 [==============================] - 4s 15ms/step - loss: 5.2295e-05 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.45438558 -0.7843429
 -0.37581572]
Sparsity at: 0.028493613824192337
Epoch 459/500
235/235 [==============================] - 3s 15ms/step - loss: 2.3513e-05 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.44743505 -0.7828709
 -0.37561774]
Sparsity at: 0.028493613824192337
Epoch 460/500
235/235 [==============================] - 4s 15ms/step - loss: 1.0521e-05 - accuracy: 1.0000 - val_loss: 0.1281 - val_accuracy: 0.9844
[-0.05253947 -0.00531845 -0.04093379 ...  0.4481748  -0.78239226
 -0.38164914]
Sparsity at: 0.028493613824192337
Epoch 461/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1733e-05 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9839
[-0.05253947 -0.00531845 -0.04093379 ...  0.44826797 -0.7827413
 -0.37476727]
Sparsity at: 0.028493613824192337
Epoch 462/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1007e-04 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.4491203  -0.7834212
 -0.380703  ]
Sparsity at: 0.028493613824192337
Epoch 463/500
235/235 [==============================] - 3s 15ms/step - loss: 4.7313e-04 - accuracy: 0.9999 - val_loss: 0.1573 - val_accuracy: 0.9807
[-0.05253947 -0.00531845 -0.04093379 ...  0.44775844 -0.7974664
 -0.37249154]
Sparsity at: 0.028493613824192337
Epoch 464/500
235/235 [==============================] - 3s 15ms/step - loss: 2.9944e-04 - accuracy: 0.9999 - val_loss: 0.1434 - val_accuracy: 0.9823
[-0.05253947 -0.00531845 -0.04093379 ...  0.46233323 -0.7907427
 -0.40250686]
Sparsity at: 0.028493613824192337
Epoch 465/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0314e-04 - accuracy: 0.9999 - val_loss: 0.1464 - val_accuracy: 0.9815
[-0.05253947 -0.00531845 -0.04093379 ...  0.48241386 -0.77869844
 -0.43053284]
Sparsity at: 0.028493613824192337
Epoch 466/500
235/235 [==============================] - 3s 15ms/step - loss: 5.9290e-04 - accuracy: 0.9998 - val_loss: 0.1502 - val_accuracy: 0.9813
[-0.05253947 -0.00531845 -0.04093379 ...  0.48071018 -0.77188385
 -0.40305296]
Sparsity at: 0.028493613824192337
Epoch 467/500
235/235 [==============================] - 3s 15ms/step - loss: 2.5307e-04 - accuracy: 0.9999 - val_loss: 0.1560 - val_accuracy: 0.9805
[-0.05253947 -0.00531845 -0.04093379 ...  0.48189926 -0.7822515
 -0.4155562 ]
Sparsity at: 0.028493613824192337
Epoch 468/500
235/235 [==============================] - 4s 16ms/step - loss: 2.5292e-04 - accuracy: 0.9999 - val_loss: 0.1468 - val_accuracy: 0.9823
[-0.05253947 -0.00531845 -0.04093379 ...  0.4805368  -0.7811953
 -0.41130468]
Sparsity at: 0.028493613824192337
Epoch 469/500
235/235 [==============================] - 4s 16ms/step - loss: 7.7404e-05 - accuracy: 1.0000 - val_loss: 0.1470 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.47709835 -0.77943796
 -0.41125587]
Sparsity at: 0.028493613824192337
Epoch 470/500
235/235 [==============================] - 4s 15ms/step - loss: 5.3709e-04 - accuracy: 0.9998 - val_loss: 0.1526 - val_accuracy: 0.9818
[-0.05253947 -0.00531845 -0.04093379 ...  0.4774776  -0.77366155
 -0.41163024]
Sparsity at: 0.028493613824192337
Epoch 471/500
235/235 [==============================] - 3s 15ms/step - loss: 4.4727e-04 - accuracy: 0.9999 - val_loss: 0.1575 - val_accuracy: 0.9812
[-0.05253947 -0.00531845 -0.04093379 ...  0.47658587 -0.7739178
 -0.40960613]
Sparsity at: 0.028493613824192337
Epoch 472/500
235/235 [==============================] - 3s 15ms/step - loss: 2.6488e-04 - accuracy: 1.0000 - val_loss: 0.1489 - val_accuracy: 0.9815
[-0.05253947 -0.00531845 -0.04093379 ...  0.47049978 -0.76471066
 -0.40972468]
Sparsity at: 0.028493613824192337
Epoch 473/500
235/235 [==============================] - 4s 15ms/step - loss: 3.7643e-05 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9820
[-0.05253947 -0.00531845 -0.04093379 ...  0.4682524  -0.7636404
 -0.40440422]
Sparsity at: 0.028493613824192337
Epoch 474/500
235/235 [==============================] - 3s 15ms/step - loss: 3.9213e-05 - accuracy: 1.0000 - val_loss: 0.1467 - val_accuracy: 0.9819
[-0.05253947 -0.00531845 -0.04093379 ...  0.46853176 -0.76619816
 -0.41043293]
Sparsity at: 0.028493613824192337
Epoch 475/500
235/235 [==============================] - 3s 15ms/step - loss: 2.9909e-05 - accuracy: 1.0000 - val_loss: 0.1450 - val_accuracy: 0.9824
[-0.05253947 -0.00531845 -0.04093379 ...  0.4769757  -0.7653083
 -0.4103344 ]
Sparsity at: 0.028493613824192337
Epoch 476/500
235/235 [==============================] - 3s 15ms/step - loss: 8.4159e-05 - accuracy: 1.0000 - val_loss: 0.1474 - val_accuracy: 0.9820
[-0.05253947 -0.00531845 -0.04093379 ...  0.47163013 -0.7642421
 -0.40839946]
Sparsity at: 0.028493613824192337
Epoch 477/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.1547 - val_accuracy: 0.9814
[-0.05253947 -0.00531845 -0.04093379 ...  0.47469926 -0.7604162
 -0.4080412 ]
Sparsity at: 0.028493613824192337
Epoch 478/500
235/235 [==============================] - 4s 15ms/step - loss: 8.5478e-04 - accuracy: 0.9998 - val_loss: 0.1524 - val_accuracy: 0.9815
[-0.05253947 -0.00531845 -0.04093379 ...  0.47705188 -0.7633717
 -0.43099895]
Sparsity at: 0.028493613824192337
Epoch 479/500
235/235 [==============================] - 3s 15ms/step - loss: 4.1060e-04 - accuracy: 0.9998 - val_loss: 0.1554 - val_accuracy: 0.9813
[-0.05253947 -0.00531845 -0.04093379 ...  0.4762578  -0.7704749
 -0.43019977]
Sparsity at: 0.028493613824192337
Epoch 480/500
235/235 [==============================] - 4s 15ms/step - loss: 3.3028e-04 - accuracy: 0.9999 - val_loss: 0.1489 - val_accuracy: 0.9826
[-0.05253947 -0.00531845 -0.04093379 ...  0.46161947 -0.7570636
 -0.42566922]
Sparsity at: 0.028493613824192337
Epoch 481/500
235/235 [==============================] - 4s 15ms/step - loss: 4.6733e-04 - accuracy: 0.9999 - val_loss: 0.1464 - val_accuracy: 0.9812
[-0.05253947 -0.00531845 -0.04093379 ...  0.50503606 -0.7605507
 -0.41889885]
Sparsity at: 0.028493613824192337
Epoch 482/500
235/235 [==============================] - 3s 15ms/step - loss: 8.6350e-05 - accuracy: 1.0000 - val_loss: 0.1402 - val_accuracy: 0.9828
[-0.05253947 -0.00531845 -0.04093379 ...  0.49830016 -0.76539403
 -0.42194146]
Sparsity at: 0.028493613824192337
Epoch 483/500
235/235 [==============================] - 3s 15ms/step - loss: 2.9361e-05 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.4935826  -0.76682633
 -0.42509323]
Sparsity at: 0.028493613824192337
Epoch 484/500
235/235 [==============================] - 3s 15ms/step - loss: 3.1918e-05 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9827
[-0.05253947 -0.00531845 -0.04093379 ...  0.49565995 -0.76823354
 -0.42688572]
Sparsity at: 0.028493613824192337
Epoch 485/500
235/235 [==============================] - 3s 15ms/step - loss: 7.4953e-05 - accuracy: 0.9999 - val_loss: 0.1393 - val_accuracy: 0.9826
[-0.05253947 -0.00531845 -0.04093379 ...  0.49147967 -0.77344
 -0.42743585]
Sparsity at: 0.028493613824192337
Epoch 486/500
235/235 [==============================] - 3s 15ms/step - loss: 4.1151e-05 - accuracy: 1.0000 - val_loss: 0.1407 - val_accuracy: 0.9825
[-0.05253947 -0.00531845 -0.04093379 ...  0.49260774 -0.7732432
 -0.42564327]
Sparsity at: 0.028493613824192337
Epoch 487/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1632 - val_accuracy: 0.9803
[-0.05253947 -0.00531845 -0.04093379 ...  0.5001445  -0.79058915
 -0.37974396]
Sparsity at: 0.028493613824192337
Epoch 488/500
235/235 [==============================] - 3s 15ms/step - loss: 7.9429e-04 - accuracy: 0.9998 - val_loss: 0.1464 - val_accuracy: 0.9832
[-0.05253947 -0.00531845 -0.04093379 ...  0.47101036 -0.7862826
 -0.37028828]
Sparsity at: 0.028493613824192337
Epoch 489/500
235/235 [==============================] - 4s 15ms/step - loss: 5.0195e-04 - accuracy: 0.9998 - val_loss: 0.1496 - val_accuracy: 0.9819
[-0.05253947 -0.00531845 -0.04093379 ...  0.4698597  -0.7996573
 -0.3700886 ]
Sparsity at: 0.028493613824192337
Epoch 490/500
235/235 [==============================] - 4s 15ms/step - loss: 7.0435e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9833
[-0.05253947 -0.00531845 -0.04093379 ...  0.47072944 -0.79275405
 -0.37202716]
Sparsity at: 0.028493613824192337
Epoch 491/500
235/235 [==============================] - 4s 15ms/step - loss: 1.7996e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.47048974 -0.7935129
 -0.37207144]
Sparsity at: 0.028493613824192337
Epoch 492/500
235/235 [==============================] - 3s 15ms/step - loss: 6.8346e-06 - accuracy: 1.0000 - val_loss: 0.1434 - val_accuracy: 0.9835
[-0.05253947 -0.00531845 -0.04093379 ...  0.4697649  -0.79402566
 -0.37249804]
Sparsity at: 0.028493613824192337
Epoch 493/500
235/235 [==============================] - 4s 15ms/step - loss: 9.9476e-06 - accuracy: 1.0000 - val_loss: 0.1449 - val_accuracy: 0.9838
[-0.05253947 -0.00531845 -0.04093379 ...  0.46975625 -0.79390025
 -0.37281314]
Sparsity at: 0.028493613824192337
Epoch 494/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1428e-05 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9834
[-0.05253947 -0.00531845 -0.04093379 ...  0.46974587 -0.7925006
 -0.37250876]
Sparsity at: 0.028493613824192337
Epoch 495/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3169e-04 - accuracy: 1.0000 - val_loss: 0.1500 - val_accuracy: 0.9829
[-0.05253947 -0.00531845 -0.04093379 ...  0.47021708 -0.7950469
 -0.37293392]
Sparsity at: 0.028493613824192337
Epoch 496/500
235/235 [==============================] - 3s 15ms/step - loss: 7.1532e-05 - accuracy: 1.0000 - val_loss: 0.1480 - val_accuracy: 0.9821
[-0.05253947 -0.00531845 -0.04093379 ...  0.46999153 -0.79871917
 -0.37485737]
Sparsity at: 0.028493613824192337
Epoch 497/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7961e-04 - accuracy: 0.9999 - val_loss: 0.1502 - val_accuracy: 0.9826
[-0.05253947 -0.00531845 -0.04093379 ...  0.48269853 -0.80003166
 -0.35478765]
Sparsity at: 0.028493613824192337
Epoch 498/500
235/235 [==============================] - 3s 15ms/step - loss: 5.0421e-04 - accuracy: 0.9998 - val_loss: 0.1569 - val_accuracy: 0.9821
[-0.05253947 -0.00531845 -0.04093379 ...  0.48291698 -0.79664034
 -0.3223419 ]
Sparsity at: 0.028493613824192337
Epoch 499/500
235/235 [==============================] - 3s 15ms/step - loss: 5.3367e-04 - accuracy: 0.9998 - val_loss: 0.1659 - val_accuracy: 0.9805
[-0.05253947 -0.00531845 -0.04093379 ...  0.48295888 -0.78966117
 -0.35997248]
Sparsity at: 0.028493613824192337
Epoch 500/500
235/235 [==============================] - 3s 15ms/step - loss: 4.8086e-04 - accuracy: 0.9998 - val_loss: 0.1496 - val_accuracy: 0.9831
[-0.05253947 -0.00531845 -0.04093379 ...  0.47987184 -0.78292745
 -0.3698607 ]
Sparsity at: 0.028493613824192337
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.041994860395789146
Thresholhold -0.05940218269824982
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.09000259265303612
Thresholhold 0.044793546199798584
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10164112225174904
Thresholhold -0.008578553795814514
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 1:00:57 - loss: 4.6249 - accuracy: 0.1055WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0070s vs `on_train_batch_begin` time: 2.5328s). Check your callbacks.
235/235 [==============================] - 18s 9ms/step - loss: 1.6116 - accuracy: 0.8512 - val_loss: 0.9586 - val_accuracy: 0.9011
[-5.1867892e-07  3.2510341e-09 -3.8373156e-07 ... -2.1742404e-02
  7.4999206e-02 -7.3073901e-02]
Sparsity at: 0.03389887339055794
Epoch 2/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9035 - accuracy: 0.8962 - val_loss: 0.8544 - val_accuracy: 0.9004
[-1.63640846e-12 -1.11412697e-13  1.27109055e-12 ... -5.66245578e-02
  7.01948553e-02 -1.02945901e-01]
Sparsity at: 0.03389887339055794
Epoch 3/500
235/235 [==============================] - 2s 11ms/step - loss: 0.8593 - accuracy: 0.8970 - val_loss: 0.8384 - val_accuracy: 0.8985
[-1.814238e-17  2.594543e-19  7.712466e-18 ... -7.999009e-02  6.481430e-02
 -1.186992e-01]
Sparsity at: 0.03389887339055794
Epoch 4/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8474 - accuracy: 0.8968 - val_loss: 0.8284 - val_accuracy: 0.8982
[-9.4904549e-23  1.1852956e-24  3.2996739e-23 ... -9.8160937e-02
  5.6796581e-02 -1.2494359e-01]
Sparsity at: 0.03389887339055794
Epoch 5/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8967 - val_loss: 0.8225 - val_accuracy: 0.8987
[ 8.7769503e-29 -6.4780971e-30 -7.7824018e-29 ... -1.0970281e-01
  4.7417544e-02 -1.2560837e-01]
Sparsity at: 0.03389887339055794
Epoch 6/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8356 - accuracy: 0.8971 - val_loss: 0.8190 - val_accuracy: 0.8982
[-1.7431876e-33  3.0090966e-34  6.6428912e-34 ... -1.1537661e-01
  3.6847204e-02 -1.2400684e-01]
Sparsity at: 0.03389887339055794
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8325 - accuracy: 0.8972 - val_loss: 0.8149 - val_accuracy: 0.9000
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -1.1746345e-01
  2.6333677e-02 -1.2117126e-01]
Sparsity at: 0.03389887339055794
Epoch 8/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8300 - accuracy: 0.8970 - val_loss: 0.8132 - val_accuracy: 0.8991
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -1.17063634e-01
  1.55907189e-02 -1.17971309e-01]
Sparsity at: 0.03389887339055794
Epoch 9/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8283 - accuracy: 0.8974 - val_loss: 0.8120 - val_accuracy: 0.9001
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -1.15020871e-01
  4.96495608e-03 -1.14879176e-01]
Sparsity at: 0.03389887339055794
Epoch 10/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8267 - accuracy: 0.8973 - val_loss: 0.8095 - val_accuracy: 0.9002
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -1.12723984e-01
 -5.65887056e-03 -1.11563139e-01]
Sparsity at: 0.03389887339055794
Epoch 11/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8256 - accuracy: 0.8972 - val_loss: 0.8075 - val_accuracy: 0.9013
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -1.0996724e-01
 -1.6520964e-02 -1.0806606e-01]
Sparsity at: 0.03389887339055794
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.8974 - val_loss: 0.8076 - val_accuracy: 0.9008
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -1.0701427e-01
 -2.7215760e-02 -1.0463226e-01]
Sparsity at: 0.03389887339055794
Epoch 13/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8236 - accuracy: 0.8976 - val_loss: 0.8062 - val_accuracy: 0.9015
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -1.03802055e-01
 -3.85156162e-02 -1.00779600e-01]
Sparsity at: 0.03389887339055794
Epoch 14/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8231 - accuracy: 0.8977 - val_loss: 0.8048 - val_accuracy: 0.9016
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -1.0063963e-01
 -4.9483795e-02 -9.7168446e-02]
Sparsity at: 0.03389887339055794
Epoch 15/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8222 - accuracy: 0.8980 - val_loss: 0.8044 - val_accuracy: 0.9019
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -9.7607523e-02
 -5.9485435e-02 -9.3539394e-02]
Sparsity at: 0.03389887339055794
Epoch 16/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8220 - accuracy: 0.8983 - val_loss: 0.8041 - val_accuracy: 0.9014
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -9.4508007e-02
 -6.8980098e-02 -9.0206429e-02]
Sparsity at: 0.03389887339055794
Epoch 17/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8212 - accuracy: 0.8984 - val_loss: 0.8040 - val_accuracy: 0.9011
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -9.1799170e-02
 -7.7286400e-02 -8.7064967e-02]
Sparsity at: 0.03389887339055794
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8209 - accuracy: 0.8985 - val_loss: 0.8027 - val_accuracy: 0.9027
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -8.9337848e-02
 -8.4185131e-02 -8.4532142e-02]
Sparsity at: 0.03389887339055794
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8203 - accuracy: 0.8986 - val_loss: 0.8024 - val_accuracy: 0.9018
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -8.6802363e-02
 -8.9671671e-02 -8.2026012e-02]
Sparsity at: 0.03389887339055794
Epoch 20/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8198 - accuracy: 0.8986 - val_loss: 0.8025 - val_accuracy: 0.9018
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -8.4220380e-02
 -9.3932204e-02 -8.0264077e-02]
Sparsity at: 0.03389887339055794
Epoch 21/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8199 - accuracy: 0.8987 - val_loss: 0.8018 - val_accuracy: 0.9015
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -8.1991687e-02
 -9.7548373e-02 -7.8438587e-02]
Sparsity at: 0.03389887339055794
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8195 - accuracy: 0.8987 - val_loss: 0.8024 - val_accuracy: 0.9021
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -7.99612477e-02
 -1.00618616e-01 -7.69459531e-02]
Sparsity at: 0.03389887339055794
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8192 - accuracy: 0.8990 - val_loss: 0.8017 - val_accuracy: 0.9025
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -7.8238539e-02
 -1.0330485e-01 -7.5646684e-02]
Sparsity at: 0.03389887339055794
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8192 - accuracy: 0.8992 - val_loss: 0.8005 - val_accuracy: 0.9023
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -7.6306343e-02
 -1.0567435e-01 -7.4780442e-02]
Sparsity at: 0.03389887339055794
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8190 - accuracy: 0.8987 - val_loss: 0.8014 - val_accuracy: 0.9020
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -7.49229714e-02
 -1.07650965e-01 -7.38429055e-02]
Sparsity at: 0.03389887339055794
Epoch 26/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8188 - accuracy: 0.8993 - val_loss: 0.8017 - val_accuracy: 0.9020
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -7.3448040e-02
 -1.0967043e-01 -7.2962880e-02]
Sparsity at: 0.03389887339055794
Epoch 27/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8187 - accuracy: 0.8990 - val_loss: 0.8006 - val_accuracy: 0.9025
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -7.2161205e-02
 -1.1192950e-01 -7.2087057e-02]
Sparsity at: 0.03389887339055794
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8185 - accuracy: 0.8991 - val_loss: 0.8007 - val_accuracy: 0.9029
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -7.07969218e-02
 -1.13647334e-01 -7.13215023e-02]
Sparsity at: 0.03389887339055794
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8185 - accuracy: 0.8992 - val_loss: 0.8006 - val_accuracy: 0.9019
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -6.9616646e-02
 -1.1501378e-01 -7.0356563e-02]
Sparsity at: 0.03389887339055794
Epoch 30/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8184 - accuracy: 0.8989 - val_loss: 0.8005 - val_accuracy: 0.9029
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -6.82849288e-02
 -1.16282366e-01 -6.96369335e-02]
Sparsity at: 0.03389887339055794
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8183 - accuracy: 0.8989 - val_loss: 0.8011 - val_accuracy: 0.9022
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -6.7237139e-02
 -1.1749545e-01 -6.8666451e-02]
Sparsity at: 0.03389887339055794
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8179 - accuracy: 0.8992 - val_loss: 0.8012 - val_accuracy: 0.9021
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -6.5859020e-02
 -1.1835413e-01 -6.7956232e-02]
Sparsity at: 0.03389887339055794
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8180 - accuracy: 0.8992 - val_loss: 0.8007 - val_accuracy: 0.9027
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -6.4907841e-02
 -1.1937189e-01 -6.7061514e-02]
Sparsity at: 0.03389887339055794
Epoch 34/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8179 - accuracy: 0.8996 - val_loss: 0.8002 - val_accuracy: 0.9025
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -6.3616976e-02
 -1.1994730e-01 -6.6348381e-02]
Sparsity at: 0.03389887339055794
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8181 - accuracy: 0.8991 - val_loss: 0.7999 - val_accuracy: 0.9029
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -6.28290027e-02
 -1.20415725e-01 -6.55044168e-02]
Sparsity at: 0.03389887339055794
Epoch 36/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8175 - accuracy: 0.8992 - val_loss: 0.8007 - val_accuracy: 0.9027
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -6.1789092e-02
 -1.2052637e-01 -6.5280698e-02]
Sparsity at: 0.03389887339055794
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8175 - accuracy: 0.8996 - val_loss: 0.8000 - val_accuracy: 0.9026
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -6.0971141e-02
 -1.2054722e-01 -6.4772278e-02]
Sparsity at: 0.03389887339055794
Epoch 38/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8174 - accuracy: 0.8995 - val_loss: 0.8011 - val_accuracy: 0.9026
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -6.0116373e-02
 -1.2057943e-01 -6.4296521e-02]
Sparsity at: 0.03389887339055794
Epoch 39/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8176 - accuracy: 0.8996 - val_loss: 0.8007 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.9313439e-02
 -1.2035989e-01 -6.3906372e-02]
Sparsity at: 0.03389887339055794
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8172 - accuracy: 0.8995 - val_loss: 0.8008 - val_accuracy: 0.9028
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.8514629e-02
 -1.1996755e-01 -6.3588679e-02]
Sparsity at: 0.03389887339055794
Epoch 41/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8173 - accuracy: 0.8996 - val_loss: 0.7999 - val_accuracy: 0.9033
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -5.76196797e-02
 -1.19375385e-01 -6.33844733e-02]
Sparsity at: 0.03389887339055794
Epoch 42/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8173 - accuracy: 0.8993 - val_loss: 0.8003 - val_accuracy: 0.9027
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -5.69621995e-02
 -1.18990906e-01 -6.31420463e-02]
Sparsity at: 0.03389887339055794
Epoch 43/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8170 - accuracy: 0.8996 - val_loss: 0.8009 - val_accuracy: 0.9024
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -5.61681837e-02
 -1.18346445e-01 -6.29561394e-02]
Sparsity at: 0.03389887339055794
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8170 - accuracy: 0.8997 - val_loss: 0.7997 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.5535611e-02
 -1.1779005e-01 -6.2512837e-02]
Sparsity at: 0.03389887339055794
Epoch 45/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8170 - accuracy: 0.8997 - val_loss: 0.8001 - val_accuracy: 0.9027
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.4861203e-02
 -1.1697276e-01 -6.2190395e-02]
Sparsity at: 0.03389887339055794
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8166 - accuracy: 0.8996 - val_loss: 0.7997 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.4020420e-02
 -1.1622476e-01 -6.2079724e-02]
Sparsity at: 0.03389887339055794
Epoch 47/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8167 - accuracy: 0.9000 - val_loss: 0.8005 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.3064734e-02
 -1.1530566e-01 -6.1944101e-02]
Sparsity at: 0.03389887339055794
Epoch 48/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8170 - accuracy: 0.8998 - val_loss: 0.8012 - val_accuracy: 0.9028
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.2496590e-02
 -1.1481889e-01 -6.1551772e-02]
Sparsity at: 0.03389887339055794
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8169 - accuracy: 0.8995 - val_loss: 0.8007 - val_accuracy: 0.9027
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.1630687e-02
 -1.1380547e-01 -6.1330020e-02]
Sparsity at: 0.03389887339055794
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8166 - accuracy: 0.8995 - val_loss: 0.8004 - val_accuracy: 0.9030
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.0834756e-02
 -1.1296777e-01 -6.1183617e-02]
Sparsity at: 0.03389887339055794
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.008841478533112124
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.029927758149965733
Thresholhold -0.03914691135287285
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.11400627827637067
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 52s 8ms/step - loss: 0.8169 - accuracy: 0.8995 - val_loss: 0.8006 - val_accuracy: 0.9026
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -5.0018761e-02
 -1.1243409e-01 -6.0859196e-02]
Sparsity at: 0.03389887339055794
Epoch 52/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8167 - accuracy: 0.8995 - val_loss: 0.7997 - val_accuracy: 0.9029
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.9329411e-02
 -1.1143783e-01 -6.0601756e-02]
Sparsity at: 0.03389887339055794
Epoch 53/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8166 - accuracy: 0.8996 - val_loss: 0.8011 - val_accuracy: 0.9027
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.8642311e-02
 -1.1079598e-01 -6.0539868e-02]
Sparsity at: 0.03389887339055794
Epoch 54/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8166 - accuracy: 0.8998 - val_loss: 0.7996 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.8091397e-02
 -1.0989093e-01 -6.0272042e-02]
Sparsity at: 0.03389887339055794
Epoch 55/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8164 - accuracy: 0.9000 - val_loss: 0.8005 - val_accuracy: 0.9026
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.7489285e-02
 -1.0932671e-01 -6.0045645e-02]
Sparsity at: 0.03389887339055794
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8165 - accuracy: 0.8997 - val_loss: 0.7999 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.6922620e-02
 -1.0863767e-01 -5.9877496e-02]
Sparsity at: 0.03389887339055794
Epoch 57/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8163 - accuracy: 0.8997 - val_loss: 0.8004 - val_accuracy: 0.9028
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.6353284e-02
 -1.0794710e-01 -5.9844345e-02]
Sparsity at: 0.03389887339055794
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8164 - accuracy: 0.8995 - val_loss: 0.8004 - val_accuracy: 0.9028
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.5732312e-02
 -1.0721869e-01 -5.9489831e-02]
Sparsity at: 0.03389887339055794
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8161 - accuracy: 0.8998 - val_loss: 0.7996 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.5561280e-02
 -1.0671770e-01 -5.9577957e-02]
Sparsity at: 0.03389887339055794
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8159 - accuracy: 0.8997 - val_loss: 0.7996 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.5070834e-02
 -1.0609949e-01 -5.9505381e-02]
Sparsity at: 0.03389887339055794
Epoch 61/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8160 - accuracy: 0.8999 - val_loss: 0.7998 - val_accuracy: 0.9028
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.4533413e-02
 -1.0556256e-01 -5.9660539e-02]
Sparsity at: 0.03389887339055794
Epoch 62/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8161 - accuracy: 0.8997 - val_loss: 0.8002 - val_accuracy: 0.9028
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.42719497e-02
 -1.04760684e-01 -5.94633296e-02]
Sparsity at: 0.03389887339055794
Epoch 63/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8160 - accuracy: 0.8999 - val_loss: 0.7999 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.4076074e-02
 -1.0435625e-01 -5.9546530e-02]
Sparsity at: 0.03389887339055794
Epoch 64/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8157 - accuracy: 0.8999 - val_loss: 0.8002 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.4097386e-02
 -1.0384041e-01 -5.9653349e-02]
Sparsity at: 0.03389887339055794
Epoch 65/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8159 - accuracy: 0.8999 - val_loss: 0.7992 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3751463e-02
 -1.0322722e-01 -5.9759129e-02]
Sparsity at: 0.03389887339055794
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8162 - accuracy: 0.8997 - val_loss: 0.7990 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3436848e-02
 -1.0306304e-01 -5.9770711e-02]
Sparsity at: 0.03389887339055794
Epoch 67/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.9000 - val_loss: 0.7995 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3260247e-02
 -1.0261369e-01 -5.9764374e-02]
Sparsity at: 0.03389887339055794
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.9000 - val_loss: 0.7993 - val_accuracy: 0.9029
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3172728e-02
 -1.0246329e-01 -5.9830181e-02]
Sparsity at: 0.03389887339055794
Epoch 69/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8158 - accuracy: 0.8998 - val_loss: 0.7987 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2952046e-02
 -1.0223703e-01 -6.0060523e-02]
Sparsity at: 0.03389887339055794
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.9005 - val_loss: 0.7998 - val_accuracy: 0.9028
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2847432e-02
 -1.0210761e-01 -5.9955411e-02]
Sparsity at: 0.03389887339055794
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8156 - accuracy: 0.9001 - val_loss: 0.8000 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2690706e-02
 -1.0215598e-01 -5.9946191e-02]
Sparsity at: 0.03389887339055794
Epoch 72/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.8998 - val_loss: 0.7999 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2593256e-02
 -1.0200527e-01 -6.0073420e-02]
Sparsity at: 0.03389887339055794
Epoch 73/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8158 - accuracy: 0.9000 - val_loss: 0.7997 - val_accuracy: 0.9035
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.25566882e-02
 -1.02058426e-01 -6.01961277e-02]
Sparsity at: 0.03389887339055794
Epoch 74/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8157 - accuracy: 0.9000 - val_loss: 0.7994 - val_accuracy: 0.9030
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2653352e-02
 -1.0220907e-01 -6.0107101e-02]
Sparsity at: 0.03389887339055794
Epoch 75/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8155 - accuracy: 0.9000 - val_loss: 0.7997 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2721152e-02
 -1.0218142e-01 -5.9989989e-02]
Sparsity at: 0.03389887339055794
Epoch 76/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8156 - accuracy: 0.8997 - val_loss: 0.7993 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2699050e-02
 -1.0206069e-01 -6.0101144e-02]
Sparsity at: 0.03389887339055794
Epoch 77/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8153 - accuracy: 0.9002 - val_loss: 0.7991 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2642426e-02
 -1.0209481e-01 -5.9778787e-02]
Sparsity at: 0.03389887339055794
Epoch 78/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8159 - accuracy: 0.9000 - val_loss: 0.7989 - val_accuracy: 0.9038
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.26993296e-02
 -1.02029786e-01 -5.97770475e-02]
Sparsity at: 0.03389887339055794
Epoch 79/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8155 - accuracy: 0.9002 - val_loss: 0.7997 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2686451e-02
 -1.0229172e-01 -5.9739932e-02]
Sparsity at: 0.03389887339055794
Epoch 80/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8155 - accuracy: 0.8999 - val_loss: 0.7991 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2758383e-02
 -1.0228651e-01 -5.9687842e-02]
Sparsity at: 0.03389887339055794
Epoch 81/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8155 - accuracy: 0.9000 - val_loss: 0.7994 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2721070e-02
 -1.0227813e-01 -5.9756547e-02]
Sparsity at: 0.03389887339055794
Epoch 82/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8156 - accuracy: 0.8999 - val_loss: 0.7983 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2667691e-02
 -1.0236472e-01 -5.9636872e-02]
Sparsity at: 0.03389887339055794
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8153 - accuracy: 0.9002 - val_loss: 0.7993 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2820834e-02
 -1.0246852e-01 -5.9721407e-02]
Sparsity at: 0.03389887339055794
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9003 - val_loss: 0.7994 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3027382e-02
 -1.0258234e-01 -5.9431273e-02]
Sparsity at: 0.03389887339055794
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8152 - accuracy: 0.9000 - val_loss: 0.7989 - val_accuracy: 0.9036
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.30842824e-02
 -1.02748506e-01 -5.93341328e-02]
Sparsity at: 0.03389887339055794
Epoch 86/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8152 - accuracy: 0.9003 - val_loss: 0.7986 - val_accuracy: 0.9041
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.32263575e-02
 -1.02651976e-01 -5.92290089e-02]
Sparsity at: 0.03389887339055794
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8152 - accuracy: 0.9002 - val_loss: 0.7989 - val_accuracy: 0.9037
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.30954695e-02
 -1.02805525e-01 -5.92073500e-02]
Sparsity at: 0.03389887339055794
Epoch 88/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.9001 - val_loss: 0.7987 - val_accuracy: 0.9035
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.29843776e-02
 -1.02934405e-01 -5.94149083e-02]
Sparsity at: 0.03389887339055794
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9001 - val_loss: 0.7984 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3267526e-02
 -1.0295344e-01 -5.9377521e-02]
Sparsity at: 0.03389887339055794
Epoch 90/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9005 - val_loss: 0.7988 - val_accuracy: 0.9034
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.31034379e-02
 -1.03300735e-01 -5.92039339e-02]
Sparsity at: 0.03389887339055794
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9003 - val_loss: 0.7988 - val_accuracy: 0.9041
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3195322e-02
 -1.0320137e-01 -5.9198733e-02]
Sparsity at: 0.03389887339055794
Epoch 92/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8152 - accuracy: 0.9001 - val_loss: 0.7991 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3269783e-02
 -1.0332963e-01 -5.9170473e-02]
Sparsity at: 0.03389887339055794
Epoch 93/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8151 - accuracy: 0.9003 - val_loss: 0.7981 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3193411e-02
 -1.0335717e-01 -5.9079077e-02]
Sparsity at: 0.03389887339055794
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8150 - accuracy: 0.9000 - val_loss: 0.7990 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3360170e-02
 -1.0341211e-01 -5.8922432e-02]
Sparsity at: 0.03389887339055794
Epoch 95/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8150 - accuracy: 0.9003 - val_loss: 0.7990 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3186929e-02
 -1.0355022e-01 -5.8969285e-02]
Sparsity at: 0.03389887339055794
Epoch 96/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8154 - accuracy: 0.8998 - val_loss: 0.7993 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3404993e-02
 -1.0351978e-01 -5.8893465e-02]
Sparsity at: 0.03389887339055794
Epoch 97/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8150 - accuracy: 0.9002 - val_loss: 0.7990 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3365113e-02
 -1.0374590e-01 -5.8799703e-02]
Sparsity at: 0.03389887339055794
Epoch 98/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9003 - val_loss: 0.7989 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3394301e-02
 -1.0380616e-01 -5.8410849e-02]
Sparsity at: 0.03389887339055794
Epoch 99/500
235/235 [==============================] - 2s 11ms/step - loss: 0.8152 - accuracy: 0.9001 - val_loss: 0.7988 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3228216e-02
 -1.0352213e-01 -5.8603521e-02]
Sparsity at: 0.03389887339055794
Epoch 100/500
235/235 [==============================] - 3s 12ms/step - loss: 0.8150 - accuracy: 0.9004 - val_loss: 0.7988 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3516897e-02
 -1.0359441e-01 -5.8454055e-02]
Sparsity at: 0.03389887339055794
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.014089003952281964
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.04218507345917066
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.13139845531020988
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 54s 8ms/step - loss: 0.8150 - accuracy: 0.9001 - val_loss: 0.7986 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3408722e-02
 -1.0365215e-01 -5.8270115e-02]
Sparsity at: 0.03389887339055794
Epoch 102/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.8999 - val_loss: 0.7984 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3493111e-02
 -1.0369851e-01 -5.8087692e-02]
Sparsity at: 0.03389887339055794
Epoch 103/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9002 - val_loss: 0.7990 - val_accuracy: 0.9034
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.35344800e-02
 -1.03766024e-01 -5.80144748e-02]
Sparsity at: 0.03389887339055794
Epoch 104/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8151 - accuracy: 0.9004 - val_loss: 0.7991 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3485872e-02
 -1.0388845e-01 -5.7728324e-02]
Sparsity at: 0.03389887339055794
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.9003 - val_loss: 0.7987 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3369964e-02
 -1.0382074e-01 -5.7680707e-02]
Sparsity at: 0.03389887339055794
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9002 - val_loss: 0.7987 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3414984e-02
 -1.0373279e-01 -5.7621069e-02]
Sparsity at: 0.03389887339055794
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9001 - val_loss: 0.7983 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3463595e-02
 -1.0389680e-01 -5.7223033e-02]
Sparsity at: 0.03389887339055794
Epoch 108/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.9002 - val_loss: 0.7981 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3469924e-02
 -1.0386919e-01 -5.7355382e-02]
Sparsity at: 0.03389887339055794
Epoch 109/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9004 - val_loss: 0.7986 - val_accuracy: 0.9030
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3421373e-02
 -1.0379905e-01 -5.7199921e-02]
Sparsity at: 0.03389887339055794
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.9002 - val_loss: 0.7993 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3324940e-02
 -1.0367604e-01 -5.7209872e-02]
Sparsity at: 0.03389887339055794
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9000 - val_loss: 0.7984 - val_accuracy: 0.9035
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.33785133e-02
 -1.03679314e-01 -5.68143949e-02]
Sparsity at: 0.03389887339055794
Epoch 112/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9000 - val_loss: 0.7981 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3366689e-02
 -1.0370591e-01 -5.7007808e-02]
Sparsity at: 0.03389887339055794
Epoch 113/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8147 - accuracy: 0.9005 - val_loss: 0.7982 - val_accuracy: 0.9030
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3441698e-02
 -1.0359780e-01 -5.6747593e-02]
Sparsity at: 0.03389887339055794
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9004 - val_loss: 0.7988 - val_accuracy: 0.9035
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.32605334e-02
 -1.03409335e-01 -5.69705740e-02]
Sparsity at: 0.03389887339055794
Epoch 115/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9000 - val_loss: 0.7982 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3318138e-02
 -1.0342982e-01 -5.6834307e-02]
Sparsity at: 0.03389887339055794
Epoch 116/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8148 - accuracy: 0.9002 - val_loss: 0.7986 - val_accuracy: 0.9034
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.32412885e-02
 -1.03304535e-01 -5.67218773e-02]
Sparsity at: 0.03389887339055794
Epoch 117/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8151 - accuracy: 0.8998 - val_loss: 0.7981 - val_accuracy: 0.9037
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.32308204e-02
 -1.03328034e-01 -5.65019250e-02]
Sparsity at: 0.03389887339055794
Epoch 118/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8148 - accuracy: 0.9003 - val_loss: 0.7983 - val_accuracy: 0.9034
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.30994667e-02
 -1.03185125e-01 -5.65388575e-02]
Sparsity at: 0.03389887339055794
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9003 - val_loss: 0.7980 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.3158479e-02
 -1.0296783e-01 -5.6642301e-02]
Sparsity at: 0.03389887339055794
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9005 - val_loss: 0.7982 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2970240e-02
 -1.0286489e-01 -5.6414004e-02]
Sparsity at: 0.03389887339055794
Epoch 121/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9003 - val_loss: 0.7985 - val_accuracy: 0.9036
[ 2.24728898e-34  3.00909656e-34  2.58297068e-34 ... -4.28457558e-02
 -1.02532186e-01 -5.65643236e-02]
Sparsity at: 0.03389887339055794
Epoch 122/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8150 - accuracy: 0.8999 - val_loss: 0.7985 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2795397e-02
 -1.0252886e-01 -5.6533631e-02]
Sparsity at: 0.03389887339055794
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2820655e-02
 -1.0224216e-01 -5.6592166e-02]
Sparsity at: 0.03389887339055794
Epoch 124/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9005 - val_loss: 0.7981 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2520795e-02
 -1.0203333e-01 -5.6734998e-02]
Sparsity at: 0.03389887339055794
Epoch 125/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8147 - accuracy: 0.9006 - val_loss: 0.7976 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2683408e-02
 -1.0183863e-01 -5.6465697e-02]
Sparsity at: 0.03389887339055794
Epoch 126/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9002 - val_loss: 0.7984 - val_accuracy: 0.9030
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2323146e-02
 -1.0170538e-01 -5.6575030e-02]
Sparsity at: 0.03389887339055794
Epoch 127/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9004 - val_loss: 0.7981 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2393982e-02
 -1.0149348e-01 -5.6534257e-02]
Sparsity at: 0.03389887339055794
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9006 - val_loss: 0.7979 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2290986e-02
 -1.0125504e-01 -5.6540426e-02]
Sparsity at: 0.03389887339055794
Epoch 129/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8147 - accuracy: 0.9003 - val_loss: 0.7981 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2168841e-02
 -1.0130440e-01 -5.6469820e-02]
Sparsity at: 0.03389887339055794
Epoch 130/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8148 - accuracy: 0.9001 - val_loss: 0.7989 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1968044e-02
 -1.0121643e-01 -5.6500975e-02]
Sparsity at: 0.03389887339055794
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7992 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.2103246e-02
 -1.0091434e-01 -5.6479193e-02]
Sparsity at: 0.03389887339055794
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8147 - accuracy: 0.9004 - val_loss: 0.7970 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1887861e-02
 -1.0072812e-01 -5.6370724e-02]
Sparsity at: 0.03389887339055794
Epoch 133/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1784987e-02
 -1.0064794e-01 -5.6207594e-02]
Sparsity at: 0.03389887339055794
Epoch 134/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9002 - val_loss: 0.7988 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1575938e-02
 -1.0050968e-01 -5.6128304e-02]
Sparsity at: 0.03389887339055794
Epoch 135/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9008 - val_loss: 0.7981 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1508704e-02
 -1.0034810e-01 -5.6251936e-02]
Sparsity at: 0.03389887339055794
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7986 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1482348e-02
 -1.0032443e-01 -5.5893302e-02]
Sparsity at: 0.03389887339055794
Epoch 137/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1265231e-02
 -1.0019071e-01 -5.5812761e-02]
Sparsity at: 0.03389887339055794
Epoch 138/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1203286e-02
 -1.0004001e-01 -5.5689316e-02]
Sparsity at: 0.03389887339055794
Epoch 139/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9004 - val_loss: 0.7980 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.1064810e-02
 -9.9953301e-02 -5.5468183e-02]
Sparsity at: 0.03389887339055794
Epoch 140/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9029
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0913761e-02
 -9.9871598e-02 -5.5456411e-02]
Sparsity at: 0.03389887339055794
Epoch 141/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9002 - val_loss: 0.7990 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0681038e-02
 -9.9681742e-02 -5.5142451e-02]
Sparsity at: 0.03389887339055794
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9002 - val_loss: 0.7983 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0787324e-02
 -9.9635944e-02 -5.4876503e-02]
Sparsity at: 0.03389887339055794
Epoch 143/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7968 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0774621e-02
 -9.9405967e-02 -5.4792445e-02]
Sparsity at: 0.03389887339055794
Epoch 144/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0682767e-02
 -9.9346727e-02 -5.4538336e-02]
Sparsity at: 0.03389887339055794
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7983 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0526766e-02
 -9.9196672e-02 -5.4513894e-02]
Sparsity at: 0.03389887339055794
Epoch 146/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9004 - val_loss: 0.7981 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0534776e-02
 -9.9095128e-02 -5.4254137e-02]
Sparsity at: 0.03389887339055794
Epoch 147/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7985 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0429071e-02
 -9.9093825e-02 -5.4120582e-02]
Sparsity at: 0.03389887339055794
Epoch 148/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9001 - val_loss: 0.7971 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0368181e-02
 -9.9069409e-02 -5.3941395e-02]
Sparsity at: 0.03389887339055794
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9005 - val_loss: 0.7980 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0155347e-02
 -9.8817952e-02 -5.3824235e-02]
Sparsity at: 0.03389887339055794
Epoch 150/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9041
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0206220e-02
 -9.8611146e-02 -5.3612702e-02]
Sparsity at: 0.03389887339055794
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.02029129779350991
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.05520869099208903
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.14918099394537432
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 52s 9ms/step - loss: 0.8143 - accuracy: 0.9004 - val_loss: 0.7977 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0160734e-02
 -9.8526441e-02 -5.3414274e-02]
Sparsity at: 0.03389887339055794
Epoch 152/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -4.0012572e-02
 -9.8373704e-02 -5.3168286e-02]
Sparsity at: 0.03389887339055794
Epoch 153/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7980 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9970174e-02
 -9.8315604e-02 -5.3018395e-02]
Sparsity at: 0.03389887339055794
Epoch 154/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8146 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9984159e-02
 -9.8079465e-02 -5.2911773e-02]
Sparsity at: 0.03389887339055794
Epoch 155/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9005 - val_loss: 0.7979 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9841775e-02
 -9.7797677e-02 -5.3076610e-02]
Sparsity at: 0.03389887339055794
Epoch 156/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9005 - val_loss: 0.7978 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9869893e-02
 -9.7771361e-02 -5.3057663e-02]
Sparsity at: 0.03389887339055794
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9005 - val_loss: 0.7979 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9757889e-02
 -9.7843446e-02 -5.2573107e-02]
Sparsity at: 0.03389887339055794
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9566200e-02
 -9.7679682e-02 -5.2691698e-02]
Sparsity at: 0.03389887339055794
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9004 - val_loss: 0.7976 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9520927e-02
 -9.7562119e-02 -5.2433077e-02]
Sparsity at: 0.03389887339055794
Epoch 160/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9531954e-02
 -9.7527303e-02 -5.2402634e-02]
Sparsity at: 0.03389887339055794
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9005 - val_loss: 0.7974 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9467428e-02
 -9.7221486e-02 -5.2435603e-02]
Sparsity at: 0.03389887339055794
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9006 - val_loss: 0.7976 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9368384e-02
 -9.7259119e-02 -5.2208368e-02]
Sparsity at: 0.03389887339055794
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9002 - val_loss: 0.7985 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9185010e-02
 -9.7304963e-02 -5.2055605e-02]
Sparsity at: 0.03389887339055794
Epoch 164/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9004 - val_loss: 0.7986 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9120469e-02
 -9.7124003e-02 -5.2002370e-02]
Sparsity at: 0.03389887339055794
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9173972e-02
 -9.7064294e-02 -5.1777918e-02]
Sparsity at: 0.03389887339055794
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.9044779e-02
 -9.6707635e-02 -5.1831044e-02]
Sparsity at: 0.03389887339055794
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9002 - val_loss: 0.7985 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8891442e-02
 -9.6584521e-02 -5.1631693e-02]
Sparsity at: 0.03389887339055794
Epoch 168/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8145 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8880162e-02
 -9.6602522e-02 -5.1566467e-02]
Sparsity at: 0.03389887339055794
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7978 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8936529e-02
 -9.6487880e-02 -5.1476356e-02]
Sparsity at: 0.03389887339055794
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9004 - val_loss: 0.7981 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8828190e-02
 -9.6309483e-02 -5.1377986e-02]
Sparsity at: 0.03389887339055794
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7982 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8618475e-02
 -9.6259847e-02 -5.1290415e-02]
Sparsity at: 0.03389887339055794
Epoch 172/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8145 - accuracy: 0.9008 - val_loss: 0.7971 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8620308e-02
 -9.6174836e-02 -5.1033225e-02]
Sparsity at: 0.03389887339055794
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8503442e-02
 -9.6053489e-02 -5.0986473e-02]
Sparsity at: 0.03389887339055794
Epoch 174/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9008 - val_loss: 0.7972 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8421802e-02
 -9.6070699e-02 -5.0773028e-02]
Sparsity at: 0.03389887339055794
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7980 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8304843e-02
 -9.5860213e-02 -5.0784688e-02]
Sparsity at: 0.03389887339055794
Epoch 176/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8256925e-02
 -9.5857024e-02 -5.0564509e-02]
Sparsity at: 0.03389887339055794
Epoch 177/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9042
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.8131539e-02
 -9.5654763e-02 -5.0429154e-02]
Sparsity at: 0.03389887339055794
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7858557e-02
 -9.5619597e-02 -5.0271366e-02]
Sparsity at: 0.03389887339055794
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7970 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7936624e-02
 -9.5671259e-02 -5.0046794e-02]
Sparsity at: 0.03389887339055794
Epoch 180/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9005 - val_loss: 0.7973 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7678573e-02
 -9.5665842e-02 -4.9962055e-02]
Sparsity at: 0.03389887339055794
Epoch 181/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7981 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7428129e-02
 -9.5617063e-02 -5.0066829e-02]
Sparsity at: 0.03389887339055794
Epoch 182/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7379395e-02
 -9.5496528e-02 -5.0057884e-02]
Sparsity at: 0.03389887339055794
Epoch 183/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7289985e-02
 -9.5511854e-02 -4.9875248e-02]
Sparsity at: 0.03389887339055794
Epoch 184/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7304468e-02
 -9.5596969e-02 -4.9739204e-02]
Sparsity at: 0.03389887339055794
Epoch 185/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9000 - val_loss: 0.7975 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7144955e-02
 -9.5311917e-02 -4.9604733e-02]
Sparsity at: 0.03389887339055794
Epoch 186/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6989454e-02
 -9.5435508e-02 -4.9652930e-02]
Sparsity at: 0.03389887339055794
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.7039138e-02
 -9.5328264e-02 -4.9320236e-02]
Sparsity at: 0.03389887339055794
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6990669e-02
 -9.5435224e-02 -4.9284574e-02]
Sparsity at: 0.03389887339055794
Epoch 189/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7968 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6846232e-02
 -9.5318988e-02 -4.9229257e-02]
Sparsity at: 0.03389887339055794
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6845025e-02
 -9.5394447e-02 -4.8909664e-02]
Sparsity at: 0.03389887339055794
Epoch 191/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6853984e-02
 -9.5421620e-02 -4.8937008e-02]
Sparsity at: 0.03389887339055794
Epoch 192/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6846444e-02
 -9.5321164e-02 -4.8895042e-02]
Sparsity at: 0.03389887339055794
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6788758e-02
 -9.5355622e-02 -4.8985101e-02]
Sparsity at: 0.03389887339055794
Epoch 194/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9005 - val_loss: 0.7973 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6745898e-02
 -9.5456250e-02 -4.8845507e-02]
Sparsity at: 0.03389887339055794
Epoch 195/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7974 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6742866e-02
 -9.5368162e-02 -4.8822910e-02]
Sparsity at: 0.03389887339055794
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6657874e-02
 -9.5273271e-02 -4.8721895e-02]
Sparsity at: 0.03389887339055794
Epoch 197/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7974 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6893822e-02
 -9.5330276e-02 -4.8658468e-02]
Sparsity at: 0.03389887339055794
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9007 - val_loss: 0.7970 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6702529e-02
 -9.5401332e-02 -4.8524633e-02]
Sparsity at: 0.03389887339055794
Epoch 199/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6791317e-02
 -9.5299415e-02 -4.8651472e-02]
Sparsity at: 0.03389887339055794
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6822926e-02
 -9.5364712e-02 -4.8633400e-02]
Sparsity at: 0.03389887339055794
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.02736453349347512
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.07399579934944
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.169265381781198
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 50s 8ms/step - loss: 0.8141 - accuracy: 0.9005 - val_loss: 0.7970 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6766224e-02
 -9.5349081e-02 -4.8700295e-02]
Sparsity at: 0.03389887339055794
Epoch 202/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6654163e-02
 -9.5388561e-02 -4.8548795e-02]
Sparsity at: 0.03389887339055794
Epoch 203/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6740389e-02
 -9.5344596e-02 -4.8460465e-02]
Sparsity at: 0.03389887339055794
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9041
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6682975e-02
 -9.5422961e-02 -4.8477147e-02]
Sparsity at: 0.03389887339055794
Epoch 205/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7975 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6712363e-02
 -9.5528968e-02 -4.8388049e-02]
Sparsity at: 0.03389887339055794
Epoch 206/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6666282e-02
 -9.5661275e-02 -4.8298709e-02]
Sparsity at: 0.03389887339055794
Epoch 207/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9002 - val_loss: 0.7970 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6647327e-02
 -9.5709555e-02 -4.8337694e-02]
Sparsity at: 0.03389887339055794
Epoch 208/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6614973e-02
 -9.5689550e-02 -4.8189010e-02]
Sparsity at: 0.03389887339055794
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6514372e-02
 -9.5547587e-02 -4.8356015e-02]
Sparsity at: 0.03389887339055794
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6545422e-02
 -9.5651612e-02 -4.8239220e-02]
Sparsity at: 0.03389887339055794
Epoch 211/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6518127e-02
 -9.5698588e-02 -4.8232540e-02]
Sparsity at: 0.03389887339055794
Epoch 212/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7969 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6425881e-02
 -9.5569089e-02 -4.8165847e-02]
Sparsity at: 0.03389887339055794
Epoch 213/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6330771e-02
 -9.5476292e-02 -4.8044495e-02]
Sparsity at: 0.03389887339055794
Epoch 214/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7983 - val_accuracy: 0.9029
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6186114e-02
 -9.5544897e-02 -4.8149280e-02]
Sparsity at: 0.03389887339055794
Epoch 215/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6222409e-02
 -9.5553547e-02 -4.8078544e-02]
Sparsity at: 0.03389887339055794
Epoch 216/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7967 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6257256e-02
 -9.5641248e-02 -4.7827017e-02]
Sparsity at: 0.03389887339055794
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7974 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6222503e-02
 -9.5413141e-02 -4.7789477e-02]
Sparsity at: 0.03389887339055794
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6229964e-02
 -9.5456518e-02 -4.7731601e-02]
Sparsity at: 0.03389887339055794
Epoch 219/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6161963e-02
 -9.5515124e-02 -4.7808003e-02]
Sparsity at: 0.03389887339055794
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6126863e-02
 -9.5618695e-02 -4.7855288e-02]
Sparsity at: 0.03389887339055794
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9006 - val_loss: 0.7970 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6202468e-02
 -9.5375992e-02 -4.7583513e-02]
Sparsity at: 0.03389887339055794
Epoch 222/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6060847e-02
 -9.5313169e-02 -4.7714949e-02]
Sparsity at: 0.03389887339055794
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6002871e-02
 -9.5425524e-02 -4.7506902e-02]
Sparsity at: 0.03389887339055794
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7976 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.6038239e-02
 -9.5601425e-02 -4.7517646e-02]
Sparsity at: 0.03389887339055794
Epoch 225/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9042
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5968818e-02
 -9.5511794e-02 -4.7478750e-02]
Sparsity at: 0.03389887339055794
Epoch 226/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5871066e-02
 -9.5570743e-02 -4.7512822e-02]
Sparsity at: 0.03389887339055794
Epoch 227/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7968 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5942599e-02
 -9.5443942e-02 -4.7278110e-02]
Sparsity at: 0.03389887339055794
Epoch 228/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5953950e-02
 -9.5320515e-02 -4.7337536e-02]
Sparsity at: 0.03389887339055794
Epoch 229/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7972 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5934608e-02
 -9.5209248e-02 -4.7216520e-02]
Sparsity at: 0.03389887339055794
Epoch 230/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5973743e-02
 -9.5165089e-02 -4.7222558e-02]
Sparsity at: 0.03389887339055794
Epoch 231/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5904784e-02
 -9.5220149e-02 -4.7137473e-02]
Sparsity at: 0.03389887339055794
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5810012e-02
 -9.5180489e-02 -4.7144707e-02]
Sparsity at: 0.03389887339055794
Epoch 233/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7975 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5889797e-02
 -9.5145136e-02 -4.7049131e-02]
Sparsity at: 0.03389887339055794
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5673074e-02
 -9.5019743e-02 -4.6992846e-02]
Sparsity at: 0.03389887339055794
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5785090e-02
 -9.5116653e-02 -4.6951301e-02]
Sparsity at: 0.03389887339055794
Epoch 236/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7981 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5729729e-02
 -9.4922632e-02 -4.6977296e-02]
Sparsity at: 0.03389887339055794
Epoch 237/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7964 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5872459e-02
 -9.4980769e-02 -4.6689376e-02]
Sparsity at: 0.03389887339055794
Epoch 238/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5715520e-02
 -9.5009916e-02 -4.6760783e-02]
Sparsity at: 0.03389887339055794
Epoch 239/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.8999 - val_loss: 0.7977 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5639398e-02
 -9.4968215e-02 -4.6792123e-02]
Sparsity at: 0.03389887339055794
Epoch 240/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7967 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5708439e-02
 -9.4937362e-02 -4.6704542e-02]
Sparsity at: 0.03389887339055794
Epoch 241/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5715658e-02
 -9.4963834e-02 -4.6583574e-02]
Sparsity at: 0.03389887339055794
Epoch 242/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5747267e-02
 -9.4868518e-02 -4.6438731e-02]
Sparsity at: 0.03389887339055794
Epoch 243/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7965 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5732713e-02
 -9.4895750e-02 -4.6354875e-02]
Sparsity at: 0.03389887339055794
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9007 - val_loss: 0.7975 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5870828e-02
 -9.4912007e-02 -4.6404488e-02]
Sparsity at: 0.03389887339055794
Epoch 245/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7975 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5899110e-02
 -9.4851337e-02 -4.6195492e-02]
Sparsity at: 0.03389887339055794
Epoch 246/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5635576e-02
 -9.4908312e-02 -4.6327610e-02]
Sparsity at: 0.03389887339055794
Epoch 247/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9007 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5510745e-02
 -9.4812930e-02 -4.6261530e-02]
Sparsity at: 0.03389887339055794
Epoch 248/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5489000e-02
 -9.4934434e-02 -4.6172898e-02]
Sparsity at: 0.03389887339055794
Epoch 249/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5617728e-02
 -9.4838835e-02 -4.6212889e-02]
Sparsity at: 0.03389887339055794
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7967 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5649642e-02
 -9.5029481e-02 -4.6063609e-02]
Sparsity at: 0.03389887339055794
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.035545293051153504
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.09501041010693445
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.18689646920878822
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 52s 8ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7969 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5617806e-02
 -9.5094390e-02 -4.6234921e-02]
Sparsity at: 0.03389887339055794
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5730913e-02
 -9.4931237e-02 -4.6082266e-02]
Sparsity at: 0.03389887339055794
Epoch 253/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5592459e-02
 -9.5175654e-02 -4.5908988e-02]
Sparsity at: 0.03389887339055794
Epoch 254/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7968 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5549644e-02
 -9.5256902e-02 -4.5850161e-02]
Sparsity at: 0.03389887339055794
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7967 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5488561e-02
 -9.5142990e-02 -4.6026327e-02]
Sparsity at: 0.03389887339055794
Epoch 256/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7969 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5485275e-02
 -9.5352359e-02 -4.5914993e-02]
Sparsity at: 0.03389887339055794
Epoch 257/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5459593e-02
 -9.5213458e-02 -4.5930501e-02]
Sparsity at: 0.03389887339055794
Epoch 258/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7967 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5578165e-02
 -9.5176637e-02 -4.5899615e-02]
Sparsity at: 0.03389887339055794
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7964 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5370272e-02
 -9.5058732e-02 -4.5882393e-02]
Sparsity at: 0.03389887339055794
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9001 - val_loss: 0.7966 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5681546e-02
 -9.5164001e-02 -4.5845345e-02]
Sparsity at: 0.03389887339055794
Epoch 261/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9001 - val_loss: 0.7975 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5568878e-02
 -9.5177382e-02 -4.5810193e-02]
Sparsity at: 0.03389887339055794
Epoch 262/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5556264e-02
 -9.5076583e-02 -4.5826472e-02]
Sparsity at: 0.03389887339055794
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5551034e-02
 -9.5224850e-02 -4.5927629e-02]
Sparsity at: 0.03389887339055794
Epoch 264/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7977 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5350829e-02
 -9.5211506e-02 -4.5852963e-02]
Sparsity at: 0.03389887339055794
Epoch 265/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5366494e-02
 -9.5223732e-02 -4.5784220e-02]
Sparsity at: 0.03389887339055794
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9005 - val_loss: 0.7970 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5338186e-02
 -9.5380858e-02 -4.5706812e-02]
Sparsity at: 0.03389887339055794
Epoch 267/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7965 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5397481e-02
 -9.5375113e-02 -4.5691725e-02]
Sparsity at: 0.03389887339055794
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9041
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5126481e-02
 -9.5453985e-02 -4.5792866e-02]
Sparsity at: 0.03389887339055794
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7979 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5170663e-02
 -9.5191173e-02 -4.5779787e-02]
Sparsity at: 0.03389887339055794
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7967 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5334084e-02
 -9.5455982e-02 -4.5629680e-02]
Sparsity at: 0.03389887339055794
Epoch 271/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5297435e-02
 -9.5394075e-02 -4.5442551e-02]
Sparsity at: 0.03389887339055794
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9030
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5194211e-02
 -9.5356166e-02 -4.5473021e-02]
Sparsity at: 0.03389887339055794
Epoch 273/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5214480e-02
 -9.5590666e-02 -4.5393050e-02]
Sparsity at: 0.03389887339055794
Epoch 274/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5112038e-02
 -9.5436752e-02 -4.5453407e-02]
Sparsity at: 0.03389887339055794
Epoch 275/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5072628e-02
 -9.5558852e-02 -4.5651820e-02]
Sparsity at: 0.03389887339055794
Epoch 276/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9007 - val_loss: 0.7967 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5105940e-02
 -9.5468014e-02 -4.5516830e-02]
Sparsity at: 0.03389887339055794
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7972 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5074644e-02
 -9.5516369e-02 -4.5503777e-02]
Sparsity at: 0.03389887339055794
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5044502e-02
 -9.5379956e-02 -4.5790471e-02]
Sparsity at: 0.03389887339055794
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7976 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5028566e-02
 -9.5406018e-02 -4.5601208e-02]
Sparsity at: 0.03389887339055794
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5066415e-02
 -9.5495582e-02 -4.5434110e-02]
Sparsity at: 0.03389887339055794
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7967 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5047494e-02
 -9.5373660e-02 -4.5322888e-02]
Sparsity at: 0.03389887339055794
Epoch 282/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7967 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5069466e-02
 -9.5422871e-02 -4.5361780e-02]
Sparsity at: 0.03389887339055794
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9005 - val_loss: 0.7964 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5249315e-02
 -9.5551983e-02 -4.5328576e-02]
Sparsity at: 0.03389887339055794
Epoch 284/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5007332e-02
 -9.5532939e-02 -4.5345385e-02]
Sparsity at: 0.03389887339055794
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5146143e-02
 -9.5632568e-02 -4.5161236e-02]
Sparsity at: 0.03389887339055794
Epoch 286/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7967 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5075072e-02
 -9.5724843e-02 -4.5253091e-02]
Sparsity at: 0.03389887339055794
Epoch 287/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7978 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5112120e-02
 -9.5643081e-02 -4.5250937e-02]
Sparsity at: 0.03389887339055794
Epoch 288/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4965958e-02
 -9.5800288e-02 -4.5286123e-02]
Sparsity at: 0.03389887339055794
Epoch 289/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.8999 - val_loss: 0.7962 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5031259e-02
 -9.5649160e-02 -4.5255616e-02]
Sparsity at: 0.03389887339055794
Epoch 290/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7970 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5092145e-02
 -9.5739923e-02 -4.5327332e-02]
Sparsity at: 0.03389887339055794
Epoch 291/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4978494e-02
 -9.5722571e-02 -4.5004915e-02]
Sparsity at: 0.03389887339055794
Epoch 292/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7976 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4856971e-02
 -9.5970541e-02 -4.5212328e-02]
Sparsity at: 0.03389887339055794
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7971 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5053559e-02
 -9.5789202e-02 -4.5266882e-02]
Sparsity at: 0.03389887339055794
Epoch 294/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.5143219e-02
 -9.5957994e-02 -4.5177188e-02]
Sparsity at: 0.03389887339055794
Epoch 295/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4843873e-02
 -9.5676102e-02 -4.5376319e-02]
Sparsity at: 0.03389887339055794
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7980 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4879837e-02
 -9.5855080e-02 -4.5196690e-02]
Sparsity at: 0.03389887339055794
Epoch 297/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7969 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4731772e-02
 -9.5909670e-02 -4.5119233e-02]
Sparsity at: 0.03389887339055794
Epoch 298/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.8999 - val_loss: 0.7975 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4756761e-02
 -9.6019350e-02 -4.5176577e-02]
Sparsity at: 0.03389887339055794
Epoch 299/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7973 - val_accuracy: 0.9041
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4808137e-02
 -9.6014977e-02 -4.5063160e-02]
Sparsity at: 0.03389887339055794
Epoch 300/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4819629e-02
 -9.6085303e-02 -4.5012720e-02]
Sparsity at: 0.03389887339055794
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.04435477734010984
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.11068923326743096
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.19937929907021257
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 51s 8ms/step - loss: 0.8143 - accuracy: 0.9000 - val_loss: 0.7962 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4797397e-02
 -9.5800512e-02 -4.4861566e-02]
Sparsity at: 0.03389887339055794
Epoch 302/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4943394e-02
 -9.6041270e-02 -4.4934463e-02]
Sparsity at: 0.03389887339055794
Epoch 303/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4885950e-02
 -9.5935509e-02 -4.4857688e-02]
Sparsity at: 0.03389887339055794
Epoch 304/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4726981e-02
 -9.6023098e-02 -4.4909544e-02]
Sparsity at: 0.03389887339055794
Epoch 305/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4916848e-02
 -9.6007399e-02 -4.4866450e-02]
Sparsity at: 0.03389887339055794
Epoch 306/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7969 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4770831e-02
 -9.6156009e-02 -4.4731978e-02]
Sparsity at: 0.03389887339055794
Epoch 307/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7964 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4854479e-02
 -9.6200190e-02 -4.4606078e-02]
Sparsity at: 0.03389887339055794
Epoch 308/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7969 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4713868e-02
 -9.6203551e-02 -4.4499710e-02]
Sparsity at: 0.03389887339055794
Epoch 309/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4713618e-02
 -9.6233115e-02 -4.4924371e-02]
Sparsity at: 0.03389887339055794
Epoch 310/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7964 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4718137e-02
 -9.6289963e-02 -4.4647429e-02]
Sparsity at: 0.03389887339055794
Epoch 311/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4685895e-02
 -9.6370988e-02 -4.4619747e-02]
Sparsity at: 0.03389887339055794
Epoch 312/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4652330e-02
 -9.6495420e-02 -4.4804323e-02]
Sparsity at: 0.03389887339055794
Epoch 313/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7974 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4587249e-02
 -9.6361816e-02 -4.4753689e-02]
Sparsity at: 0.03389887339055794
Epoch 314/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7978 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4594022e-02
 -9.6612059e-02 -4.4690166e-02]
Sparsity at: 0.03389887339055794
Epoch 315/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4554962e-02
 -9.6487179e-02 -4.4727668e-02]
Sparsity at: 0.03389887339055794
Epoch 316/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7978 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4506112e-02
 -9.6533008e-02 -4.4648893e-02]
Sparsity at: 0.03389887339055794
Epoch 317/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7974 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4443866e-02
 -9.6589275e-02 -4.4686474e-02]
Sparsity at: 0.03389887339055794
Epoch 318/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4492228e-02
 -9.6707433e-02 -4.4591308e-02]
Sparsity at: 0.03389887339055794
Epoch 319/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.8999 - val_loss: 0.7974 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4442347e-02
 -9.6671715e-02 -4.4693373e-02]
Sparsity at: 0.03389887339055794
Epoch 320/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4525141e-02
 -9.6611209e-02 -4.4650257e-02]
Sparsity at: 0.03389887339055794
Epoch 321/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8137 - accuracy: 0.9004 - val_loss: 0.7976 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4595732e-02
 -9.6739486e-02 -4.4451512e-02]
Sparsity at: 0.03389887339055794
Epoch 322/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4630559e-02
 -9.6848808e-02 -4.4497199e-02]
Sparsity at: 0.03389887339055794
Epoch 323/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7975 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4547932e-02
 -9.6909791e-02 -4.4492215e-02]
Sparsity at: 0.03389887339055794
Epoch 324/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7968 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4590151e-02
 -9.6832573e-02 -4.4349544e-02]
Sparsity at: 0.03389887339055794
Epoch 325/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4474447e-02
 -9.6833356e-02 -4.4484090e-02]
Sparsity at: 0.03389887339055794
Epoch 326/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7974 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4530651e-02
 -9.6835099e-02 -4.4366024e-02]
Sparsity at: 0.03389887339055794
Epoch 327/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7973 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4442931e-02
 -9.6881233e-02 -4.4429567e-02]
Sparsity at: 0.03389887339055794
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7982 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4487471e-02
 -9.6792482e-02 -4.4422358e-02]
Sparsity at: 0.03389887339055794
Epoch 329/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.8999 - val_loss: 0.7976 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4553282e-02
 -9.6873365e-02 -4.4153761e-02]
Sparsity at: 0.03389887339055794
Epoch 330/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.8999 - val_loss: 0.7974 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4503222e-02
 -9.6902415e-02 -4.4259042e-02]
Sparsity at: 0.03389887339055794
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7974 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4294885e-02
 -9.6824251e-02 -4.4309717e-02]
Sparsity at: 0.03389887339055794
Epoch 332/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7975 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4381229e-02
 -9.6809439e-02 -4.4335492e-02]
Sparsity at: 0.03389887339055794
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4424666e-02
 -9.7084351e-02 -4.4255201e-02]
Sparsity at: 0.03389887339055794
Epoch 334/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4483191e-02
 -9.7174242e-02 -4.4148780e-02]
Sparsity at: 0.03389887339055794
Epoch 335/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9001 - val_loss: 0.7970 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4550045e-02
 -9.7047716e-02 -4.3985836e-02]
Sparsity at: 0.03389887339055794
Epoch 336/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7980 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4406908e-02
 -9.7309612e-02 -4.3992408e-02]
Sparsity at: 0.03389887339055794
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7965 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4412827e-02
 -9.7169854e-02 -4.4006787e-02]
Sparsity at: 0.03389887339055794
Epoch 338/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7979 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4341406e-02
 -9.7310677e-02 -4.3965001e-02]
Sparsity at: 0.03389887339055794
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7979 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4288958e-02
 -9.7450174e-02 -4.3762885e-02]
Sparsity at: 0.03389887339055794
Epoch 340/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4331642e-02
 -9.7471438e-02 -4.3890771e-02]
Sparsity at: 0.03389887339055794
Epoch 341/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4296717e-02
 -9.7389624e-02 -4.3782044e-02]
Sparsity at: 0.03389887339055794
Epoch 342/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4141175e-02
 -9.7261921e-02 -4.4168811e-02]
Sparsity at: 0.03389887339055794
Epoch 343/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7975 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4265943e-02
 -9.7186625e-02 -4.3805435e-02]
Sparsity at: 0.03389887339055794
Epoch 344/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4208931e-02
 -9.7442105e-02 -4.3690924e-02]
Sparsity at: 0.03389887339055794
Epoch 345/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4246147e-02
 -9.7375527e-02 -4.3946978e-02]
Sparsity at: 0.03389887339055794
Epoch 346/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7969 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4285273e-02
 -9.7365528e-02 -4.3918546e-02]
Sparsity at: 0.03389887339055794
Epoch 347/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7975 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4364570e-02
 -9.7446091e-02 -4.3807507e-02]
Sparsity at: 0.03389887339055794
Epoch 348/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7967 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4288522e-02
 -9.7659193e-02 -4.3912929e-02]
Sparsity at: 0.03389887339055794
Epoch 349/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4248292e-02
 -9.7485386e-02 -4.3722317e-02]
Sparsity at: 0.03389887339055794
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7965 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4196287e-02
 -9.7396024e-02 -4.3715805e-02]
Sparsity at: 0.03389887339055794
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.052743863487109355
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.12429208335510111
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.21417686976981543
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 51s 8ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7966 - val_accuracy: 0.9042
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4352012e-02
 -9.7539194e-02 -4.3598451e-02]
Sparsity at: 0.03389887339055794
Epoch 352/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7968 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4314599e-02
 -9.7610652e-02 -4.3607969e-02]
Sparsity at: 0.03389887339055794
Epoch 353/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4292400e-02
 -9.7494572e-02 -4.3581631e-02]
Sparsity at: 0.03389887339055794
Epoch 354/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9000 - val_loss: 0.7974 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4235049e-02
 -9.7610846e-02 -4.3565672e-02]
Sparsity at: 0.03389887339055794
Epoch 355/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7985 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4263253e-02
 -9.7436532e-02 -4.3598924e-02]
Sparsity at: 0.03389887339055794
Epoch 356/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7973 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4184940e-02
 -9.7340889e-02 -4.3624602e-02]
Sparsity at: 0.03389887339055794
Epoch 357/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7967 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4227826e-02
 -9.7506076e-02 -4.3547548e-02]
Sparsity at: 0.03389887339055794
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4216747e-02
 -9.7449049e-02 -4.3502424e-02]
Sparsity at: 0.03389887339055794
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7970 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4172311e-02
 -9.7490683e-02 -4.3405849e-02]
Sparsity at: 0.03389887339055794
Epoch 360/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7973 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4276269e-02
 -9.7525246e-02 -4.3468878e-02]
Sparsity at: 0.03389887339055794
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4174651e-02
 -9.7377978e-02 -4.3271024e-02]
Sparsity at: 0.03389887339055794
Epoch 362/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4285706e-02
 -9.7638935e-02 -4.3281332e-02]
Sparsity at: 0.03389887339055794
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7974 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4212306e-02
 -9.7392350e-02 -4.3429457e-02]
Sparsity at: 0.03389887339055794
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4162395e-02
 -9.7629458e-02 -4.3191437e-02]
Sparsity at: 0.03389887339055794
Epoch 365/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4181096e-02
 -9.7734824e-02 -4.3249957e-02]
Sparsity at: 0.03389887339055794
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7966 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4099434e-02
 -9.7754508e-02 -4.3237682e-02]
Sparsity at: 0.03389887339055794
Epoch 367/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7965 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4158781e-02
 -9.7804271e-02 -4.3223567e-02]
Sparsity at: 0.03389887339055794
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4111638e-02
 -9.7762577e-02 -4.3182202e-02]
Sparsity at: 0.03389887339055794
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9005 - val_loss: 0.7967 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4281168e-02
 -9.7769648e-02 -4.3119546e-02]
Sparsity at: 0.03389887339055794
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4193531e-02
 -9.7666502e-02 -4.3246772e-02]
Sparsity at: 0.03389887339055794
Epoch 371/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4162913e-02
 -9.7703449e-02 -4.3131862e-02]
Sparsity at: 0.03389887339055794
Epoch 372/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4065649e-02
 -9.7926319e-02 -4.3108523e-02]
Sparsity at: 0.03389887339055794
Epoch 373/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7976 - val_accuracy: 0.9030
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4127947e-02
 -9.7778030e-02 -4.3077670e-02]
Sparsity at: 0.03389887339055794
Epoch 374/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8144 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4074351e-02
 -9.7829141e-02 -4.3011408e-02]
Sparsity at: 0.03389887339055794
Epoch 375/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4054548e-02
 -9.7785011e-02 -4.2790920e-02]
Sparsity at: 0.03389887339055794
Epoch 376/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7973 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4001186e-02
 -9.7881466e-02 -4.3031055e-02]
Sparsity at: 0.03389887339055794
Epoch 377/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.8999 - val_loss: 0.7971 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3961233e-02
 -9.7930126e-02 -4.2864677e-02]
Sparsity at: 0.03389887339055794
Epoch 378/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4112930e-02
 -9.7780302e-02 -4.2862244e-02]
Sparsity at: 0.03389887339055794
Epoch 379/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4011547e-02
 -9.7965978e-02 -4.3037977e-02]
Sparsity at: 0.03389887339055794
Epoch 380/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7968 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3951871e-02
 -9.7891696e-02 -4.3034099e-02]
Sparsity at: 0.03389887339055794
Epoch 381/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3959676e-02
 -9.8005764e-02 -4.2922460e-02]
Sparsity at: 0.03389887339055794
Epoch 382/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4084368e-02
 -9.7997084e-02 -4.3019287e-02]
Sparsity at: 0.03389887339055794
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4002177e-02
 -9.8031886e-02 -4.2913541e-02]
Sparsity at: 0.03389887339055794
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4053478e-02
 -9.7856842e-02 -4.2811599e-02]
Sparsity at: 0.03389887339055794
Epoch 385/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7978 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3933617e-02
 -9.8102175e-02 -4.2863261e-02]
Sparsity at: 0.03389887339055794
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3994645e-02
 -9.7985752e-02 -4.2854995e-02]
Sparsity at: 0.03389887339055794
Epoch 387/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3935145e-02
 -9.8022252e-02 -4.2910121e-02]
Sparsity at: 0.03389887339055794
Epoch 388/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7967 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3961538e-02
 -9.8096751e-02 -4.2805966e-02]
Sparsity at: 0.03389887339055794
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7975 - val_accuracy: 0.9030
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3820871e-02
 -9.8180383e-02 -4.2728234e-02]
Sparsity at: 0.03389887339055794
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7969 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3863250e-02
 -9.8316744e-02 -4.2488240e-02]
Sparsity at: 0.03389887339055794
Epoch 391/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4028519e-02
 -9.8252214e-02 -4.2500339e-02]
Sparsity at: 0.03389887339055794
Epoch 392/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7970 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3830155e-02
 -9.8116055e-02 -4.2651556e-02]
Sparsity at: 0.03389887339055794
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7975 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3948086e-02
 -9.8080926e-02 -4.2738486e-02]
Sparsity at: 0.03389887339055794
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.8998 - val_loss: 0.7978 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3788186e-02
 -9.8295294e-02 -4.2450201e-02]
Sparsity at: 0.03389887339055794
Epoch 395/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9007 - val_loss: 0.7967 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3931240e-02
 -9.8315232e-02 -4.2527422e-02]
Sparsity at: 0.03389887339055794
Epoch 396/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.4009095e-02
 -9.8474383e-02 -4.2499047e-02]
Sparsity at: 0.03389887339055794
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3984900e-02
 -9.8390952e-02 -4.2577066e-02]
Sparsity at: 0.03389887339055794
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7977 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3980556e-02
 -9.8338909e-02 -4.2471454e-02]
Sparsity at: 0.03389887339055794
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7971 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3913329e-02
 -9.8397359e-02 -4.2382736e-02]
Sparsity at: 0.03389887339055794
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3771519e-02
 -9.8473676e-02 -4.2455129e-02]
Sparsity at: 0.03389887339055794
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.057099786329344315
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.1306709387685725
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.22291756162696963
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 52s 8ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3895817e-02
 -9.8488562e-02 -4.2521272e-02]
Sparsity at: 0.03389887339055794
Epoch 402/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8138 - accuracy: 0.9001 - val_loss: 0.7973 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3841401e-02
 -9.8517023e-02 -4.2242482e-02]
Sparsity at: 0.03389887339055794
Epoch 403/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7971 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3847246e-02
 -9.8482326e-02 -4.2353295e-02]
Sparsity at: 0.03389887339055794
Epoch 404/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8137 - accuracy: 0.9007 - val_loss: 0.7979 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3787936e-02
 -9.8648608e-02 -4.2141754e-02]
Sparsity at: 0.03389887339055794
Epoch 405/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7965 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3911116e-02
 -9.8407306e-02 -4.2297687e-02]
Sparsity at: 0.03389887339055794
Epoch 406/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3867877e-02
 -9.8565169e-02 -4.2211983e-02]
Sparsity at: 0.03389887339055794
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8142 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3771940e-02
 -9.8551877e-02 -4.2215295e-02]
Sparsity at: 0.03389887339055794
Epoch 408/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3729360e-02
 -9.8579481e-02 -4.2312663e-02]
Sparsity at: 0.03389887339055794
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8136 - accuracy: 0.9001 - val_loss: 0.7970 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3793513e-02
 -9.8539606e-02 -4.2214945e-02]
Sparsity at: 0.03389887339055794
Epoch 410/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7967 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3695366e-02
 -9.8623827e-02 -4.2219795e-02]
Sparsity at: 0.03389887339055794
Epoch 411/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9000 - val_loss: 0.7974 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3801686e-02
 -9.8452963e-02 -4.2066209e-02]
Sparsity at: 0.03389887339055794
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3612847e-02
 -9.8672025e-02 -4.2159382e-02]
Sparsity at: 0.03389887339055794
Epoch 413/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7986 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3461779e-02
 -9.8621055e-02 -4.2199086e-02]
Sparsity at: 0.03389887339055794
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7969 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3701170e-02
 -9.8626263e-02 -4.2190455e-02]
Sparsity at: 0.03389887339055794
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3752497e-02
 -9.8701462e-02 -4.2082854e-02]
Sparsity at: 0.03389887339055794
Epoch 416/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.8999 - val_loss: 0.7965 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3724170e-02
 -9.8552339e-02 -4.1962158e-02]
Sparsity at: 0.03389887339055794
Epoch 417/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7973 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3672780e-02
 -9.8629236e-02 -4.2007413e-02]
Sparsity at: 0.03389887339055794
Epoch 418/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.8997 - val_loss: 0.7983 - val_accuracy: 0.9028
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3397265e-02
 -9.8585725e-02 -4.2155094e-02]
Sparsity at: 0.03389887339055794
Epoch 419/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7976 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3515714e-02
 -9.8487645e-02 -4.1888595e-02]
Sparsity at: 0.03389887339055794
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7968 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3506636e-02
 -9.8538473e-02 -4.1954197e-02]
Sparsity at: 0.03389887339055794
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9005 - val_loss: 0.7978 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3627063e-02
 -9.8559923e-02 -4.1992247e-02]
Sparsity at: 0.03389887339055794
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7969 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3643913e-02
 -9.8599210e-02 -4.1897912e-02]
Sparsity at: 0.03389887339055794
Epoch 423/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7962 - val_accuracy: 0.9041
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3565946e-02
 -9.8294690e-02 -4.1929375e-02]
Sparsity at: 0.03389887339055794
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7981 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3456821e-02
 -9.8460555e-02 -4.1839413e-02]
Sparsity at: 0.03389887339055794
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3577211e-02
 -9.8268174e-02 -4.1835126e-02]
Sparsity at: 0.03389887339055794
Epoch 426/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7974 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3537619e-02
 -9.8361425e-02 -4.2015020e-02]
Sparsity at: 0.03389887339055794
Epoch 427/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7975 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3623248e-02
 -9.8630942e-02 -4.1822635e-02]
Sparsity at: 0.03389887339055794
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7976 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3484727e-02
 -9.8556668e-02 -4.1832197e-02]
Sparsity at: 0.03389887339055794
Epoch 429/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3484090e-02
 -9.8386228e-02 -4.1712198e-02]
Sparsity at: 0.03389887339055794
Epoch 430/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3183515e-02
 -9.8379575e-02 -4.2001665e-02]
Sparsity at: 0.03389887339055794
Epoch 431/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7966 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3385403e-02
 -9.8537073e-02 -4.1758124e-02]
Sparsity at: 0.03389887339055794
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7965 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3382837e-02
 -9.8590985e-02 -4.1638099e-02]
Sparsity at: 0.03389887339055794
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3401709e-02
 -9.8427467e-02 -4.1817617e-02]
Sparsity at: 0.03389887339055794
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7977 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3349462e-02
 -9.8614946e-02 -4.1713927e-02]
Sparsity at: 0.03389887339055794
Epoch 435/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3443537e-02
 -9.8493300e-02 -4.1573796e-02]
Sparsity at: 0.03389887339055794
Epoch 436/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7979 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3224288e-02
 -9.8619498e-02 -4.1613799e-02]
Sparsity at: 0.03389887339055794
Epoch 437/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3298600e-02
 -9.8531902e-02 -4.1629866e-02]
Sparsity at: 0.03389887339055794
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3341009e-02
 -9.8537236e-02 -4.1511521e-02]
Sparsity at: 0.03389887339055794
Epoch 439/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8135 - accuracy: 0.9004 - val_loss: 0.7980 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3372898e-02
 -9.8537736e-02 -4.1489102e-02]
Sparsity at: 0.03389887339055794
Epoch 440/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3253733e-02
 -9.8515213e-02 -4.1431401e-02]
Sparsity at: 0.03389887339055794
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7971 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3296064e-02
 -9.8250359e-02 -4.1552152e-02]
Sparsity at: 0.03389887339055794
Epoch 442/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7976 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3168200e-02
 -9.8508604e-02 -4.1460197e-02]
Sparsity at: 0.03389887339055794
Epoch 443/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.8999 - val_loss: 0.7977 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3131447e-02
 -9.8569706e-02 -4.1339118e-02]
Sparsity at: 0.03389887339055794
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7980 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3171963e-02
 -9.8383456e-02 -4.1247256e-02]
Sparsity at: 0.03389887339055794
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8139 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3127006e-02
 -9.8380260e-02 -4.1312773e-02]
Sparsity at: 0.03389887339055794
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3022691e-02
 -9.8342314e-02 -4.1346565e-02]
Sparsity at: 0.03389887339055794
Epoch 447/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7968 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.3103276e-02
 -9.8171256e-02 -4.1225247e-02]
Sparsity at: 0.03389887339055794
Epoch 448/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.8995 - val_loss: 0.7977 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2863699e-02
 -9.8137617e-02 -4.1382264e-02]
Sparsity at: 0.03389887339055794
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9031
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2875180e-02
 -9.7793140e-02 -4.1399263e-02]
Sparsity at: 0.03389887339055794
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2995105e-02
 -9.7972013e-02 -4.1317664e-02]
Sparsity at: 0.03389887339055794
Epoch 451/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7967 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2796476e-02
 -9.8173089e-02 -4.1315712e-02]
Sparsity at: 0.03389887339055794
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9034
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2771200e-02
 -9.7888947e-02 -4.1428145e-02]
Sparsity at: 0.03389887339055794
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2825306e-02
 -9.7943068e-02 -4.1350365e-02]
Sparsity at: 0.03389887339055794
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2773655e-02
 -9.7861797e-02 -4.1438095e-02]
Sparsity at: 0.03389887339055794
Epoch 455/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7982 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2560099e-02
 -9.7882271e-02 -4.1324764e-02]
Sparsity at: 0.03389887339055794
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7982 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2684274e-02
 -9.7680338e-02 -4.1447867e-02]
Sparsity at: 0.03389887339055794
Epoch 457/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.8999 - val_loss: 0.7980 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2584418e-02
 -9.7736590e-02 -4.1477330e-02]
Sparsity at: 0.03389887339055794
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8143 - accuracy: 0.9001 - val_loss: 0.7970 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2609370e-02
 -9.7734906e-02 -4.1320972e-02]
Sparsity at: 0.03389887339055794
Epoch 459/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2586563e-02
 -9.7586520e-02 -4.1410767e-02]
Sparsity at: 0.03389887339055794
Epoch 460/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7979 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2380771e-02
 -9.7531818e-02 -4.1558821e-02]
Sparsity at: 0.03389887339055794
Epoch 461/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2592177e-02
 -9.7453482e-02 -4.1687291e-02]
Sparsity at: 0.03389887339055794
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7973 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2498971e-02
 -9.7383112e-02 -4.1759025e-02]
Sparsity at: 0.03389887339055794
Epoch 463/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2485206e-02
 -9.7343452e-02 -4.1740205e-02]
Sparsity at: 0.03389887339055794
Epoch 464/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9000 - val_loss: 0.7975 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2445662e-02
 -9.7372174e-02 -4.1723810e-02]
Sparsity at: 0.03389887339055794
Epoch 465/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7966 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2429863e-02
 -9.7428471e-02 -4.1692205e-02]
Sparsity at: 0.03389887339055794
Epoch 466/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7980 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2395758e-02
 -9.7465284e-02 -4.1868746e-02]
Sparsity at: 0.03389887339055794
Epoch 467/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.8999 - val_loss: 0.7969 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2290079e-02
 -9.7259723e-02 -4.1966259e-02]
Sparsity at: 0.03389887339055794
Epoch 468/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2357518e-02
 -9.7218126e-02 -4.1879151e-02]
Sparsity at: 0.03389887339055794
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2468431e-02
 -9.7285219e-02 -4.1787252e-02]
Sparsity at: 0.03389887339055794
Epoch 470/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2481883e-02
 -9.7273722e-02 -4.1809745e-02]
Sparsity at: 0.03389887339055794
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2374643e-02
 -9.7160019e-02 -4.1899446e-02]
Sparsity at: 0.03389887339055794
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7972 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2324605e-02
 -9.7227447e-02 -4.1919228e-02]
Sparsity at: 0.03389887339055794
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.8998 - val_loss: 0.7977 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2324005e-02
 -9.7026393e-02 -4.2112291e-02]
Sparsity at: 0.03389887339055794
Epoch 474/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9040
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2257445e-02
 -9.7084872e-02 -4.1847751e-02]
Sparsity at: 0.03389887339055794
Epoch 475/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9000 - val_loss: 0.7978 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2258838e-02
 -9.7053260e-02 -4.2122334e-02]
Sparsity at: 0.03389887339055794
Epoch 476/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7977 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2405175e-02
 -9.7091451e-02 -4.1951727e-02]
Sparsity at: 0.03389887339055794
Epoch 477/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7965 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2385971e-02
 -9.7107440e-02 -4.1911151e-02]
Sparsity at: 0.03389887339055794
Epoch 478/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7981 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2050751e-02
 -9.7092815e-02 -4.2159550e-02]
Sparsity at: 0.03389887339055794
Epoch 479/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2291226e-02
 -9.7128980e-02 -4.2024780e-02]
Sparsity at: 0.03389887339055794
Epoch 480/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2265317e-02
 -9.7074740e-02 -4.2164955e-02]
Sparsity at: 0.03389887339055794
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2336153e-02
 -9.7119838e-02 -4.2179491e-02]
Sparsity at: 0.03389887339055794
Epoch 482/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9032
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2358196e-02
 -9.7241424e-02 -4.2186491e-02]
Sparsity at: 0.03389887339055794
Epoch 483/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7972 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2261532e-02
 -9.7131863e-02 -4.2098816e-02]
Sparsity at: 0.03389887339055794
Epoch 484/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2360382e-02
 -9.7139090e-02 -4.2085752e-02]
Sparsity at: 0.03389887339055794
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7978 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2124233e-02
 -9.7216226e-02 -4.2437308e-02]
Sparsity at: 0.03389887339055794
Epoch 486/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7987 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2250691e-02
 -9.7064272e-02 -4.2238608e-02]
Sparsity at: 0.03389887339055794
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7978 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2165430e-02
 -9.6924998e-02 -4.2306665e-02]
Sparsity at: 0.03389887339055794
Epoch 488/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7972 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2230306e-02
 -9.6959658e-02 -4.2463355e-02]
Sparsity at: 0.03389887339055794
Epoch 489/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9037
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2372519e-02
 -9.7048096e-02 -4.2283148e-02]
Sparsity at: 0.03389887339055794
Epoch 490/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2220703e-02
 -9.7035661e-02 -4.2394150e-02]
Sparsity at: 0.03389887339055794
Epoch 491/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2222286e-02
 -9.6831806e-02 -4.2429715e-02]
Sparsity at: 0.03389887339055794
Epoch 492/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2252103e-02
 -9.6830562e-02 -4.2398795e-02]
Sparsity at: 0.03389887339055794
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2231782e-02
 -9.6809991e-02 -4.2366639e-02]
Sparsity at: 0.03389887339055794
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7977 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2308962e-02
 -9.6736394e-02 -4.2408559e-02]
Sparsity at: 0.03389887339055794
Epoch 495/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.8999 - val_loss: 0.7980 - val_accuracy: 0.9035
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2263845e-02
 -9.6778102e-02 -4.2350445e-02]
Sparsity at: 0.03389887339055794
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7978 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2086045e-02
 -9.6758753e-02 -4.2493511e-02]
Sparsity at: 0.03389887339055794
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9033
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2205243e-02
 -9.6739762e-02 -4.2327952e-02]
Sparsity at: 0.03389887339055794
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9038
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2306924e-02
 -9.6956059e-02 -4.2436440e-02]
Sparsity at: 0.03389887339055794
Epoch 499/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.8999 - val_loss: 0.7969 - val_accuracy: 0.9036
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2242112e-02
 -9.6948199e-02 -4.2466994e-02]
Sparsity at: 0.03389887339055794
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7964 - val_accuracy: 0.9039
[ 2.2472890e-34  3.0090966e-34  2.5829707e-34 ... -3.2124970e-02
 -9.6946724e-02 -4.2541802e-02]
Sparsity at: 0.03389887339055794
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.041994860395789146
Thresholhold -0.05940218269824982
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.09000259265303612
Thresholhold 0.044793546199798584
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10164112225174904
Thresholhold -0.008578553795814514
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 59:01 - loss: 2.3990 - accuracy: 0.1016WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_begin` time: 2.4637s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 0.4928 - accuracy: 0.8662 - val_loss: 0.2552 - val_accuracy: 0.9237
[-0.05940218 -0.00601314 -0.04628057 ...  0.03355239  0.10355379
 -0.05929007]
Sparsity at: 0.03389887339055794
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2327 - accuracy: 0.9321 - val_loss: 0.1939 - val_accuracy: 0.9421
[-0.05940218 -0.00601314 -0.04628057 ...  0.04306844  0.127009
 -0.09002554]
Sparsity at: 0.03389887339055794
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1780 - accuracy: 0.9482 - val_loss: 0.1591 - val_accuracy: 0.9533
[-0.05940218 -0.00601314 -0.04628057 ...  0.05200673  0.14935198
 -0.11586972]
Sparsity at: 0.03389887339055794
Epoch 4/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1431 - accuracy: 0.9583 - val_loss: 0.1387 - val_accuracy: 0.9583
[-0.05940218 -0.00601314 -0.04628057 ...  0.05814288  0.16725424
 -0.13663232]
Sparsity at: 0.03389887339055794
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1186 - accuracy: 0.9654 - val_loss: 0.1255 - val_accuracy: 0.9616
[-0.05940218 -0.00601314 -0.04628057 ...  0.06137127  0.18227267
 -0.15361361]
Sparsity at: 0.03389887339055794
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1001 - accuracy: 0.9706 - val_loss: 0.1165 - val_accuracy: 0.9642
[-0.05940218 -0.00601314 -0.04628057 ...  0.06442316  0.19550368
 -0.16942883]
Sparsity at: 0.03389887339055794
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0855 - accuracy: 0.9751 - val_loss: 0.1102 - val_accuracy: 0.9659
[-0.05940218 -0.00601314 -0.04628057 ...  0.06771217  0.2068753
 -0.18425012]
Sparsity at: 0.03389887339055794
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0736 - accuracy: 0.9787 - val_loss: 0.1070 - val_accuracy: 0.9661
[-0.05940218 -0.00601314 -0.04628057 ...  0.07277302  0.2159994
 -0.1982556 ]
Sparsity at: 0.03389887339055794
Epoch 9/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0637 - accuracy: 0.9820 - val_loss: 0.1059 - val_accuracy: 0.9661
[-0.05940218 -0.00601314 -0.04628057 ...  0.0786024   0.22384056
 -0.21131212]
Sparsity at: 0.03389887339055794
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0552 - accuracy: 0.9848 - val_loss: 0.1052 - val_accuracy: 0.9671
[-0.05940218 -0.00601314 -0.04628057 ...  0.08526144  0.23026066
 -0.22348079]
Sparsity at: 0.03389887339055794
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0481 - accuracy: 0.9867 - val_loss: 0.1059 - val_accuracy: 0.9674
[-0.05940218 -0.00601314 -0.04628057 ...  0.09223063  0.23614064
 -0.2357654 ]
Sparsity at: 0.03389887339055794
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0416 - accuracy: 0.9887 - val_loss: 0.1049 - val_accuracy: 0.9678
[-0.05940218 -0.00601314 -0.04628057 ...  0.09893388  0.24136019
 -0.24704528]
Sparsity at: 0.03389887339055794
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0361 - accuracy: 0.9905 - val_loss: 0.1048 - val_accuracy: 0.9688
[-0.05940218 -0.00601314 -0.04628057 ...  0.10543391  0.24643552
 -0.2588473 ]
Sparsity at: 0.03389887339055794
Epoch 14/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0312 - accuracy: 0.9922 - val_loss: 0.1046 - val_accuracy: 0.9698
[-0.05940218 -0.00601314 -0.04628057 ...  0.11128943  0.25178874
 -0.27069703]
Sparsity at: 0.03389887339055794
Epoch 15/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0269 - accuracy: 0.9938 - val_loss: 0.1058 - val_accuracy: 0.9698
[-0.05940218 -0.00601314 -0.04628057 ...  0.11687295  0.25669625
 -0.28307456]
Sparsity at: 0.03389887339055794
Epoch 16/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0230 - accuracy: 0.9950 - val_loss: 0.1064 - val_accuracy: 0.9706
[-0.05940218 -0.00601314 -0.04628057 ...  0.12233329  0.2628853
 -0.2965134 ]
Sparsity at: 0.03389887339055794
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0200 - accuracy: 0.9959 - val_loss: 0.1083 - val_accuracy: 0.9716
[-0.05940218 -0.00601314 -0.04628057 ...  0.12722383  0.26781642
 -0.3099829 ]
Sparsity at: 0.03389887339055794
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0169 - accuracy: 0.9970 - val_loss: 0.1096 - val_accuracy: 0.9716
[-0.05940218 -0.00601314 -0.04628057 ...  0.13265127  0.27276492
 -0.32279322]
Sparsity at: 0.03389887339055794
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0143 - accuracy: 0.9977 - val_loss: 0.1120 - val_accuracy: 0.9713
[-0.05940218 -0.00601314 -0.04628057 ...  0.13801153  0.2774157
 -0.33601323]
Sparsity at: 0.03389887339055794
Epoch 20/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0122 - accuracy: 0.9982 - val_loss: 0.1135 - val_accuracy: 0.9717
[-0.05940218 -0.00601314 -0.04628057 ...  0.14326216  0.28218043
 -0.34886774]
Sparsity at: 0.03389887339055794
Epoch 21/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0104 - accuracy: 0.9987 - val_loss: 0.1177 - val_accuracy: 0.9712
[-0.05940218 -0.00601314 -0.04628057 ...  0.14827676  0.28738096
 -0.36206338]
Sparsity at: 0.03389887339055794
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0091 - accuracy: 0.9988 - val_loss: 0.1221 - val_accuracy: 0.9710
[-0.05940218 -0.00601314 -0.04628057 ...  0.15419894  0.29274356
 -0.37602103]
Sparsity at: 0.03389887339055794
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0081 - accuracy: 0.9990 - val_loss: 0.1224 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.15875074  0.29662132
 -0.38703412]
Sparsity at: 0.03389887339055794
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0077 - accuracy: 0.9990 - val_loss: 0.1277 - val_accuracy: 0.9708
[-0.05940218 -0.00601314 -0.04628057 ...  0.16266237  0.30038568
 -0.39776897]
Sparsity at: 0.03389887339055794
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0079 - accuracy: 0.9986 - val_loss: 0.1350 - val_accuracy: 0.9708
[-0.05940218 -0.00601314 -0.04628057 ...  0.16916683  0.306584
 -0.4090132 ]
Sparsity at: 0.03389887339055794
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0081 - accuracy: 0.9982 - val_loss: 0.1435 - val_accuracy: 0.9693
[-0.05940218 -0.00601314 -0.04628057 ...  0.17787816  0.3090474
 -0.43033794]
Sparsity at: 0.03389887339055794
Epoch 27/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0082 - accuracy: 0.9979 - val_loss: 0.1572 - val_accuracy: 0.9668
[-0.05940218 -0.00601314 -0.04628057 ...  0.18144172  0.3148685
 -0.43937868]
Sparsity at: 0.03389887339055794
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0093 - accuracy: 0.9977 - val_loss: 0.1579 - val_accuracy: 0.9663
[-0.05940218 -0.00601314 -0.04628057 ...  0.18820922  0.32477862
 -0.45496634]
Sparsity at: 0.03389887339055794
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0090 - accuracy: 0.9973 - val_loss: 0.1392 - val_accuracy: 0.9709
[-0.05940218 -0.00601314 -0.04628057 ...  0.19406983  0.32353452
 -0.46231714]
Sparsity at: 0.03389887339055794
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0054 - accuracy: 0.9990 - val_loss: 0.1437 - val_accuracy: 0.9704
[-0.05940218 -0.00601314 -0.04628057 ...  0.19425677  0.32384244
 -0.47047985]
Sparsity at: 0.03389887339055794
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0038 - accuracy: 0.9994 - val_loss: 0.1382 - val_accuracy: 0.9714
[-0.05940218 -0.00601314 -0.04628057 ...  0.19487807  0.3296997
 -0.4809714 ]
Sparsity at: 0.03389887339055794
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0034 - accuracy: 0.9996 - val_loss: 0.1565 - val_accuracy: 0.9684
[-0.05940218 -0.00601314 -0.04628057 ...  0.19794552  0.3304043
 -0.48628515]
Sparsity at: 0.03389887339055794
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.1387 - val_accuracy: 0.9717
[-0.05940218 -0.00601314 -0.04628057 ...  0.19958706  0.33018023
 -0.4919303 ]
Sparsity at: 0.03389887339055794
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0022 - accuracy: 0.9998 - val_loss: 0.1446 - val_accuracy: 0.9713
[-0.05940218 -0.00601314 -0.04628057 ...  0.20211159  0.33018634
 -0.49206343]
Sparsity at: 0.03389887339055794
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.1526 - val_accuracy: 0.9716
[-0.05940218 -0.00601314 -0.04628057 ...  0.20457648  0.33314812
 -0.49380362]
Sparsity at: 0.03389887339055794
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 0.9999 - val_loss: 0.1495 - val_accuracy: 0.9709
[-0.05940218 -0.00601314 -0.04628057 ...  0.20947328  0.33832636
 -0.49930528]
Sparsity at: 0.03389887339055794
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1478 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.21518593  0.33443418
 -0.50703704]
Sparsity at: 0.03389887339055794
Epoch 38/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.1563 - val_accuracy: 0.9716
[-0.05940218 -0.00601314 -0.04628057 ...  0.21783085  0.3409901
 -0.5192698 ]
Sparsity at: 0.03389887339055794
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 9.9506e-04 - accuracy: 1.0000 - val_loss: 0.1614 - val_accuracy: 0.9706
[-0.05940218 -0.00601314 -0.04628057 ...  0.22173098  0.34268042
 -0.5206231 ]
Sparsity at: 0.03389887339055794
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3184e-04 - accuracy: 0.9999 - val_loss: 0.1511 - val_accuracy: 0.9724
[-0.05940218 -0.00601314 -0.04628057 ...  0.2217555   0.3449013
 -0.52254677]
Sparsity at: 0.03389887339055794
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0106 - accuracy: 0.9965 - val_loss: 0.2183 - val_accuracy: 0.9612
[-0.05940218 -0.00601314 -0.04628057 ...  0.23324452  0.33994022
 -0.53029704]
Sparsity at: 0.03389887339055794
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0167 - accuracy: 0.9944 - val_loss: 0.1735 - val_accuracy: 0.9689
[-0.05940218 -0.00601314 -0.04628057 ...  0.22699447  0.34736437
 -0.54328394]
Sparsity at: 0.03389887339055794
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 0.1556 - val_accuracy: 0.9708
[-0.05940218 -0.00601314 -0.04628057 ...  0.2332056   0.34935996
 -0.5521103 ]
Sparsity at: 0.03389887339055794
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1579 - val_accuracy: 0.9718
[-0.05940218 -0.00601314 -0.04628057 ...  0.2379061   0.34813896
 -0.5615081 ]
Sparsity at: 0.03389887339055794
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 9.8159e-04 - accuracy: 0.9999 - val_loss: 0.1523 - val_accuracy: 0.9718
[-0.05940218 -0.00601314 -0.04628057 ...  0.24017219  0.34930378
 -0.56211096]
Sparsity at: 0.03389887339055794
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 6.2018e-04 - accuracy: 1.0000 - val_loss: 0.1538 - val_accuracy: 0.9718
[-0.05940218 -0.00601314 -0.04628057 ...  0.24123164  0.34891978
 -0.5664592 ]
Sparsity at: 0.03389887339055794
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3618e-04 - accuracy: 1.0000 - val_loss: 0.1544 - val_accuracy: 0.9724
[-0.05940218 -0.00601314 -0.04628057 ...  0.24187332  0.34923056
 -0.569474  ]
Sparsity at: 0.03389887339055794
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5330e-04 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.24225368  0.35001963
 -0.572502  ]
Sparsity at: 0.03389887339055794
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0884e-04 - accuracy: 1.0000 - val_loss: 0.1554 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.24264894  0.3508149
 -0.5758019 ]
Sparsity at: 0.03389887339055794
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7624e-04 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.24313384  0.35161105
 -0.57921016]
Sparsity at: 0.03389887339055794
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.12466052184773169
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.18753654881625081
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.43717587914926526
Thresholhold 0.06464134901762009
Using suggest threshold.
Applying new mask
Percentage zeros 0.10078125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 51s 7ms/step - loss: 2.5263e-04 - accuracy: 1.0000 - val_loss: 0.1570 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.24367297  0.3523807
 -0.58303964]
Sparsity at: 0.036061561158798286
Epoch 52/500
235/235 [==============================] - 2s 7ms/step - loss: 2.2820e-04 - accuracy: 1.0000 - val_loss: 0.1580 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.24443471  0.35329822
 -0.5868884 ]
Sparsity at: 0.036061561158798286
Epoch 53/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0809e-04 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.24530917  0.3542586
 -0.590967  ]
Sparsity at: 0.036061561158798286
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9008e-04 - accuracy: 1.0000 - val_loss: 0.1601 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.24631745  0.3552871
 -0.59526014]
Sparsity at: 0.036061561158798286
Epoch 55/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7388e-04 - accuracy: 1.0000 - val_loss: 0.1613 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.24744877  0.35640043
 -0.59972507]
Sparsity at: 0.036061561158798286
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5945e-04 - accuracy: 1.0000 - val_loss: 0.1625 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.2486416   0.35758874
 -0.60441893]
Sparsity at: 0.036061561158798286
Epoch 57/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4565e-04 - accuracy: 1.0000 - val_loss: 0.1637 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.2498594   0.3588183
 -0.6093436 ]
Sparsity at: 0.036061561158798286
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3322e-04 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.2511337   0.36016235
 -0.61446464]
Sparsity at: 0.036061561158798286
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2169e-04 - accuracy: 1.0000 - val_loss: 0.1663 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.25241202  0.36150485
 -0.6198472 ]
Sparsity at: 0.036061561158798286
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1109e-04 - accuracy: 1.0000 - val_loss: 0.1677 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.25370044  0.36290064
 -0.62545013]
Sparsity at: 0.036061561158798286
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0123e-04 - accuracy: 1.0000 - val_loss: 0.1692 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.25521642  0.3643691
 -0.6311998 ]
Sparsity at: 0.036061561158798286
Epoch 62/500
235/235 [==============================] - 2s 9ms/step - loss: 9.2011e-05 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.25663424  0.3658786
 -0.637148  ]
Sparsity at: 0.036061561158798286
Epoch 63/500
235/235 [==============================] - 2s 9ms/step - loss: 8.3623e-05 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.25815254  0.36742285
 -0.6433546 ]
Sparsity at: 0.036061561158798286
Epoch 64/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5786e-05 - accuracy: 1.0000 - val_loss: 0.1737 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.25967544  0.3690472
 -0.64968497]
Sparsity at: 0.036061561158798286
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 6.8594e-05 - accuracy: 1.0000 - val_loss: 0.1754 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.26120433  0.37070265
 -0.6562151 ]
Sparsity at: 0.036061561158798286
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1934e-05 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.2627519   0.3724224
 -0.6629668 ]
Sparsity at: 0.036061561158798286
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5940e-05 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.2643473   0.3741994
 -0.6698568 ]
Sparsity at: 0.036061561158798286
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0343e-05 - accuracy: 1.0000 - val_loss: 0.1804 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.26593775  0.37601003
 -0.6769373 ]
Sparsity at: 0.036061561158798286
Epoch 69/500
235/235 [==============================] - 2s 9ms/step - loss: 4.5286e-05 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.26757812  0.37785307
 -0.68411005]
Sparsity at: 0.036061561158798286
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 4.0626e-05 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.269156    0.37974578
 -0.69150627]
Sparsity at: 0.036061561158798286
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6408e-05 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.2708193   0.3816925
 -0.69897497]
Sparsity at: 0.036061561158798286
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2610e-05 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.27251378  0.38366756
 -0.70667696]
Sparsity at: 0.036061561158798286
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9146e-05 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.27419886  0.38570395
 -0.7144129 ]
Sparsity at: 0.036061561158798286
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6020e-05 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.2758885   0.38772577
 -0.72228426]
Sparsity at: 0.036061561158798286
Epoch 75/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3207e-05 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.2775986   0.38985962
 -0.73020875]
Sparsity at: 0.036061561158798286
Epoch 76/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0659e-05 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.27940014  0.392011
 -0.7383061 ]
Sparsity at: 0.036061561158798286
Epoch 77/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8402e-05 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.2810973   0.3941745
 -0.7464559 ]
Sparsity at: 0.036061561158798286
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6330e-05 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.28283328  0.39637396
 -0.75476164]
Sparsity at: 0.036061561158798286
Epoch 79/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4518e-05 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9725
[-0.05940218 -0.00601314 -0.04628057 ...  0.2844636   0.39860216
 -0.7629115 ]
Sparsity at: 0.036061561158798286
Epoch 80/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2893e-05 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9725
[-0.05940218 -0.00601314 -0.04628057 ...  0.2862499   0.40082029
 -0.771361  ]
Sparsity at: 0.036061561158798286
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1420e-05 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9724
[-0.05940218 -0.00601314 -0.04628057 ...  0.28783566  0.40309924
 -0.779747  ]
Sparsity at: 0.036061561158798286
Epoch 82/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0115e-05 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9724
[-0.05940218 -0.00601314 -0.04628057 ...  0.28949818  0.40536532
 -0.788187  ]
Sparsity at: 0.036061561158798286
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 8.9538e-06 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.29120973  0.4076956
 -0.79667705]
Sparsity at: 0.036061561158798286
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 7.9362e-06 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.29291773  0.40998065
 -0.80517226]
Sparsity at: 0.036061561158798286
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 7.0274e-06 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.29466718  0.41232425
 -0.8137191 ]
Sparsity at: 0.036061561158798286
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2212e-06 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.29637045  0.41463712
 -0.82225025]
Sparsity at: 0.036061561158798286
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 5.5017e-06 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.29801774  0.4169323
 -0.8307521 ]
Sparsity at: 0.036061561158798286
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8656e-06 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.29971218  0.41927862
 -0.83933234]
Sparsity at: 0.036061561158798286
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2986e-06 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.30141833  0.42163005
 -0.8478284 ]
Sparsity at: 0.036061561158798286
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8024e-06 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.30308437  0.42391056
 -0.8564479 ]
Sparsity at: 0.036061561158798286
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3664e-06 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.3047794   0.42620647
 -0.8649628 ]
Sparsity at: 0.036061561158798286
Epoch 92/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9741e-06 - accuracy: 1.0000 - val_loss: 0.2264 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.30643582  0.42853895
 -0.8735429 ]
Sparsity at: 0.036061561158798286
Epoch 93/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6304e-06 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.30815512  0.43083894
 -0.8820202 ]
Sparsity at: 0.036061561158798286
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3265e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.30968112  0.43320584
 -0.8905162 ]
Sparsity at: 0.036061561158798286
Epoch 95/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0561e-06 - accuracy: 1.0000 - val_loss: 0.2328 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.31121066  0.43547422
 -0.8989246 ]
Sparsity at: 0.036061561158798286
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8199e-06 - accuracy: 1.0000 - val_loss: 0.2345 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.312879    0.43774602
 -0.90741193]
Sparsity at: 0.036061561158798286
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6108e-06 - accuracy: 1.0000 - val_loss: 0.2366 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.3145279   0.44009018
 -0.91578734]
Sparsity at: 0.036061561158798286
Epoch 98/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4253e-06 - accuracy: 1.0000 - val_loss: 0.2386 - val_accuracy: 0.9724
[-0.05940218 -0.00601314 -0.04628057 ...  0.31607017  0.44235307
 -0.9241536 ]
Sparsity at: 0.036061561158798286
Epoch 99/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2638e-06 - accuracy: 1.0000 - val_loss: 0.2406 - val_accuracy: 0.9724
[-0.05940218 -0.00601314 -0.04628057 ...  0.31765768  0.4446322
 -0.9324005 ]
Sparsity at: 0.036061561158798286
Epoch 100/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1206e-06 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.31922382  0.4468842
 -0.94068354]
Sparsity at: 0.036061561158798286
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.1777579907073843
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.28248873381016537
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.7064567878092234
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10078125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 9.9216e-07 - accuracy: 1.0000 - val_loss: 0.2445 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.32077536  0.44915947
 -0.9488824 ]
Sparsity at: 0.036061561158798286
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 8.8317e-07 - accuracy: 1.0000 - val_loss: 0.2466 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.32216695  0.45138523
 -0.9570187 ]
Sparsity at: 0.036061561158798286
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8301e-07 - accuracy: 1.0000 - val_loss: 0.2483 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.3237306   0.45361713
 -0.96516657]
Sparsity at: 0.036061561158798286
Epoch 104/500
235/235 [==============================] - 2s 9ms/step - loss: 6.9567e-07 - accuracy: 1.0000 - val_loss: 0.2504 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.3251415   0.4558646
 -0.9731345 ]
Sparsity at: 0.036061561158798286
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1827e-07 - accuracy: 1.0000 - val_loss: 0.2521 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.3266561   0.45807195
 -0.98110086]
Sparsity at: 0.036061561158798286
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 5.5029e-07 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.32811597  0.46028712
 -0.98895335]
Sparsity at: 0.036061561158798286
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 4.8991e-07 - accuracy: 1.0000 - val_loss: 0.2560 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.32952535  0.46246526
 -0.9967266 ]
Sparsity at: 0.036061561158798286
Epoch 108/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3768e-07 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.33090702  0.46456534
 -1.0044128 ]
Sparsity at: 0.036061561158798286
Epoch 109/500
235/235 [==============================] - 2s 9ms/step - loss: 3.9142e-07 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.3322797   0.46673286
 -1.0120156 ]
Sparsity at: 0.036061561158798286
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4989e-07 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.33361828  0.46890292
 -1.0194789 ]
Sparsity at: 0.036061561158798286
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 3.1303e-07 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.33497915  0.47099724
 -1.0268598 ]
Sparsity at: 0.036061561158798286
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8056e-07 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.33630833  0.47309563
 -1.0341535 ]
Sparsity at: 0.036061561158798286
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5206e-07 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.33753088  0.4751327
 -1.0413007 ]
Sparsity at: 0.036061561158798286
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2632e-07 - accuracy: 1.0000 - val_loss: 0.2687 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.3388323   0.47715908
 -1.0483243 ]
Sparsity at: 0.036061561158798286
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0369e-07 - accuracy: 1.0000 - val_loss: 0.2705 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.34005743  0.47920534
 -1.055242  ]
Sparsity at: 0.036061561158798286
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8383e-07 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9724
[-0.05940218 -0.00601314 -0.04628057 ...  0.34125978  0.48114258
 -1.0619344 ]
Sparsity at: 0.036061561158798286
Epoch 117/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6627e-07 - accuracy: 1.0000 - val_loss: 0.2738 - val_accuracy: 0.9724
[-0.05940218 -0.00601314 -0.04628057 ...  0.3424438   0.48305067
 -1.0685158 ]
Sparsity at: 0.036061561158798286
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5061e-07 - accuracy: 1.0000 - val_loss: 0.2752 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.34358823  0.48495767
 -1.0750087 ]
Sparsity at: 0.036061561158798286
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3693e-07 - accuracy: 1.0000 - val_loss: 0.2771 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.3446664   0.48679602
 -1.0812649 ]
Sparsity at: 0.036061561158798286
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2442e-07 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.34576535  0.48862296
 -1.0873562 ]
Sparsity at: 0.036061561158798286
Epoch 121/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1349e-07 - accuracy: 1.0000 - val_loss: 0.2798 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.34679845  0.49042913
 -1.0933244 ]
Sparsity at: 0.036061561158798286
Epoch 122/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0358e-07 - accuracy: 1.0000 - val_loss: 0.2814 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.34782377  0.492172
 -1.0991111 ]
Sparsity at: 0.036061561158798286
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 9.4958e-08 - accuracy: 1.0000 - val_loss: 0.2826 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.34882167  0.49385104
 -1.1046981 ]
Sparsity at: 0.036061561158798286
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 8.7098e-08 - accuracy: 1.0000 - val_loss: 0.2840 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.34984297  0.4955171
 -1.1101247 ]
Sparsity at: 0.036061561158798286
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 8.0113e-08 - accuracy: 1.0000 - val_loss: 0.2855 - val_accuracy: 0.9720
[-0.05940218 -0.00601314 -0.04628057 ...  0.3507149   0.49710315
 -1.1153219 ]
Sparsity at: 0.036061561158798286
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 7.3922e-08 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.3515817   0.4986421
 -1.1203995 ]
Sparsity at: 0.036061561158798286
Epoch 127/500
235/235 [==============================] - 2s 9ms/step - loss: 6.8325e-08 - accuracy: 1.0000 - val_loss: 0.2879 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.35243478  0.5001407
 -1.1252488 ]
Sparsity at: 0.036061561158798286
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 6.3278e-08 - accuracy: 1.0000 - val_loss: 0.2891 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.35319194  0.50155824
 -1.1299348 ]
Sparsity at: 0.036061561158798286
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8842e-08 - accuracy: 1.0000 - val_loss: 0.2902 - val_accuracy: 0.9721
[-0.05940218 -0.00601314 -0.04628057 ...  0.3539534   0.503002
 -1.1344548 ]
Sparsity at: 0.036061561158798286
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 5.4679e-08 - accuracy: 1.0000 - val_loss: 0.2913 - val_accuracy: 0.9722
[-0.05940218 -0.00601314 -0.04628057 ...  0.35470918  0.50437796
 -1.138862  ]
Sparsity at: 0.036061561158798286
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1024e-08 - accuracy: 1.0000 - val_loss: 0.2924 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.3554769   0.505696
 -1.1430457 ]
Sparsity at: 0.036061561158798286
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 4.7682e-08 - accuracy: 1.0000 - val_loss: 0.2934 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.35615498  0.5069446
 -1.1470921 ]
Sparsity at: 0.036061561158798286
Epoch 133/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4622e-08 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9723
[-0.05940218 -0.00601314 -0.04628057 ...  0.35680446  0.5081799
 -1.1509615 ]
Sparsity at: 0.036061561158798286
Epoch 134/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2065e-08 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9725
[-0.05940218 -0.00601314 -0.04628057 ...  0.3574548   0.5093881
 -1.1546996 ]
Sparsity at: 0.036061561158798286
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9470e-08 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.35809693  0.5105564
 -1.1582747 ]
Sparsity at: 0.036061561158798286
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7336e-08 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.35867622  0.5117009
 -1.1617181 ]
Sparsity at: 0.036061561158798286
Epoch 137/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5165e-08 - accuracy: 1.0000 - val_loss: 0.2981 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.3592626   0.5127769
 -1.165033  ]
Sparsity at: 0.036061561158798286
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3299e-08 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.35981143  0.51380795
 -1.1682234 ]
Sparsity at: 0.036061561158798286
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1495e-08 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.36035112  0.51477534
 -1.1713048 ]
Sparsity at: 0.036061561158798286
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9943e-08 - accuracy: 1.0000 - val_loss: 0.3005 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36085218  0.5157216
 -1.1742716 ]
Sparsity at: 0.036061561158798286
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8433e-08 - accuracy: 1.0000 - val_loss: 0.3013 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36135888  0.51656955
 -1.1771457 ]
Sparsity at: 0.036061561158798286
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7005e-08 - accuracy: 1.0000 - val_loss: 0.3019 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36181638  0.5173682
 -1.179869  ]
Sparsity at: 0.036061561158798286
Epoch 143/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5870e-08 - accuracy: 1.0000 - val_loss: 0.3026 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36223406  0.5181281
 -1.1825261 ]
Sparsity at: 0.036061561158798286
Epoch 144/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4656e-08 - accuracy: 1.0000 - val_loss: 0.3033 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36261612  0.51884675
 -1.1850615 ]
Sparsity at: 0.036061561158798286
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3627e-08 - accuracy: 1.0000 - val_loss: 0.3039 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36298308  0.5195678
 -1.1875218 ]
Sparsity at: 0.036061561158798286
Epoch 146/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2632e-08 - accuracy: 1.0000 - val_loss: 0.3045 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3633582   0.52022207
 -1.1898788 ]
Sparsity at: 0.036061561158798286
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1770e-08 - accuracy: 1.0000 - val_loss: 0.3053 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3637365   0.52088064
 -1.1921483 ]
Sparsity at: 0.036061561158798286
Epoch 148/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0953e-08 - accuracy: 1.0000 - val_loss: 0.3059 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.36409712  0.5214975
 -1.1943593 ]
Sparsity at: 0.036061561158798286
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0126e-08 - accuracy: 1.0000 - val_loss: 0.3065 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.36442307  0.52206886
 -1.1965054 ]
Sparsity at: 0.036061561158798286
Epoch 150/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9403e-08 - accuracy: 1.0000 - val_loss: 0.3071 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36472526  0.52264017
 -1.1985694 ]
Sparsity at: 0.036061561158798286
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.23543465671956376
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.37804899443405304
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.9533427882879089
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10078125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 1.8716e-08 - accuracy: 1.0000 - val_loss: 0.3077 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3650185   0.5231604
 -1.2005807 ]
Sparsity at: 0.036061561158798286
Epoch 152/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8072e-08 - accuracy: 1.0000 - val_loss: 0.3083 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.36528185  0.52366614
 -1.2025083 ]
Sparsity at: 0.036061561158798286
Epoch 153/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7486e-08 - accuracy: 1.0000 - val_loss: 0.3087 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36554983  0.5241782
 -1.2043424 ]
Sparsity at: 0.036061561158798286
Epoch 154/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6942e-08 - accuracy: 1.0000 - val_loss: 0.3093 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.3658118   0.5246774
 -1.2061359 ]
Sparsity at: 0.036061561158798286
Epoch 155/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6344e-08 - accuracy: 1.0000 - val_loss: 0.3098 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.36608312  0.52512
 -1.2078426 ]
Sparsity at: 0.036061561158798286
Epoch 156/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5855e-08 - accuracy: 1.0000 - val_loss: 0.3103 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.3663202   0.5255552
 -1.2095631 ]
Sparsity at: 0.036061561158798286
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5380e-08 - accuracy: 1.0000 - val_loss: 0.3108 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.36656913  0.52596104
 -1.2112132 ]
Sparsity at: 0.036061561158798286
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4965e-08 - accuracy: 1.0000 - val_loss: 0.3112 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36684233  0.52633184
 -1.2128217 ]
Sparsity at: 0.036061561158798286
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4528e-08 - accuracy: 1.0000 - val_loss: 0.3117 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36708415  0.5267006
 -1.2144184 ]
Sparsity at: 0.036061561158798286
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4124e-08 - accuracy: 1.0000 - val_loss: 0.3122 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36732408  0.5270325
 -1.2159587 ]
Sparsity at: 0.036061561158798286
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3745e-08 - accuracy: 1.0000 - val_loss: 0.3127 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36755633  0.52734774
 -1.2174815 ]
Sparsity at: 0.036061561158798286
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3347e-08 - accuracy: 1.0000 - val_loss: 0.3131 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.3677866   0.5276297
 -1.2189165 ]
Sparsity at: 0.036061561158798286
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2998e-08 - accuracy: 1.0000 - val_loss: 0.3135 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.368002    0.527866
 -1.2203283 ]
Sparsity at: 0.036061561158798286
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2690e-08 - accuracy: 1.0000 - val_loss: 0.3139 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36820224  0.52811056
 -1.2217069 ]
Sparsity at: 0.036061561158798286
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2364e-08 - accuracy: 1.0000 - val_loss: 0.3143 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36838797  0.5283562
 -1.2230624 ]
Sparsity at: 0.036061561158798286
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2100e-08 - accuracy: 1.0000 - val_loss: 0.3147 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.36857566  0.5285898
 -1.2243809 ]
Sparsity at: 0.036061561158798286
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1796e-08 - accuracy: 1.0000 - val_loss: 0.3151 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.36875585  0.5287983
 -1.2256767 ]
Sparsity at: 0.036061561158798286
Epoch 168/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1514e-08 - accuracy: 1.0000 - val_loss: 0.3155 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.36891288  0.52900386
 -1.2269247 ]
Sparsity at: 0.036061561158798286
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1323e-08 - accuracy: 1.0000 - val_loss: 0.3158 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.36907277  0.5291965
 -1.2281431 ]
Sparsity at: 0.036061561158798286
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1045e-08 - accuracy: 1.0000 - val_loss: 0.3161 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3692305   0.5293664
 -1.2293657 ]
Sparsity at: 0.036061561158798286
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0759e-08 - accuracy: 1.0000 - val_loss: 0.3165 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36938813  0.52953094
 -1.230559  ]
Sparsity at: 0.036061561158798286
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0580e-08 - accuracy: 1.0000 - val_loss: 0.3168 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.3695263   0.5296838
 -1.2317338 ]
Sparsity at: 0.036061561158798286
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0339e-08 - accuracy: 1.0000 - val_loss: 0.3171 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.36966577  0.5298433
 -1.2329025 ]
Sparsity at: 0.036061561158798286
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0153e-08 - accuracy: 1.0000 - val_loss: 0.3175 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.36982587  0.52999115
 -1.2340353 ]
Sparsity at: 0.036061561158798286
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 9.8924e-09 - accuracy: 1.0000 - val_loss: 0.3178 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.36996588  0.5300925
 -1.23515   ]
Sparsity at: 0.036061561158798286
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7315e-09 - accuracy: 1.0000 - val_loss: 0.3181 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3701052   0.5301961
 -1.2362359 ]
Sparsity at: 0.036061561158798286
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 9.5546e-09 - accuracy: 1.0000 - val_loss: 0.3184 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37023664  0.5302996
 -1.237342  ]
Sparsity at: 0.036061561158798286
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 9.3857e-09 - accuracy: 1.0000 - val_loss: 0.3187 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37035912  0.53039736
 -1.2383842 ]
Sparsity at: 0.036061561158798286
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 9.1732e-09 - accuracy: 1.0000 - val_loss: 0.3189 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37050477  0.53049564
 -1.2393944 ]
Sparsity at: 0.036061561158798286
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 9.0559e-09 - accuracy: 1.0000 - val_loss: 0.3191 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37061685  0.530596
 -1.2404022 ]
Sparsity at: 0.036061561158798286
Epoch 181/500
235/235 [==============================] - 2s 9ms/step - loss: 8.8255e-09 - accuracy: 1.0000 - val_loss: 0.3194 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37075382  0.5306733
 -1.2413902 ]
Sparsity at: 0.036061561158798286
Epoch 182/500
235/235 [==============================] - 2s 9ms/step - loss: 8.7023e-09 - accuracy: 1.0000 - val_loss: 0.3197 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37089917  0.530762
 -1.242333  ]
Sparsity at: 0.036061561158798286
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 8.5870e-09 - accuracy: 1.0000 - val_loss: 0.3199 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37102923  0.5308503
 -1.2432815 ]
Sparsity at: 0.036061561158798286
Epoch 184/500
235/235 [==============================] - 2s 9ms/step - loss: 8.3943e-09 - accuracy: 1.0000 - val_loss: 0.3202 - val_accuracy: 0.9736
[-0.05940218 -0.00601314 -0.04628057 ...  0.3711411   0.53092235
 -1.2442253 ]
Sparsity at: 0.036061561158798286
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2473e-09 - accuracy: 1.0000 - val_loss: 0.3204 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.3712636   0.53098273
 -1.2451632 ]
Sparsity at: 0.036061561158798286
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 8.1440e-09 - accuracy: 1.0000 - val_loss: 0.3206 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.3713761   0.5310613
 -1.2460926 ]
Sparsity at: 0.036061561158798286
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 7.9870e-09 - accuracy: 1.0000 - val_loss: 0.3210 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37146538  0.53110147
 -1.2470101 ]
Sparsity at: 0.036061561158798286
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 7.8400e-09 - accuracy: 1.0000 - val_loss: 0.3212 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37154433  0.531137
 -1.2479092 ]
Sparsity at: 0.036061561158798286
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7387e-09 - accuracy: 1.0000 - val_loss: 0.3214 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37164173  0.53117794
 -1.2488012 ]
Sparsity at: 0.036061561158798286
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5777e-09 - accuracy: 1.0000 - val_loss: 0.3216 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37173265  0.5312083
 -1.2496612 ]
Sparsity at: 0.036061561158798286
Epoch 191/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5201e-09 - accuracy: 1.0000 - val_loss: 0.3218 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.3718344   0.5312695
 -1.2505108 ]
Sparsity at: 0.036061561158798286
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3651e-09 - accuracy: 1.0000 - val_loss: 0.3220 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37194136  0.53131557
 -1.2513653 ]
Sparsity at: 0.036061561158798286
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 7.2300e-09 - accuracy: 1.0000 - val_loss: 0.3221 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37201995  0.53134334
 -1.2521983 ]
Sparsity at: 0.036061561158798286
Epoch 194/500
235/235 [==============================] - 2s 9ms/step - loss: 7.1386e-09 - accuracy: 1.0000 - val_loss: 0.3224 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.3721058   0.5313626
 -1.2530528 ]
Sparsity at: 0.036061561158798286
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0671e-09 - accuracy: 1.0000 - val_loss: 0.3225 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37220564  0.5314044
 -1.2538744 ]
Sparsity at: 0.036061561158798286
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 6.9499e-09 - accuracy: 1.0000 - val_loss: 0.3226 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37228486  0.53141016
 -1.2546892 ]
Sparsity at: 0.036061561158798286
Epoch 197/500
235/235 [==============================] - 2s 9ms/step - loss: 6.8347e-09 - accuracy: 1.0000 - val_loss: 0.3228 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37235153  0.53143054
 -1.2554952 ]
Sparsity at: 0.036061561158798286
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 6.7155e-09 - accuracy: 1.0000 - val_loss: 0.3230 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37244567  0.53141725
 -1.2562755 ]
Sparsity at: 0.036061561158798286
Epoch 199/500
235/235 [==============================] - 2s 9ms/step - loss: 6.6559e-09 - accuracy: 1.0000 - val_loss: 0.3231 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.3725268   0.5314011
 -1.2570539 ]
Sparsity at: 0.036061561158798286
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5049e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.3725928   0.53135365
 -1.2578161 ]
Sparsity at: 0.036061561158798286
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.29506325402118705
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.4555327289969071
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 1.1360591471881776
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10078125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 6.4691e-09 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.3726702   0.5313338
 -1.2585928 ]
Sparsity at: 0.036061561158798286
Epoch 202/500
235/235 [==============================] - 2s 7ms/step - loss: 6.3340e-09 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37271845  0.531315
 -1.2593386 ]
Sparsity at: 0.036061561158798286
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2585e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.3727929   0.5312752
 -1.260086  ]
Sparsity at: 0.036061561158798286
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1750e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37284914  0.5312293
 -1.2608207 ]
Sparsity at: 0.036061561158798286
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1075e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37290972  0.5311705
 -1.2615368 ]
Sparsity at: 0.036061561158798286
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 5.9823e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37295794  0.53113633
 -1.2622548 ]
Sparsity at: 0.036061561158798286
Epoch 207/500
235/235 [==============================] - 2s 9ms/step - loss: 5.8830e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37300938  0.5310511
 -1.262961  ]
Sparsity at: 0.036061561158798286
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 5.8393e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9735
[-0.05940218 -0.00601314 -0.04628057 ...  0.37305403  0.53095496
 -1.2636534 ]
Sparsity at: 0.036061561158798286
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7499e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37309292  0.5308613
 -1.2643573 ]
Sparsity at: 0.036061561158798286
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 5.6863e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37315693  0.53077614
 -1.2650367 ]
Sparsity at: 0.036061561158798286
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6028e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37319955  0.5306883
 -1.265726  ]
Sparsity at: 0.036061561158798286
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5889e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37325287  0.5306084
 -1.2663789 ]
Sparsity at: 0.036061561158798286
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 5.4955e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.3733038   0.53052443
 -1.2670349 ]
Sparsity at: 0.036061561158798286
Epoch 214/500
235/235 [==============================] - 2s 9ms/step - loss: 5.4061e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.3733592   0.5304374
 -1.2676969 ]
Sparsity at: 0.036061561158798286
Epoch 215/500
235/235 [==============================] - 2s 9ms/step - loss: 5.3525e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37339056  0.5303309
 -1.2683264 ]
Sparsity at: 0.036061561158798286
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2929e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.3734238   0.5302341
 -1.268955  ]
Sparsity at: 0.036061561158798286
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2671e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9734
[-0.05940218 -0.00601314 -0.04628057 ...  0.37348118  0.53014904
 -1.2695562 ]
Sparsity at: 0.036061561158798286
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 5.1816e-09 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37352023  0.53004664
 -1.2701916 ]
Sparsity at: 0.036061561158798286
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 5.1101e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37355566  0.5299307
 -1.2708026 ]
Sparsity at: 0.036061561158798286
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0525e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37360355  0.52983177
 -1.2713892 ]
Sparsity at: 0.036061561158798286
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0068e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.37361625  0.5297242
 -1.2719827 ]
Sparsity at: 0.036061561158798286
Epoch 222/500
235/235 [==============================] - 2s 9ms/step - loss: 4.9671e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9733
[-0.05940218 -0.00601314 -0.04628057 ...  0.373659    0.52962226
 -1.2725705 ]
Sparsity at: 0.036061561158798286
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 4.8876e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37370038  0.5295085
 -1.2731571 ]
Sparsity at: 0.036061561158798286
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 4.8240e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37373602  0.5293984
 -1.2737247 ]
Sparsity at: 0.036061561158798286
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7843e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3737719   0.5292881
 -1.2742908 ]
Sparsity at: 0.036061561158798286
Epoch 226/500
235/235 [==============================] - 2s 9ms/step - loss: 4.7723e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37380368  0.52917385
 -1.2748594 ]
Sparsity at: 0.036061561158798286
Epoch 227/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6551e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37383416  0.5290347
 -1.2754354 ]
Sparsity at: 0.036061561158798286
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6412e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3738923   0.52889943
 -1.275976  ]
Sparsity at: 0.036061561158798286
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5876e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3739303   0.52879244
 -1.2765222 ]
Sparsity at: 0.036061561158798286
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5439e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37397715  0.528663
 -1.2770733 ]
Sparsity at: 0.036061561158798286
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4902e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3740148   0.52851105
 -1.277631  ]
Sparsity at: 0.036061561158798286
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4167e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37407872  0.52834666
 -1.2781562 ]
Sparsity at: 0.036061561158798286
Epoch 233/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3770e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37411058  0.528196
 -1.2786919 ]
Sparsity at: 0.036061561158798286
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3313e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37414593  0.5280488
 -1.279211  ]
Sparsity at: 0.036061561158798286
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2935e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37417608  0.5279016
 -1.2797203 ]
Sparsity at: 0.036061561158798286
Epoch 236/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2677e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37422577  0.5277442
 -1.2802407 ]
Sparsity at: 0.036061561158798286
Epoch 237/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1902e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37427002  0.5275785
 -1.2807403 ]
Sparsity at: 0.036061561158798286
Epoch 238/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1604e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37428844  0.52741057
 -1.2812531 ]
Sparsity at: 0.036061561158798286
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1425e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37431946  0.5272479
 -1.2817591 ]
Sparsity at: 0.036061561158798286
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1087e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37435773  0.5270909
 -1.2822707 ]
Sparsity at: 0.036061561158798286
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 4.0531e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37436128  0.52691054
 -1.2827698 ]
Sparsity at: 0.036061561158798286
Epoch 242/500
235/235 [==============================] - 2s 9ms/step - loss: 4.0611e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37439308  0.526747
 -1.2832632 ]
Sparsity at: 0.036061561158798286
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0392e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37441924  0.5265964
 -1.2837512 ]
Sparsity at: 0.036061561158798286
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 3.9399e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37442172  0.52643144
 -1.2842286 ]
Sparsity at: 0.036061561158798286
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9856e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37444612  0.52626836
 -1.2847204 ]
Sparsity at: 0.036061561158798286
Epoch 246/500
235/235 [==============================] - 2s 9ms/step - loss: 3.8902e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3744725   0.5260815
 -1.285209  ]
Sparsity at: 0.036061561158798286
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8763e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37449235  0.52592057
 -1.2856705 ]
Sparsity at: 0.036061561158798286
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8584e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745018   0.5257238
 -1.2861542 ]
Sparsity at: 0.036061561158798286
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8028e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37450033  0.5255538
 -1.2866144 ]
Sparsity at: 0.036061561158798286
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7611e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37451905  0.52535456
 -1.287095  ]
Sparsity at: 0.036061561158798286
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.3589129205544168
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.5320120862464677
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 1.288071074504856
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10078125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 42s 7ms/step - loss: 3.7869e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37453255  0.52518344
 -1.2875634 ]
Sparsity at: 0.036061561158798286
Epoch 252/500
235/235 [==============================] - 2s 7ms/step - loss: 3.7193e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37453893  0.5249683
 -1.2880385 ]
Sparsity at: 0.036061561158798286
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7233e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37455785  0.5247879
 -1.2884748 ]
Sparsity at: 0.036061561158798286
Epoch 254/500
235/235 [==============================] - 2s 10ms/step - loss: 3.6558e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37454426  0.5245993
 -1.2889473 ]
Sparsity at: 0.036061561158798286
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6220e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745522   0.52438486
 -1.2893964 ]
Sparsity at: 0.036061561158798286
Epoch 256/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6279e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745384   0.5242022
 -1.289853  ]
Sparsity at: 0.036061561158798286
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5763e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37455633  0.5240113
 -1.2903045 ]
Sparsity at: 0.036061561158798286
Epoch 258/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5544e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745623   0.523799
 -1.2907453 ]
Sparsity at: 0.036061561158798286
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5624e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745711   0.52361375
 -1.2912056 ]
Sparsity at: 0.036061561158798286
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5008e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37457135  0.5233972
 -1.2916387 ]
Sparsity at: 0.036061561158798286
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4829e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37457293  0.5231888
 -1.2920777 ]
Sparsity at: 0.036061561158798286
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4491e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37457472  0.52298445
 -1.2925158 ]
Sparsity at: 0.036061561158798286
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3836e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37455934  0.52275056
 -1.2929486 ]
Sparsity at: 0.036061561158798286
Epoch 264/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4491e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37454963  0.5225236
 -1.2933884 ]
Sparsity at: 0.036061561158798286
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3716e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37454018  0.52229095
 -1.2938296 ]
Sparsity at: 0.036061561158798286
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3935e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745457   0.5220874
 -1.2942737 ]
Sparsity at: 0.036061561158798286
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3796e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37453622  0.5218652
 -1.2946929 ]
Sparsity at: 0.036061561158798286
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3498e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37455365  0.5216467
 -1.295136  ]
Sparsity at: 0.036061561158798286
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2604e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745576   0.52142465
 -1.2955457 ]
Sparsity at: 0.036061561158798286
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3359e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37455934  0.5212357
 -1.2959728 ]
Sparsity at: 0.036061561158798286
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2922e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37456656  0.521047
 -1.2963836 ]
Sparsity at: 0.036061561158798286
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2485e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37455577  0.52081543
 -1.2968068 ]
Sparsity at: 0.036061561158798286
Epoch 273/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2584e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37455684  0.5206027
 -1.29723   ]
Sparsity at: 0.036061561158798286
Epoch 274/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2047e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745533   0.52038664
 -1.2976494 ]
Sparsity at: 0.036061561158798286
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2028e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3745627   0.52016073
 -1.2980654 ]
Sparsity at: 0.036061561158798286
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1710e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37454444  0.51992947
 -1.2984579 ]
Sparsity at: 0.036061561158798286
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 3.1511e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37451687  0.5197071
 -1.2988719 ]
Sparsity at: 0.036061561158798286
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 3.1610e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37451735  0.519455
 -1.2992994 ]
Sparsity at: 0.036061561158798286
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 3.1133e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37449437  0.5192113
 -1.2996916 ]
Sparsity at: 0.036061561158798286
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0994e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.3744859   0.5189654
 -1.3000956 ]
Sparsity at: 0.036061561158798286
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0537e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37446934  0.51872987
 -1.3004866 ]
Sparsity at: 0.036061561158798286
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0577e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37447476  0.51847893
 -1.3008807 ]
Sparsity at: 0.036061561158798286
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0180e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37444812  0.51823044
 -1.301266  ]
Sparsity at: 0.036061561158798286
Epoch 284/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0518e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37442675  0.517988
 -1.3016759 ]
Sparsity at: 0.036061561158798286
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0239e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.374419    0.51774865
 -1.3020513 ]
Sparsity at: 0.036061561158798286
Epoch 286/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9902e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.3744029   0.51751006
 -1.3024378 ]
Sparsity at: 0.036061561158798286
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0220e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37438506  0.51726395
 -1.3028284 ]
Sparsity at: 0.036061561158798286
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9544e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37437615  0.5170244
 -1.3032199 ]
Sparsity at: 0.036061561158798286
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9822e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37437955  0.5167742
 -1.303588  ]
Sparsity at: 0.036061561158798286
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9524e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9725
[-0.05940218 -0.00601314 -0.04628057 ...  0.37437275  0.5165289
 -1.3039718 ]
Sparsity at: 0.036061561158798286
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9067e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37435326  0.51628906
 -1.304344  ]
Sparsity at: 0.036061561158798286
Epoch 292/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9484e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9725
[-0.05940218 -0.00601314 -0.04628057 ...  0.37432626  0.51604944
 -1.3047217 ]
Sparsity at: 0.036061561158798286
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9504e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37432408  0.51579857
 -1.305094  ]
Sparsity at: 0.036061561158798286
Epoch 294/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8908e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.3743226   0.51555115
 -1.3054786 ]
Sparsity at: 0.036061561158798286
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8829e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9725
[-0.05940218 -0.00601314 -0.04628057 ...  0.37430257  0.5153222
 -1.305834  ]
Sparsity at: 0.036061561158798286
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8590e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37428576  0.5150695
 -1.3062319 ]
Sparsity at: 0.036061561158798286
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9008e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37427464  0.5148128
 -1.3066056 ]
Sparsity at: 0.036061561158798286
Epoch 298/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8670e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37427315  0.51457566
 -1.3069621 ]
Sparsity at: 0.036061561158798286
Epoch 299/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8392e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37424603  0.5143179
 -1.3073198 ]
Sparsity at: 0.036061561158798286
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8253e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9725
[-0.05940218 -0.00601314 -0.04628057 ...  0.3742357   0.5140678
 -1.3076735 ]
Sparsity at: 0.036061561158798286
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.4284913756669084
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.6062501564149656
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 1.4495890268796359
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10078125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 2.8213e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37421086  0.51382315
 -1.3080279 ]
Sparsity at: 0.036061561158798286
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 2.8690e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37420675  0.5135829
 -1.3084192 ]
Sparsity at: 0.036061561158798286
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37420508  0.5133288
 -1.3087713 ]
Sparsity at: 0.036061561158798286
Epoch 304/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7796e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37419993  0.5130727
 -1.3091253 ]
Sparsity at: 0.036061561158798286
Epoch 305/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7935e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37418437  0.5128076
 -1.3094857 ]
Sparsity at: 0.036061561158798286
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37415418  0.5125172
 -1.3098589 ]
Sparsity at: 0.036061561158798286
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7359e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37413776  0.5122655
 -1.3101856 ]
Sparsity at: 0.036061561158798286
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7577e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37412596  0.51200855
 -1.3105512 ]
Sparsity at: 0.036061561158798286
Epoch 309/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.3741374   0.51174074
 -1.3108892 ]
Sparsity at: 0.036061561158798286
Epoch 310/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7597e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37411097  0.51148605
 -1.3112578 ]
Sparsity at: 0.036061561158798286
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7100e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37408814  0.5112492
 -1.311597  ]
Sparsity at: 0.036061561158798286
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.3740715   0.511002
 -1.3119444 ]
Sparsity at: 0.036061561158798286
Epoch 313/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6683e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.3740448   0.5107361
 -1.3123186 ]
Sparsity at: 0.036061561158798286
Epoch 314/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6822e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37403712  0.5104731
 -1.3126812 ]
Sparsity at: 0.036061561158798286
Epoch 315/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7299e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.3740164   0.510207
 -1.3130152 ]
Sparsity at: 0.036061561158798286
Epoch 316/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6425e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.3739978   0.50993425
 -1.3133696 ]
Sparsity at: 0.036061561158798286
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6464e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37398255  0.5096798
 -1.3136939 ]
Sparsity at: 0.036061561158798286
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7100e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37395242  0.5094016
 -1.3140361 ]
Sparsity at: 0.036061561158798286
Epoch 319/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6365e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37392282  0.50914365
 -1.3143903 ]
Sparsity at: 0.036061561158798286
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6484e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9726
[-0.05940218 -0.00601314 -0.04628057 ...  0.37389714  0.50886065
 -1.314751  ]
Sparsity at: 0.036061561158798286
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.3738661   0.5086197
 -1.315081  ]
Sparsity at: 0.036061561158798286
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6405e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37384456  0.508336
 -1.3154365 ]
Sparsity at: 0.036061561158798286
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6484e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37381685  0.5081033
 -1.3157908 ]
Sparsity at: 0.036061561158798286
Epoch 324/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6306e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.3737876   0.50780493
 -1.3161472 ]
Sparsity at: 0.036061561158798286
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5729e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37375033  0.50752443
 -1.3164933 ]
Sparsity at: 0.036061561158798286
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5570e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.373706    0.5072209
 -1.3168185 ]
Sparsity at: 0.036061561158798286
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6385e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37369674  0.5069379
 -1.317186  ]
Sparsity at: 0.036061561158798286
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5551e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37367252  0.50666493
 -1.3175242 ]
Sparsity at: 0.036061561158798286
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5829e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37363446  0.5063862
 -1.3178571 ]
Sparsity at: 0.036061561158798286
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5868e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37362015  0.50613254
 -1.3181936 ]
Sparsity at: 0.036061561158798286
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5888e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9727
[-0.05940218 -0.00601314 -0.04628057 ...  0.37359676  0.5058522
 -1.3185344 ]
Sparsity at: 0.036061561158798286
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5570e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37358427  0.5055932
 -1.3188852 ]
Sparsity at: 0.036061561158798286
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5332e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3735248   0.5052797
 -1.319237  ]
Sparsity at: 0.036061561158798286
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5829e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37347952  0.50502115
 -1.319602  ]
Sparsity at: 0.036061561158798286
Epoch 335/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5650e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37346476  0.5047515
 -1.3199508 ]
Sparsity at: 0.036061561158798286
Epoch 336/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5133e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37342778  0.5044435
 -1.3202884 ]
Sparsity at: 0.036061561158798286
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5113e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37338725  0.50414145
 -1.3206134 ]
Sparsity at: 0.036061561158798286
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5471e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37335595  0.50383985
 -1.3209686 ]
Sparsity at: 0.036061561158798286
Epoch 339/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5590e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.373305    0.5035547
 -1.3213125 ]
Sparsity at: 0.036061561158798286
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4736e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3732648   0.50323546
 -1.3216718 ]
Sparsity at: 0.036061561158798286
Epoch 341/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5630e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37322775  0.5029671
 -1.322041  ]
Sparsity at: 0.036061561158798286
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4855e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37320125  0.50266546
 -1.3223925 ]
Sparsity at: 0.036061561158798286
Epoch 343/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4815e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3731738   0.50238395
 -1.3227296 ]
Sparsity at: 0.036061561158798286
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5312e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37312672  0.5020975
 -1.3230311 ]
Sparsity at: 0.036061561158798286
Epoch 345/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4498e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37309828  0.50178486
 -1.3233533 ]
Sparsity at: 0.036061561158798286
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4776e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3730637   0.5014854
 -1.3236705 ]
Sparsity at: 0.036061561158798286
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4736e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37304652  0.50119287
 -1.3240117 ]
Sparsity at: 0.036061561158798286
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5153e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37302724  0.5009105
 -1.3243608 ]
Sparsity at: 0.036061561158798286
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5233e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37298167  0.50063175
 -1.3247061 ]
Sparsity at: 0.036061561158798286
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4994e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37296593  0.5003334
 -1.3250321 ]
Sparsity at: 0.036061561158798286
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.4925995599953019
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.6619620323722089
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 1.592738571268356
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10078125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 2.4577e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37293306  0.50002563
 -1.3253579 ]
Sparsity at: 0.036061561158798286
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 2.5014e-09 - accuracy: 1.0000 - val_loss: 0.3258 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37291762  0.49973774
 -1.325719  ]
Sparsity at: 0.036061561158798286
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37286827  0.49941763
 -1.3260385 ]
Sparsity at: 0.036061561158798286
Epoch 354/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4915e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.372805    0.49911255
 -1.3263956 ]
Sparsity at: 0.036061561158798286
Epoch 355/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4259e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37277442  0.49881005
 -1.3267217 ]
Sparsity at: 0.036061561158798286
Epoch 356/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4597e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37272745  0.49850523
 -1.3270619 ]
Sparsity at: 0.036061561158798286
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5411e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37269673  0.4982139
 -1.3274156 ]
Sparsity at: 0.036061561158798286
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37266386  0.49790472
 -1.3277262 ]
Sparsity at: 0.036061561158798286
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4299e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37262422  0.4975905
 -1.3280598 ]
Sparsity at: 0.036061561158798286
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4378e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37258506  0.49726906
 -1.3283741 ]
Sparsity at: 0.036061561158798286
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37255433  0.49696404
 -1.3287165 ]
Sparsity at: 0.036061561158798286
Epoch 362/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5133e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37253544  0.4966704
 -1.3290659 ]
Sparsity at: 0.036061561158798286
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4041e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37249577  0.49634728
 -1.3293978 ]
Sparsity at: 0.036061561158798286
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4557e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3724566   0.49606547
 -1.3297406 ]
Sparsity at: 0.036061561158798286
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4438e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37240618  0.49576166
 -1.3300943 ]
Sparsity at: 0.036061561158798286
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4478e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37235138  0.49544197
 -1.3304348 ]
Sparsity at: 0.036061561158798286
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4319e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37230977  0.49512142
 -1.3307742 ]
Sparsity at: 0.036061561158798286
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37225637  0.4948127
 -1.331085  ]
Sparsity at: 0.036061561158798286
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4656e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37224564  0.49451214
 -1.3314052 ]
Sparsity at: 0.036061561158798286
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4239e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37220725  0.49418625
 -1.3317423 ]
Sparsity at: 0.036061561158798286
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37214747  0.49387428
 -1.3320663 ]
Sparsity at: 0.036061561158798286
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4259e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37212116  0.49353817
 -1.3324158 ]
Sparsity at: 0.036061561158798286
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4319e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3720977   0.49320933
 -1.3327707 ]
Sparsity at: 0.036061561158798286
Epoch 374/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37207803  0.4929086
 -1.3331056 ]
Sparsity at: 0.036061561158798286
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3842e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37203288  0.49258214
 -1.3334275 ]
Sparsity at: 0.036061561158798286
Epoch 376/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3862e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.3719744   0.49223903
 -1.3337499 ]
Sparsity at: 0.036061561158798286
Epoch 377/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37194026  0.49191517
 -1.3341159 ]
Sparsity at: 0.036061561158798286
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4219e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37190172  0.49160987
 -1.3344594 ]
Sparsity at: 0.036061561158798286
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4080e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37185317  0.49130413
 -1.3348198 ]
Sparsity at: 0.036061561158798286
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3941e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37179795  0.4909519
 -1.3351437 ]
Sparsity at: 0.036061561158798286
Epoch 381/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37175176  0.49062988
 -1.3354775 ]
Sparsity at: 0.036061561158798286
Epoch 382/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4060e-09 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3716944   0.49028948
 -1.3358223 ]
Sparsity at: 0.036061561158798286
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4239e-09 - accuracy: 1.0000 - val_loss: 0.3250 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.37167186  0.48997888
 -1.3361493 ]
Sparsity at: 0.036061561158798286
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3643e-09 - accuracy: 1.0000 - val_loss: 0.3250 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37163135  0.48963788
 -1.3364975 ]
Sparsity at: 0.036061561158798286
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4199e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37158456  0.48931134
 -1.3368276 ]
Sparsity at: 0.036061561158798286
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3683e-09 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3715391   0.4889384
 -1.3371792 ]
Sparsity at: 0.036061561158798286
Epoch 387/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3250 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.3714875   0.48861992
 -1.3375167 ]
Sparsity at: 0.036061561158798286
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3723e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37146088  0.488283
 -1.3378537 ]
Sparsity at: 0.036061561158798286
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3941e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37140742  0.48795485
 -1.3381863 ]
Sparsity at: 0.036061561158798286
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4498e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37136656  0.48763773
 -1.3385106 ]
Sparsity at: 0.036061561158798286
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37130266  0.48729149
 -1.3388795 ]
Sparsity at: 0.036061561158798286
Epoch 392/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37123728  0.48697668
 -1.3392172 ]
Sparsity at: 0.036061561158798286
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4199e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37120995  0.48662707
 -1.3395646 ]
Sparsity at: 0.036061561158798286
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3711828   0.4863032
 -1.3399073 ]
Sparsity at: 0.036061561158798286
Epoch 395/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37115878  0.4859548
 -1.3402303 ]
Sparsity at: 0.036061561158798286
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3802e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37108728  0.4856252
 -1.3405764 ]
Sparsity at: 0.036061561158798286
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4339e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37105125  0.4852889
 -1.3409209 ]
Sparsity at: 0.036061561158798286
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37097406  0.48496714
 -1.3412572 ]
Sparsity at: 0.036061561158798286
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3709361   0.48463473
 -1.3416064 ]
Sparsity at: 0.036061561158798286
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37088767  0.48430985
 -1.341956  ]
Sparsity at: 0.036061561158798286
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.5341157585249476
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.6928729482168308
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.24682617
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 1.679269350849495
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10078125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 44s 7ms/step - loss: 2.3822e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37083155  0.48395914
 -1.3422847 ]
Sparsity at: 0.036061561158798286
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 2.3623e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37077317  0.48360687
 -1.3426182 ]
Sparsity at: 0.036061561158798286
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4060e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37071675  0.4832838
 -1.3429725 ]
Sparsity at: 0.036061561158798286
Epoch 404/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3706646   0.482978
 -1.3433253 ]
Sparsity at: 0.036061561158798286
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3782e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.3705867   0.48265088
 -1.3436669 ]
Sparsity at: 0.036061561158798286
Epoch 406/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3683e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9732
[-0.05940218 -0.00601314 -0.04628057 ...  0.37056762  0.4822972
 -1.3440218 ]
Sparsity at: 0.036061561158798286
Epoch 407/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37050194  0.48196664
 -1.3443604 ]
Sparsity at: 0.036061561158798286
Epoch 408/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37046096  0.4816298
 -1.3446752 ]
Sparsity at: 0.036061561158798286
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4080e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3704135   0.48127213
 -1.3450248 ]
Sparsity at: 0.036061561158798286
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37035987  0.48093697
 -1.3453923 ]
Sparsity at: 0.036061561158798286
Epoch 411/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.37032816  0.48057675
 -1.3457308 ]
Sparsity at: 0.036061561158798286
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4339e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37028325  0.48025593
 -1.3460753 ]
Sparsity at: 0.036061561158798286
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37021753  0.47994345
 -1.3464363 ]
Sparsity at: 0.036061561158798286
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3701749   0.47960788
 -1.3467789 ]
Sparsity at: 0.036061561158798286
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.37013832  0.47925252
 -1.3471206 ]
Sparsity at: 0.036061561158798286
Epoch 416/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3286e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.37009537  0.47892454
 -1.3474555 ]
Sparsity at: 0.036061561158798286
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3862e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3700527   0.4785859
 -1.3478229 ]
Sparsity at: 0.036061561158798286
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36999473  0.478222
 -1.3481259 ]
Sparsity at: 0.036061561158798286
Epoch 419/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3723e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3699416   0.47786346
 -1.3484771 ]
Sparsity at: 0.036061561158798286
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3698776   0.47752187
 -1.3488457 ]
Sparsity at: 0.036061561158798286
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36983067  0.47714835
 -1.3491694 ]
Sparsity at: 0.036061561158798286
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3697891   0.4767999
 -1.3495294 ]
Sparsity at: 0.036061561158798286
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3623e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.36975327  0.47645083
 -1.3498878 ]
Sparsity at: 0.036061561158798286
Epoch 424/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3901e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3696886   0.4761272
 -1.3502464 ]
Sparsity at: 0.036061561158798286
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3544e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3696453   0.47577834
 -1.3505902 ]
Sparsity at: 0.036061561158798286
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36959687  0.4754619
 -1.3509238 ]
Sparsity at: 0.036061561158798286
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3643e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36953333  0.47511104
 -1.3512607 ]
Sparsity at: 0.036061561158798286
Epoch 428/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3842e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36947006  0.47477496
 -1.3516172 ]
Sparsity at: 0.036061561158798286
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3882e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3694455   0.4744407
 -1.3519588 ]
Sparsity at: 0.036061561158798286
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3693838   0.47409728
 -1.352285  ]
Sparsity at: 0.036061561158798286
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3325e-09 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36933798  0.47371745
 -1.3526344 ]
Sparsity at: 0.036061561158798286
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36925972  0.47338074
 -1.3529731 ]
Sparsity at: 0.036061561158798286
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36919358  0.47304606
 -1.353327  ]
Sparsity at: 0.036061561158798286
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3544e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36913842  0.47269753
 -1.3536979 ]
Sparsity at: 0.036061561158798286
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3305e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36907166  0.4723477
 -1.354055  ]
Sparsity at: 0.036061561158798286
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3743e-09 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36902648  0.47201052
 -1.3543962 ]
Sparsity at: 0.036061561158798286
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4041e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36894777  0.4716593
 -1.35473   ]
Sparsity at: 0.036061561158798286
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3688989   0.47129542
 -1.3550823 ]
Sparsity at: 0.036061561158798286
Epoch 439/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36883956  0.47092095
 -1.355445  ]
Sparsity at: 0.036061561158798286
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4100e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36880812  0.47058257
 -1.3557858 ]
Sparsity at: 0.036061561158798286
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3643e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3687541   0.47022122
 -1.3561058 ]
Sparsity at: 0.036061561158798286
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36870337  0.46985197
 -1.356464  ]
Sparsity at: 0.036061561158798286
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3743e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36864144  0.46947452
 -1.356834  ]
Sparsity at: 0.036061561158798286
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36857134  0.46912
 -1.357185  ]
Sparsity at: 0.036061561158798286
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3683e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3685082   0.46874776
 -1.3575491 ]
Sparsity at: 0.036061561158798286
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3842e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36844727  0.46838197
 -1.3579353 ]
Sparsity at: 0.036061561158798286
Epoch 447/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3246e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36836934  0.4679863
 -1.3582811 ]
Sparsity at: 0.036061561158798286
Epoch 448/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4001e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3683137   0.4676689
 -1.3586267 ]
Sparsity at: 0.036061561158798286
Epoch 449/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36823508  0.4672976
 -1.3589816 ]
Sparsity at: 0.036061561158798286
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3681587   0.46693128
 -1.3593597 ]
Sparsity at: 0.036061561158798286
Epoch 451/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3882e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36812142  0.4665764
 -1.3596872 ]
Sparsity at: 0.036061561158798286
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3365e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3680569   0.46619973
 -1.3600336 ]
Sparsity at: 0.036061561158798286
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4021e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36803848  0.46586603
 -1.3603746 ]
Sparsity at: 0.036061561158798286
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3679608   0.4655147
 -1.3607421 ]
Sparsity at: 0.036061561158798286
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4080e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36789814  0.46516043
 -1.3610622 ]
Sparsity at: 0.036061561158798286
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36784092  0.4647933
 -1.3614473 ]
Sparsity at: 0.036061561158798286
Epoch 457/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3677795   0.4644324
 -1.3618213 ]
Sparsity at: 0.036061561158798286
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3584e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36770603  0.4640646
 -1.3621607 ]
Sparsity at: 0.036061561158798286
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36762768  0.4636927
 -1.3625153 ]
Sparsity at: 0.036061561158798286
Epoch 460/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4180e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3675584   0.4633437
 -1.3628732 ]
Sparsity at: 0.036061561158798286
Epoch 461/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3643e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3675012   0.4629514
 -1.363202  ]
Sparsity at: 0.036061561158798286
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3901e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36747074  0.46261394
 -1.3635626 ]
Sparsity at: 0.036061561158798286
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3127e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3673833   0.4622463
 -1.3639078 ]
Sparsity at: 0.036061561158798286
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36730486  0.46190125
 -1.3642585 ]
Sparsity at: 0.036061561158798286
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36722836  0.46153262
 -1.364602  ]
Sparsity at: 0.036061561158798286
Epoch 466/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3723e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36714482  0.46113583
 -1.3649713 ]
Sparsity at: 0.036061561158798286
Epoch 467/500
235/235 [==============================] - 2s 10ms/step - loss: 2.3325e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.36706755  0.46076614
 -1.3653077 ]
Sparsity at: 0.036061561158798286
Epoch 468/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4140e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9728
[-0.05940218 -0.00601314 -0.04628057 ...  0.3669996   0.4604226
 -1.3656886 ]
Sparsity at: 0.036061561158798286
Epoch 469/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.36693448  0.46006322
 -1.3660595 ]
Sparsity at: 0.036061561158798286
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3683e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36686295  0.45970774
 -1.3664263 ]
Sparsity at: 0.036061561158798286
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3663e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3667913   0.4593291
 -1.3667823 ]
Sparsity at: 0.036061561158798286
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4001e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3667364   0.45897734
 -1.3671376 ]
Sparsity at: 0.036061561158798286
Epoch 473/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.366681    0.45860302
 -1.3675181 ]
Sparsity at: 0.036061561158798286
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3666014   0.45821998
 -1.3678557 ]
Sparsity at: 0.036061561158798286
Epoch 475/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.36653644  0.45786613
 -1.3682197 ]
Sparsity at: 0.036061561158798286
Epoch 476/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3663e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36644208  0.45747906
 -1.3685647 ]
Sparsity at: 0.036061561158798286
Epoch 477/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3901e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36638945  0.4571207
 -1.3689424 ]
Sparsity at: 0.036061561158798286
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3802e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.366341    0.45675144
 -1.3692974 ]
Sparsity at: 0.036061561158798286
Epoch 479/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36623833  0.45635846
 -1.369663  ]
Sparsity at: 0.036061561158798286
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36614075  0.45598853
 -1.3700464 ]
Sparsity at: 0.036061561158798286
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3802e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.36609447  0.4556002
 -1.370387  ]
Sparsity at: 0.036061561158798286
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3405e-09 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36599296  0.45522544
 -1.3707418 ]
Sparsity at: 0.036061561158798286
Epoch 483/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3842e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3659192   0.4548735
 -1.3711203 ]
Sparsity at: 0.036061561158798286
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3663e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9729
[-0.05940218 -0.00601314 -0.04628057 ...  0.3658312   0.45448342
 -1.3715    ]
Sparsity at: 0.036061561158798286
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3882e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3657436   0.45409387
 -1.3718641 ]
Sparsity at: 0.036061561158798286
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4041e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3656828   0.4537486
 -1.3722153 ]
Sparsity at: 0.036061561158798286
Epoch 487/500
235/235 [==============================] - 2s 10ms/step - loss: 2.3544e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.36558267  0.45338312
 -1.372578  ]
Sparsity at: 0.036061561158798286
Epoch 488/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4041e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36550596  0.45301488
 -1.3729552 ]
Sparsity at: 0.036061561158798286
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3882e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.3654373   0.45263305
 -1.3733386 ]
Sparsity at: 0.036061561158798286
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.36535847  0.45225954
 -1.3736976 ]
Sparsity at: 0.036061561158798286
Epoch 491/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3802e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36529544  0.4518772
 -1.3740878 ]
Sparsity at: 0.036061561158798286
Epoch 492/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36520702  0.45152915
 -1.3744223 ]
Sparsity at: 0.036061561158798286
Epoch 493/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3584e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36515135  0.45115298
 -1.3747709 ]
Sparsity at: 0.036061561158798286
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3623e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36506167  0.4507676
 -1.375137  ]
Sparsity at: 0.036061561158798286
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.3649902   0.4503905
 -1.37551   ]
Sparsity at: 0.036061561158798286
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.3649184   0.45002145
 -1.3758777 ]
Sparsity at: 0.036061561158798286
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4140e-09 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.36485904  0.44965377
 -1.376249  ]
Sparsity at: 0.036061561158798286
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36477286  0.4492806
 -1.3766314 ]
Sparsity at: 0.036061561158798286
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9731
[-0.05940218 -0.00601314 -0.04628057 ...  0.36469167  0.44891402
 -1.37698   ]
Sparsity at: 0.036061561158798286
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9730
[-0.05940218 -0.00601314 -0.04628057 ...  0.36462858  0.44854546
 -1.3773383 ]
Sparsity at: 0.036061561158798286
Epoch 1/500
235/235 [==============================] - 6s 15ms/step - loss: 0.1403 - accuracy: 0.9783 - val_loss: 0.1873 - val_accuracy: 0.9651
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1387 - accuracy: 0.9782 - val_loss: 0.1958 - val_accuracy: 0.9632
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1340 - accuracy: 0.9802 - val_loss: 0.2116 - val_accuracy: 0.9588
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1397 - accuracy: 0.9782 - val_loss: 0.1913 - val_accuracy: 0.9629
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1355 - accuracy: 0.9798 - val_loss: 0.2005 - val_accuracy: 0.9613
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9800 - val_loss: 0.2257 - val_accuracy: 0.9540
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1398 - accuracy: 0.9786 - val_loss: 0.2263 - val_accuracy: 0.9546
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1433 - accuracy: 0.9778 - val_loss: 0.1851 - val_accuracy: 0.9669
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1388 - accuracy: 0.9790 - val_loss: 0.1904 - val_accuracy: 0.9627
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9793 - val_loss: 0.1901 - val_accuracy: 0.9653
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.2118 - val_accuracy: 0.9588
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1364 - accuracy: 0.9786 - val_loss: 0.2235 - val_accuracy: 0.9557
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9792 - val_loss: 0.2041 - val_accuracy: 0.9611
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9794 - val_loss: 0.1801 - val_accuracy: 0.9684
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9792 - val_loss: 0.2342 - val_accuracy: 0.9528
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9793 - val_loss: 0.2231 - val_accuracy: 0.9547
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1385 - accuracy: 0.9788 - val_loss: 0.1919 - val_accuracy: 0.9640
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1391 - accuracy: 0.9788 - val_loss: 0.1892 - val_accuracy: 0.9644
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9804 - val_loss: 0.1988 - val_accuracy: 0.9632
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1393 - accuracy: 0.9784 - val_loss: 0.2229 - val_accuracy: 0.9561
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9792 - val_loss: 0.2131 - val_accuracy: 0.9582
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1404 - accuracy: 0.9787 - val_loss: 0.2037 - val_accuracy: 0.9623
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1390 - accuracy: 0.9787 - val_loss: 0.2463 - val_accuracy: 0.9506
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1395 - accuracy: 0.9786 - val_loss: 0.1975 - val_accuracy: 0.9637
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1365 - accuracy: 0.9793 - val_loss: 0.2048 - val_accuracy: 0.9604
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9794 - val_loss: 0.2202 - val_accuracy: 0.9559
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9806 - val_loss: 0.1845 - val_accuracy: 0.9670
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9786 - val_loss: 0.2082 - val_accuracy: 0.9607
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9797 - val_loss: 0.2040 - val_accuracy: 0.9617
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1362 - accuracy: 0.9798 - val_loss: 0.2005 - val_accuracy: 0.9636
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9792 - val_loss: 0.1859 - val_accuracy: 0.9658
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9792 - val_loss: 0.1854 - val_accuracy: 0.9668
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9795 - val_loss: 0.1904 - val_accuracy: 0.9656
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1364 - accuracy: 0.9789 - val_loss: 0.1952 - val_accuracy: 0.9648
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1376 - accuracy: 0.9785 - val_loss: 0.2161 - val_accuracy: 0.9568
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9792 - val_loss: 0.2101 - val_accuracy: 0.9602
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1338 - accuracy: 0.9803 - val_loss: 0.2150 - val_accuracy: 0.9581
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9788 - val_loss: 0.2102 - val_accuracy: 0.9590
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9788 - val_loss: 0.2399 - val_accuracy: 0.9498
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1391 - accuracy: 0.9787 - val_loss: 0.2240 - val_accuracy: 0.9549
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1388 - accuracy: 0.9789 - val_loss: 0.1893 - val_accuracy: 0.9652
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1354 - accuracy: 0.9798 - val_loss: 0.1970 - val_accuracy: 0.9608
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9783 - val_loss: 0.1779 - val_accuracy: 0.9677
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9787 - val_loss: 0.1842 - val_accuracy: 0.9661
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1373 - accuracy: 0.9794 - val_loss: 0.2602 - val_accuracy: 0.9450
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1359 - accuracy: 0.9797 - val_loss: 0.2042 - val_accuracy: 0.9587
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1359 - accuracy: 0.9797 - val_loss: 0.2571 - val_accuracy: 0.9477
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1414 - accuracy: 0.9779 - val_loss: 0.1932 - val_accuracy: 0.9655
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1369 - accuracy: 0.9793 - val_loss: 0.1956 - val_accuracy: 0.9640
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.2351 - val_accuracy: 0.9528
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9793 - val_loss: 0.2418 - val_accuracy: 0.9494
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9789 - val_loss: 0.1742 - val_accuracy: 0.9685
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9794 - val_loss: 0.1958 - val_accuracy: 0.9611
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1355 - accuracy: 0.9794 - val_loss: 0.1890 - val_accuracy: 0.9638
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9788 - val_loss: 0.2076 - val_accuracy: 0.9612
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1370 - accuracy: 0.9789 - val_loss: 0.2106 - val_accuracy: 0.9582
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1370 - accuracy: 0.9793 - val_loss: 0.2237 - val_accuracy: 0.9542
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9792 - val_loss: 0.2138 - val_accuracy: 0.9601
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9791 - val_loss: 0.1834 - val_accuracy: 0.9672
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9795 - val_loss: 0.2028 - val_accuracy: 0.9635
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1387 - accuracy: 0.9790 - val_loss: 0.1870 - val_accuracy: 0.9638
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1330 - accuracy: 0.9808 - val_loss: 0.2053 - val_accuracy: 0.9625
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9784 - val_loss: 0.1949 - val_accuracy: 0.9650
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9790 - val_loss: 0.2478 - val_accuracy: 0.9468
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9787 - val_loss: 0.1917 - val_accuracy: 0.9650
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1346 - accuracy: 0.9801 - val_loss: 0.2344 - val_accuracy: 0.9495
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9785 - val_loss: 0.2420 - val_accuracy: 0.9488
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9790 - val_loss: 0.2098 - val_accuracy: 0.9588
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9783 - val_loss: 0.1903 - val_accuracy: 0.9645
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9802 - val_loss: 0.2147 - val_accuracy: 0.9569
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9798 - val_loss: 0.2125 - val_accuracy: 0.9576
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1344 - accuracy: 0.9800 - val_loss: 0.2387 - val_accuracy: 0.9523
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9795 - val_loss: 0.2173 - val_accuracy: 0.9564
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1355 - accuracy: 0.9798 - val_loss: 0.2255 - val_accuracy: 0.9541
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1355 - accuracy: 0.9796 - val_loss: 0.1874 - val_accuracy: 0.9663
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9792 - val_loss: 0.2018 - val_accuracy: 0.9617
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1323 - accuracy: 0.9807 - val_loss: 0.2060 - val_accuracy: 0.9592
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9786 - val_loss: 0.3392 - val_accuracy: 0.9240
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1355 - accuracy: 0.9801 - val_loss: 0.2371 - val_accuracy: 0.9523
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1374 - accuracy: 0.9793 - val_loss: 0.1959 - val_accuracy: 0.9631
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1397 - accuracy: 0.9775 - val_loss: 0.2244 - val_accuracy: 0.9542
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1390 - accuracy: 0.9794 - val_loss: 0.2300 - val_accuracy: 0.9530
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9790 - val_loss: 0.2263 - val_accuracy: 0.9550
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1355 - accuracy: 0.9795 - val_loss: 0.2218 - val_accuracy: 0.9546
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.1891 - val_accuracy: 0.9631
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1369 - accuracy: 0.9792 - val_loss: 0.2006 - val_accuracy: 0.9615
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1344 - accuracy: 0.9799 - val_loss: 0.1894 - val_accuracy: 0.9646
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9783 - val_loss: 0.2056 - val_accuracy: 0.9627
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1348 - accuracy: 0.9805 - val_loss: 0.2141 - val_accuracy: 0.9568
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1356 - accuracy: 0.9794 - val_loss: 0.2044 - val_accuracy: 0.9606
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1334 - accuracy: 0.9799 - val_loss: 0.2450 - val_accuracy: 0.9490
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1335 - accuracy: 0.9801 - val_loss: 0.2308 - val_accuracy: 0.9518
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1393 - accuracy: 0.9782 - val_loss: 0.2086 - val_accuracy: 0.9570
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9792 - val_loss: 0.2727 - val_accuracy: 0.9393
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.2316 - val_accuracy: 0.9526
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9789 - val_loss: 0.2023 - val_accuracy: 0.9596
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1365 - accuracy: 0.9793 - val_loss: 0.2094 - val_accuracy: 0.9584
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9787 - val_loss: 0.2270 - val_accuracy: 0.9507
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1327 - accuracy: 0.9800 - val_loss: 0.2416 - val_accuracy: 0.9507
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9800 - val_loss: 0.2189 - val_accuracy: 0.9557
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9787 - val_loss: 0.2090 - val_accuracy: 0.9605
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1409 - accuracy: 0.9776 - val_loss: 0.2981 - val_accuracy: 0.9343
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1327 - accuracy: 0.9805 - val_loss: 0.2048 - val_accuracy: 0.9596
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1324 - accuracy: 0.9794 - val_loss: 0.2451 - val_accuracy: 0.9484
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1395 - accuracy: 0.9783 - val_loss: 0.2074 - val_accuracy: 0.9617
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9793 - val_loss: 0.2639 - val_accuracy: 0.9418
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1376 - accuracy: 0.9789 - val_loss: 0.2201 - val_accuracy: 0.9558
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1374 - accuracy: 0.9796 - val_loss: 0.1994 - val_accuracy: 0.9630
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1385 - accuracy: 0.9788 - val_loss: 0.1765 - val_accuracy: 0.9687
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1373 - accuracy: 0.9790 - val_loss: 0.1824 - val_accuracy: 0.9667
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9782 - val_loss: 0.1925 - val_accuracy: 0.9637
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9799 - val_loss: 0.2151 - val_accuracy: 0.9560
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1390 - accuracy: 0.9787 - val_loss: 0.1811 - val_accuracy: 0.9674
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1360 - accuracy: 0.9791 - val_loss: 0.1892 - val_accuracy: 0.9645
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9786 - val_loss: 0.1807 - val_accuracy: 0.9661
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1330 - accuracy: 0.9796 - val_loss: 0.1965 - val_accuracy: 0.9627
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9784 - val_loss: 0.2119 - val_accuracy: 0.9604
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9803 - val_loss: 0.2320 - val_accuracy: 0.9525
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9791 - val_loss: 0.1870 - val_accuracy: 0.9634
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9787 - val_loss: 0.2263 - val_accuracy: 0.9576
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9794 - val_loss: 0.2407 - val_accuracy: 0.9505
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1376 - accuracy: 0.9794 - val_loss: 0.1989 - val_accuracy: 0.9644
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1384 - accuracy: 0.9783 - val_loss: 0.2073 - val_accuracy: 0.9589
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1351 - accuracy: 0.9803 - val_loss: 0.2003 - val_accuracy: 0.9598
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1376 - accuracy: 0.9785 - val_loss: 0.2095 - val_accuracy: 0.9592
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1414 - accuracy: 0.9776 - val_loss: 0.1903 - val_accuracy: 0.9649
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9790 - val_loss: 0.2043 - val_accuracy: 0.9622
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.2226 - val_accuracy: 0.9561
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9778 - val_loss: 0.1923 - val_accuracy: 0.9643
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9792 - val_loss: 0.2100 - val_accuracy: 0.9582
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9792 - val_loss: 0.2170 - val_accuracy: 0.9582
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1392 - accuracy: 0.9784 - val_loss: 0.2202 - val_accuracy: 0.9564
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9790 - val_loss: 0.2047 - val_accuracy: 0.9589
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9790 - val_loss: 0.2026 - val_accuracy: 0.9614
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9789 - val_loss: 0.2044 - val_accuracy: 0.9624
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1357 - accuracy: 0.9789 - val_loss: 0.2143 - val_accuracy: 0.9568
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1373 - accuracy: 0.9783 - val_loss: 0.2096 - val_accuracy: 0.9576
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1348 - accuracy: 0.9791 - val_loss: 0.2134 - val_accuracy: 0.9583
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9785 - val_loss: 0.1945 - val_accuracy: 0.9649
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9792 - val_loss: 0.1949 - val_accuracy: 0.9647
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1321 - accuracy: 0.9798 - val_loss: 0.1957 - val_accuracy: 0.9613
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1393 - accuracy: 0.9783 - val_loss: 0.2115 - val_accuracy: 0.9591
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1322 - accuracy: 0.9804 - val_loss: 0.2010 - val_accuracy: 0.9607
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9789 - val_loss: 0.1984 - val_accuracy: 0.9661
[0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00
 0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9782 - val_loss: 0.2356 - val_accuracy: 0.9523
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1366 - accuracy: 0.9793 - val_loss: 0.1937 - val_accuracy: 0.9638
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1366 - accuracy: 0.9790 - val_loss: 0.2022 - val_accuracy: 0.9633
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1322 - accuracy: 0.9805 - val_loss: 0.2189 - val_accuracy: 0.9540
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1348 - accuracy: 0.9790 - val_loss: 0.2127 - val_accuracy: 0.9567
[ 0.000000e+00  0.000000e+00  4.737296e-34 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9781 - val_loss: 0.2017 - val_accuracy: 0.9585
[ 0.000000e+00  0.000000e+00  4.737296e-34 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1389 - accuracy: 0.9779 - val_loss: 0.2069 - val_accuracy: 0.9584
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9784 - val_loss: 0.2122 - val_accuracy: 0.9573
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1373 - accuracy: 0.9785 - val_loss: 0.1985 - val_accuracy: 0.9652
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9790 - val_loss: 0.2066 - val_accuracy: 0.9603
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1384 - accuracy: 0.9783 - val_loss: 0.2213 - val_accuracy: 0.9548
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1380 - accuracy: 0.9788 - val_loss: 0.1882 - val_accuracy: 0.9662
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9783 - val_loss: 0.1963 - val_accuracy: 0.9624
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9785 - val_loss: 0.2364 - val_accuracy: 0.9493
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9799 - val_loss: 0.1858 - val_accuracy: 0.9647
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.2111 - val_accuracy: 0.9564
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1399 - accuracy: 0.9786 - val_loss: 0.2703 - val_accuracy: 0.9396
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1356 - accuracy: 0.9792 - val_loss: 0.2278 - val_accuracy: 0.9511
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9799 - val_loss: 0.2560 - val_accuracy: 0.9455
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1389 - accuracy: 0.9785 - val_loss: 0.1926 - val_accuracy: 0.9633
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1354 - accuracy: 0.9794 - val_loss: 0.2433 - val_accuracy: 0.9491
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9791 - val_loss: 0.1915 - val_accuracy: 0.9641
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1341 - accuracy: 0.9797 - val_loss: 0.2287 - val_accuracy: 0.9523
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9793 - val_loss: 0.2659 - val_accuracy: 0.9428
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1353 - accuracy: 0.9796 - val_loss: 0.2035 - val_accuracy: 0.9596
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.2084 - val_accuracy: 0.9598
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9790 - val_loss: 0.2164 - val_accuracy: 0.9555
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1367 - accuracy: 0.9789 - val_loss: 0.2448 - val_accuracy: 0.9463
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9790 - val_loss: 0.2010 - val_accuracy: 0.9617
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1385 - accuracy: 0.9783 - val_loss: 0.1971 - val_accuracy: 0.9634
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.2238 - val_accuracy: 0.9536
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1389 - accuracy: 0.9781 - val_loss: 0.2101 - val_accuracy: 0.9587
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1337 - accuracy: 0.9803 - val_loss: 0.2134 - val_accuracy: 0.9564
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1334 - accuracy: 0.9801 - val_loss: 0.1925 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1343 - accuracy: 0.9796 - val_loss: 0.1971 - val_accuracy: 0.9627
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.2013 - val_accuracy: 0.9621
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9774 - val_loss: 0.1991 - val_accuracy: 0.9640
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9781 - val_loss: 0.2090 - val_accuracy: 0.9597
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1348 - accuracy: 0.9789 - val_loss: 0.2100 - val_accuracy: 0.9600
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1359 - accuracy: 0.9791 - val_loss: 0.2031 - val_accuracy: 0.9601
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9800 - val_loss: 0.2124 - val_accuracy: 0.9590
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9791 - val_loss: 0.2415 - val_accuracy: 0.9450
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9790 - val_loss: 0.1889 - val_accuracy: 0.9651
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9796 - val_loss: 0.2252 - val_accuracy: 0.9554
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9791 - val_loss: 0.2120 - val_accuracy: 0.9566
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9790 - val_loss: 0.1998 - val_accuracy: 0.9606
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1334 - accuracy: 0.9797 - val_loss: 0.1824 - val_accuracy: 0.9658
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1343 - accuracy: 0.9790 - val_loss: 0.2202 - val_accuracy: 0.9551
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9783 - val_loss: 0.1957 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9794 - val_loss: 0.2089 - val_accuracy: 0.9586
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9790 - val_loss: 0.2311 - val_accuracy: 0.9538
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9807 - val_loss: 0.2020 - val_accuracy: 0.9610
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1366 - accuracy: 0.9786 - val_loss: 0.2173 - val_accuracy: 0.9545
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1357 - accuracy: 0.9791 - val_loss: 0.2127 - val_accuracy: 0.9560
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1333 - accuracy: 0.9796 - val_loss: 0.1967 - val_accuracy: 0.9623
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1332 - accuracy: 0.9793 - val_loss: 0.1962 - val_accuracy: 0.9633
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1415 - accuracy: 0.9773 - val_loss: 0.1961 - val_accuracy: 0.9623
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1389 - accuracy: 0.9783 - val_loss: 0.1838 - val_accuracy: 0.9659
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1400 - accuracy: 0.9776 - val_loss: 0.2199 - val_accuracy: 0.9568
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1407 - accuracy: 0.9783 - val_loss: 0.2031 - val_accuracy: 0.9595
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9784 - val_loss: 0.2360 - val_accuracy: 0.9506
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9779 - val_loss: 0.1816 - val_accuracy: 0.9672
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9786 - val_loss: 0.2046 - val_accuracy: 0.9624
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9786 - val_loss: 0.1926 - val_accuracy: 0.9622
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1375 - accuracy: 0.9783 - val_loss: 0.1992 - val_accuracy: 0.9639
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9781 - val_loss: 0.1974 - val_accuracy: 0.9632
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9780 - val_loss: 0.2283 - val_accuracy: 0.9553
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1419 - accuracy: 0.9774 - val_loss: 0.2697 - val_accuracy: 0.9422
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1430 - accuracy: 0.9775 - val_loss: 0.2648 - val_accuracy: 0.9427
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1391 - accuracy: 0.9779 - val_loss: 0.2051 - val_accuracy: 0.9616
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9781 - val_loss: 0.2300 - val_accuracy: 0.9549
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9777 - val_loss: 0.2268 - val_accuracy: 0.9543
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9782 - val_loss: 0.1947 - val_accuracy: 0.9632
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1392 - accuracy: 0.9778 - val_loss: 0.2059 - val_accuracy: 0.9593
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.2035 - val_accuracy: 0.9593
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9783 - val_loss: 0.1939 - val_accuracy: 0.9620
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9785 - val_loss: 0.2149 - val_accuracy: 0.9564
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.2192 - val_accuracy: 0.9567
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9780 - val_loss: 0.2352 - val_accuracy: 0.9530
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9789 - val_loss: 0.2006 - val_accuracy: 0.9617
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9778 - val_loss: 0.1888 - val_accuracy: 0.9629
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9783 - val_loss: 0.2058 - val_accuracy: 0.9619
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9781 - val_loss: 0.2516 - val_accuracy: 0.9470
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1340 - accuracy: 0.9792 - val_loss: 0.2043 - val_accuracy: 0.9585
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9785 - val_loss: 0.2100 - val_accuracy: 0.9576
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9780 - val_loss: 0.2087 - val_accuracy: 0.9588
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1397 - accuracy: 0.9779 - val_loss: 0.2016 - val_accuracy: 0.9604
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9790 - val_loss: 0.2500 - val_accuracy: 0.9461
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9782 - val_loss: 0.1963 - val_accuracy: 0.9642
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1385 - accuracy: 0.9781 - val_loss: 0.2393 - val_accuracy: 0.9513
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1376 - accuracy: 0.9777 - val_loss: 0.1952 - val_accuracy: 0.9630
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2104 - val_accuracy: 0.9592
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9785 - val_loss: 0.2008 - val_accuracy: 0.9621
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9786 - val_loss: 0.2021 - val_accuracy: 0.9610
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9792 - val_loss: 0.2233 - val_accuracy: 0.9563
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9787 - val_loss: 0.1952 - val_accuracy: 0.9632
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9790 - val_loss: 0.3014 - val_accuracy: 0.9312
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1394 - accuracy: 0.9775 - val_loss: 0.2264 - val_accuracy: 0.9525
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9787 - val_loss: 0.1988 - val_accuracy: 0.9605
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9780 - val_loss: 0.2520 - val_accuracy: 0.9437
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1351 - accuracy: 0.9797 - val_loss: 0.2343 - val_accuracy: 0.9508
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9776 - val_loss: 0.2053 - val_accuracy: 0.9591
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9778 - val_loss: 0.2262 - val_accuracy: 0.9521
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9779 - val_loss: 0.2048 - val_accuracy: 0.9606
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1351 - accuracy: 0.9792 - val_loss: 0.2143 - val_accuracy: 0.9568
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.2295 - val_accuracy: 0.9545
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1387 - accuracy: 0.9772 - val_loss: 0.1938 - val_accuracy: 0.9616
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1286 - accuracy: 0.9789 - val_loss: 0.1954 - val_accuracy: 0.9597
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1298 - accuracy: 0.9789 - val_loss: 0.1946 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1250 - accuracy: 0.9794 - val_loss: 0.1806 - val_accuracy: 0.9641
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1278 - accuracy: 0.9781 - val_loss: 0.1864 - val_accuracy: 0.9645
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1237 - accuracy: 0.9800 - val_loss: 0.2007 - val_accuracy: 0.9589
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1241 - accuracy: 0.9789 - val_loss: 0.1949 - val_accuracy: 0.9614
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1213 - accuracy: 0.9805 - val_loss: 0.1846 - val_accuracy: 0.9647
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1243 - accuracy: 0.9794 - val_loss: 0.2035 - val_accuracy: 0.9602
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1228 - accuracy: 0.9799 - val_loss: 0.1898 - val_accuracy: 0.9614
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9803 - val_loss: 0.1767 - val_accuracy: 0.9661
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9807 - val_loss: 0.1887 - val_accuracy: 0.9622
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1199 - accuracy: 0.9803 - val_loss: 0.1784 - val_accuracy: 0.9652
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1213 - accuracy: 0.9800 - val_loss: 0.1941 - val_accuracy: 0.9599
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1199 - accuracy: 0.9802 - val_loss: 0.1819 - val_accuracy: 0.9654
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9800 - val_loss: 0.1772 - val_accuracy: 0.9623
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1221 - accuracy: 0.9797 - val_loss: 0.1939 - val_accuracy: 0.9577
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1191 - accuracy: 0.9805 - val_loss: 0.1704 - val_accuracy: 0.9694
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9799 - val_loss: 0.1813 - val_accuracy: 0.9667
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1208 - accuracy: 0.9801 - val_loss: 0.1777 - val_accuracy: 0.9667
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1174 - accuracy: 0.9815 - val_loss: 0.1890 - val_accuracy: 0.9635
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1188 - accuracy: 0.9804 - val_loss: 0.1913 - val_accuracy: 0.9619
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1198 - accuracy: 0.9810 - val_loss: 0.1957 - val_accuracy: 0.9597
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9801 - val_loss: 0.1800 - val_accuracy: 0.9660
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9801 - val_loss: 0.1816 - val_accuracy: 0.9653
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1216 - accuracy: 0.9799 - val_loss: 0.1778 - val_accuracy: 0.9651
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9804 - val_loss: 0.1834 - val_accuracy: 0.9630
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1181 - accuracy: 0.9807 - val_loss: 0.1871 - val_accuracy: 0.9634
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1196 - accuracy: 0.9804 - val_loss: 0.1760 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1166 - accuracy: 0.9809 - val_loss: 0.1859 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1196 - accuracy: 0.9801 - val_loss: 0.2102 - val_accuracy: 0.9594
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1195 - accuracy: 0.9808 - val_loss: 0.1661 - val_accuracy: 0.9677
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1179 - accuracy: 0.9812 - val_loss: 0.1874 - val_accuracy: 0.9631
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1185 - accuracy: 0.9803 - val_loss: 0.1859 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1168 - accuracy: 0.9811 - val_loss: 0.1919 - val_accuracy: 0.9605
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1169 - accuracy: 0.9813 - val_loss: 0.1749 - val_accuracy: 0.9661
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1213 - accuracy: 0.9798 - val_loss: 0.1855 - val_accuracy: 0.9638
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1192 - accuracy: 0.9805 - val_loss: 0.1871 - val_accuracy: 0.9634
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1198 - accuracy: 0.9803 - val_loss: 0.1619 - val_accuracy: 0.9685
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1177 - accuracy: 0.9814 - val_loss: 0.1959 - val_accuracy: 0.9585
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1195 - accuracy: 0.9809 - val_loss: 0.2133 - val_accuracy: 0.9559
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1208 - accuracy: 0.9806 - val_loss: 0.1857 - val_accuracy: 0.9640
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9811 - val_loss: 0.1688 - val_accuracy: 0.9674
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1162 - accuracy: 0.9811 - val_loss: 0.1850 - val_accuracy: 0.9629
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9809 - val_loss: 0.1937 - val_accuracy: 0.9628
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9807 - val_loss: 0.2106 - val_accuracy: 0.9546
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9811 - val_loss: 0.1862 - val_accuracy: 0.9608
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1182 - accuracy: 0.9806 - val_loss: 0.1692 - val_accuracy: 0.9654
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1203 - accuracy: 0.9799 - val_loss: 0.2061 - val_accuracy: 0.9551
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9816 - val_loss: 0.1710 - val_accuracy: 0.9653
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1266 - accuracy: 0.9768 - val_loss: 0.1696 - val_accuracy: 0.9639
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1147 - accuracy: 0.9797 - val_loss: 0.1664 - val_accuracy: 0.9660
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9807 - val_loss: 0.1784 - val_accuracy: 0.9628
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1104 - accuracy: 0.9809 - val_loss: 0.1762 - val_accuracy: 0.9626
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1098 - accuracy: 0.9809 - val_loss: 0.1721 - val_accuracy: 0.9635
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1060 - accuracy: 0.9829 - val_loss: 0.1674 - val_accuracy: 0.9650
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1060 - accuracy: 0.9814 - val_loss: 0.1659 - val_accuracy: 0.9667
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1065 - accuracy: 0.9816 - val_loss: 0.1746 - val_accuracy: 0.9655
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1047 - accuracy: 0.9826 - val_loss: 0.1615 - val_accuracy: 0.9672
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1054 - accuracy: 0.9815 - val_loss: 0.1696 - val_accuracy: 0.9664
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1042 - accuracy: 0.9822 - val_loss: 0.1612 - val_accuracy: 0.9668
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1036 - accuracy: 0.9822 - val_loss: 0.1777 - val_accuracy: 0.9622
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1037 - accuracy: 0.9825 - val_loss: 0.1758 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1050 - accuracy: 0.9817 - val_loss: 0.1718 - val_accuracy: 0.9656
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1013 - accuracy: 0.9828 - val_loss: 0.1727 - val_accuracy: 0.9643
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1038 - accuracy: 0.9812 - val_loss: 0.1646 - val_accuracy: 0.9671
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1010 - accuracy: 0.9828 - val_loss: 0.1663 - val_accuracy: 0.9681
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1019 - accuracy: 0.9826 - val_loss: 0.1713 - val_accuracy: 0.9644
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1011 - accuracy: 0.9827 - val_loss: 0.1655 - val_accuracy: 0.9656
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9819 - val_loss: 0.1674 - val_accuracy: 0.9658
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1011 - accuracy: 0.9829 - val_loss: 0.1750 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1038 - accuracy: 0.9809 - val_loss: 0.1560 - val_accuracy: 0.9692
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1014 - accuracy: 0.9822 - val_loss: 0.1677 - val_accuracy: 0.9645
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1006 - accuracy: 0.9833 - val_loss: 0.1735 - val_accuracy: 0.9636
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1012 - accuracy: 0.9827 - val_loss: 0.1763 - val_accuracy: 0.9625
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1023 - accuracy: 0.9821 - val_loss: 0.1694 - val_accuracy: 0.9654
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1013 - accuracy: 0.9823 - val_loss: 0.1815 - val_accuracy: 0.9593
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1011 - accuracy: 0.9825 - val_loss: 0.1793 - val_accuracy: 0.9610
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1026 - accuracy: 0.9819 - val_loss: 0.1716 - val_accuracy: 0.9653
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1009 - accuracy: 0.9826 - val_loss: 0.1745 - val_accuracy: 0.9647
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1014 - accuracy: 0.9824 - val_loss: 0.1517 - val_accuracy: 0.9701
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0990 - accuracy: 0.9833 - val_loss: 0.1885 - val_accuracy: 0.9614
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1021 - accuracy: 0.9821 - val_loss: 0.1612 - val_accuracy: 0.9675
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0998 - accuracy: 0.9831 - val_loss: 0.1705 - val_accuracy: 0.9663
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1016 - accuracy: 0.9821 - val_loss: 0.1571 - val_accuracy: 0.9695
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1001 - accuracy: 0.9825 - val_loss: 0.1773 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0999 - accuracy: 0.9832 - val_loss: 0.1671 - val_accuracy: 0.9642
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1017 - accuracy: 0.9826 - val_loss: 0.1660 - val_accuracy: 0.9667
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1027 - accuracy: 0.9815 - val_loss: 0.1780 - val_accuracy: 0.9630
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1010 - accuracy: 0.9826 - val_loss: 0.1610 - val_accuracy: 0.9681
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 4s 18ms/step - loss: 0.0990 - accuracy: 0.9830 - val_loss: 0.1669 - val_accuracy: 0.9660
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1020 - accuracy: 0.9822 - val_loss: 0.1636 - val_accuracy: 0.9675
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1054 - accuracy: 0.9810 - val_loss: 0.1852 - val_accuracy: 0.9585
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1062 - accuracy: 0.9806 - val_loss: 0.1856 - val_accuracy: 0.9591
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1047 - accuracy: 0.9812 - val_loss: 0.1844 - val_accuracy: 0.9610
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1016 - accuracy: 0.9825 - val_loss: 0.1889 - val_accuracy: 0.9605
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1015 - accuracy: 0.9822 - val_loss: 0.1848 - val_accuracy: 0.9621
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1023 - accuracy: 0.9821 - val_loss: 0.1809 - val_accuracy: 0.9612
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1044 - accuracy: 0.9816 - val_loss: 0.1749 - val_accuracy: 0.9623
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1024 - accuracy: 0.9825 - val_loss: 0.1667 - val_accuracy: 0.9659
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1413 - accuracy: 0.9699 - val_loss: 0.1588 - val_accuracy: 0.9686
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1208 - accuracy: 0.9757 - val_loss: 0.1632 - val_accuracy: 0.9677
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1192 - accuracy: 0.9758 - val_loss: 0.1623 - val_accuracy: 0.9661
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1145 - accuracy: 0.9766 - val_loss: 0.1604 - val_accuracy: 0.9661
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1154 - accuracy: 0.9768 - val_loss: 0.1646 - val_accuracy: 0.9667
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1132 - accuracy: 0.9772 - val_loss: 0.1601 - val_accuracy: 0.9669
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1122 - accuracy: 0.9772 - val_loss: 0.1616 - val_accuracy: 0.9660
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9771 - val_loss: 0.1635 - val_accuracy: 0.9664
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1094 - accuracy: 0.9788 - val_loss: 0.1724 - val_accuracy: 0.9624
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1109 - accuracy: 0.9781 - val_loss: 0.1655 - val_accuracy: 0.9669
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1100 - accuracy: 0.9780 - val_loss: 0.1769 - val_accuracy: 0.9638
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1093 - accuracy: 0.9779 - val_loss: 0.1691 - val_accuracy: 0.9658
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1093 - accuracy: 0.9784 - val_loss: 0.1627 - val_accuracy: 0.9678
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1095 - accuracy: 0.9776 - val_loss: 0.1678 - val_accuracy: 0.9669
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1072 - accuracy: 0.9789 - val_loss: 0.1750 - val_accuracy: 0.9640
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1098 - accuracy: 0.9781 - val_loss: 0.1654 - val_accuracy: 0.9667
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1067 - accuracy: 0.9789 - val_loss: 0.1692 - val_accuracy: 0.9642
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1085 - accuracy: 0.9787 - val_loss: 0.1792 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1074 - accuracy: 0.9786 - val_loss: 0.1715 - val_accuracy: 0.9650
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1080 - accuracy: 0.9784 - val_loss: 0.1753 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1081 - accuracy: 0.9784 - val_loss: 0.1566 - val_accuracy: 0.9682
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1074 - accuracy: 0.9785 - val_loss: 0.1784 - val_accuracy: 0.9642
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1056 - accuracy: 0.9798 - val_loss: 0.1736 - val_accuracy: 0.9655
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1057 - accuracy: 0.9790 - val_loss: 0.1643 - val_accuracy: 0.9660
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1061 - accuracy: 0.9790 - val_loss: 0.1571 - val_accuracy: 0.9674
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1060 - accuracy: 0.9790 - val_loss: 0.1732 - val_accuracy: 0.9654
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1075 - accuracy: 0.9781 - val_loss: 0.1768 - val_accuracy: 0.9610
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1066 - accuracy: 0.9783 - val_loss: 0.1733 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1051 - accuracy: 0.9794 - val_loss: 0.1571 - val_accuracy: 0.9688
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1063 - accuracy: 0.9794 - val_loss: 0.1804 - val_accuracy: 0.9614
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1083 - accuracy: 0.9781 - val_loss: 0.1781 - val_accuracy: 0.9633
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1066 - accuracy: 0.9783 - val_loss: 0.1613 - val_accuracy: 0.9671
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1055 - accuracy: 0.9794 - val_loss: 0.1717 - val_accuracy: 0.9646
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1059 - accuracy: 0.9786 - val_loss: 0.1841 - val_accuracy: 0.9623
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1070 - accuracy: 0.9786 - val_loss: 0.1724 - val_accuracy: 0.9625
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1060 - accuracy: 0.9792 - val_loss: 0.1781 - val_accuracy: 0.9623
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1061 - accuracy: 0.9786 - val_loss: 0.1626 - val_accuracy: 0.9665
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1076 - accuracy: 0.9783 - val_loss: 0.1742 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1054 - accuracy: 0.9786 - val_loss: 0.1680 - val_accuracy: 0.9655
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1065 - accuracy: 0.9789 - val_loss: 0.1626 - val_accuracy: 0.9666
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1045 - accuracy: 0.9796 - val_loss: 0.1650 - val_accuracy: 0.9654
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1066 - accuracy: 0.9791 - val_loss: 0.1661 - val_accuracy: 0.9674
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1052 - accuracy: 0.9790 - val_loss: 0.1657 - val_accuracy: 0.9669
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1049 - accuracy: 0.9792 - val_loss: 0.1741 - val_accuracy: 0.9636
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1040 - accuracy: 0.9789 - val_loss: 0.1776 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1065 - accuracy: 0.9785 - val_loss: 0.1603 - val_accuracy: 0.9669
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1051 - accuracy: 0.9792 - val_loss: 0.1734 - val_accuracy: 0.9642
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1041 - accuracy: 0.9795 - val_loss: 0.1835 - val_accuracy: 0.9614
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1055 - accuracy: 0.9790 - val_loss: 0.1853 - val_accuracy: 0.9603
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1062 - accuracy: 0.9786 - val_loss: 0.1591 - val_accuracy: 0.9673
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1449 - accuracy: 0.9711 - val_loss: 0.1867 - val_accuracy: 0.9596
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1256 - accuracy: 0.9753 - val_loss: 0.1668 - val_accuracy: 0.9664
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1217 - accuracy: 0.9756 - val_loss: 0.1673 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1177 - accuracy: 0.9764 - val_loss: 0.1787 - val_accuracy: 0.9621
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1178 - accuracy: 0.9761 - val_loss: 0.1725 - val_accuracy: 0.9653
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1175 - accuracy: 0.9761 - val_loss: 0.1777 - val_accuracy: 0.9620
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1186 - accuracy: 0.9754 - val_loss: 0.1688 - val_accuracy: 0.9648
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1152 - accuracy: 0.9768 - val_loss: 0.1877 - val_accuracy: 0.9606
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1149 - accuracy: 0.9767 - val_loss: 0.1836 - val_accuracy: 0.9603
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1161 - accuracy: 0.9759 - val_loss: 0.1715 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1131 - accuracy: 0.9774 - val_loss: 0.1730 - val_accuracy: 0.9642
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1132 - accuracy: 0.9771 - val_loss: 0.1654 - val_accuracy: 0.9647
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1142 - accuracy: 0.9766 - val_loss: 0.1761 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1131 - accuracy: 0.9766 - val_loss: 0.1780 - val_accuracy: 0.9618
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1136 - accuracy: 0.9768 - val_loss: 0.1697 - val_accuracy: 0.9636
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1142 - accuracy: 0.9762 - val_loss: 0.1804 - val_accuracy: 0.9627
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1131 - accuracy: 0.9769 - val_loss: 0.1777 - val_accuracy: 0.9623
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1134 - accuracy: 0.9769 - val_loss: 0.1797 - val_accuracy: 0.9617
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1138 - accuracy: 0.9766 - val_loss: 0.1729 - val_accuracy: 0.9641
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1139 - accuracy: 0.9767 - val_loss: 0.1760 - val_accuracy: 0.9619
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9767 - val_loss: 0.1777 - val_accuracy: 0.9622
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1144 - accuracy: 0.9770 - val_loss: 0.1707 - val_accuracy: 0.9629
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1130 - accuracy: 0.9769 - val_loss: 0.1777 - val_accuracy: 0.9626
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1134 - accuracy: 0.9763 - val_loss: 0.1814 - val_accuracy: 0.9608
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1133 - accuracy: 0.9767 - val_loss: 0.1769 - val_accuracy: 0.9611
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1135 - accuracy: 0.9767 - val_loss: 0.1673 - val_accuracy: 0.9656
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1121 - accuracy: 0.9773 - val_loss: 0.1777 - val_accuracy: 0.9615
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1124 - accuracy: 0.9767 - val_loss: 0.1747 - val_accuracy: 0.9647
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1128 - accuracy: 0.9768 - val_loss: 0.1713 - val_accuracy: 0.9643
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1123 - accuracy: 0.9769 - val_loss: 0.1718 - val_accuracy: 0.9633
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9771 - val_loss: 0.1755 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1121 - accuracy: 0.9773 - val_loss: 0.1694 - val_accuracy: 0.9636
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1118 - accuracy: 0.9768 - val_loss: 0.1649 - val_accuracy: 0.9644
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1132 - accuracy: 0.9764 - val_loss: 0.1632 - val_accuracy: 0.9657
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9771 - val_loss: 0.1830 - val_accuracy: 0.9605
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1118 - accuracy: 0.9770 - val_loss: 0.1626 - val_accuracy: 0.9649
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1124 - accuracy: 0.9769 - val_loss: 0.1736 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9771 - val_loss: 0.1673 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9765 - val_loss: 0.1716 - val_accuracy: 0.9642
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1126 - accuracy: 0.9765 - val_loss: 0.1687 - val_accuracy: 0.9639
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1116 - accuracy: 0.9769 - val_loss: 0.1677 - val_accuracy: 0.9643
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1122 - accuracy: 0.9765 - val_loss: 0.1698 - val_accuracy: 0.9640
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9767 - val_loss: 0.1774 - val_accuracy: 0.9620
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1129 - accuracy: 0.9765 - val_loss: 0.1596 - val_accuracy: 0.9663
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1111 - accuracy: 0.9765 - val_loss: 0.1720 - val_accuracy: 0.9633
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9767 - val_loss: 0.1657 - val_accuracy: 0.9636
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9769 - val_loss: 0.1732 - val_accuracy: 0.9634
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1113 - accuracy: 0.9769 - val_loss: 0.1714 - val_accuracy: 0.9628
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1121 - accuracy: 0.9768 - val_loss: 0.1683 - val_accuracy: 0.9639
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1111 - accuracy: 0.9774 - val_loss: 0.1764 - val_accuracy: 0.9628
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1121 - accuracy: 0.9765 - val_loss: 0.1809 - val_accuracy: 0.9607
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9771 - val_loss: 0.1701 - val_accuracy: 0.9632
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1118 - accuracy: 0.9771 - val_loss: 0.1769 - val_accuracy: 0.9618
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1124 - accuracy: 0.9767 - val_loss: 0.1696 - val_accuracy: 0.9640
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9772 - val_loss: 0.1681 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1120 - accuracy: 0.9762 - val_loss: 0.1836 - val_accuracy: 0.9602
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1128 - accuracy: 0.9764 - val_loss: 0.1632 - val_accuracy: 0.9661
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1107 - accuracy: 0.9772 - val_loss: 0.1711 - val_accuracy: 0.9633
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1129 - accuracy: 0.9769 - val_loss: 0.1675 - val_accuracy: 0.9648
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9768 - val_loss: 0.1667 - val_accuracy: 0.9653
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1120 - accuracy: 0.9773 - val_loss: 0.1749 - val_accuracy: 0.9634
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1112 - accuracy: 0.9774 - val_loss: 0.1709 - val_accuracy: 0.9653
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9767 - val_loss: 0.1746 - val_accuracy: 0.9619
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1114 - accuracy: 0.9768 - val_loss: 0.1807 - val_accuracy: 0.9616
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1104 - accuracy: 0.9776 - val_loss: 0.1674 - val_accuracy: 0.9651
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1115 - accuracy: 0.9769 - val_loss: 0.1738 - val_accuracy: 0.9645
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9768 - val_loss: 0.1731 - val_accuracy: 0.9622
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1130 - accuracy: 0.9767 - val_loss: 0.1732 - val_accuracy: 0.9621
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1126 - accuracy: 0.9769 - val_loss: 0.1679 - val_accuracy: 0.9653
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1120 - accuracy: 0.9770 - val_loss: 0.1798 - val_accuracy: 0.9608
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9764 - val_loss: 0.1662 - val_accuracy: 0.9662
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1106 - accuracy: 0.9771 - val_loss: 0.1749 - val_accuracy: 0.9618
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1128 - accuracy: 0.9767 - val_loss: 0.1659 - val_accuracy: 0.9646
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1103 - accuracy: 0.9773 - val_loss: 0.1717 - val_accuracy: 0.9642
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1121 - accuracy: 0.9765 - val_loss: 0.1777 - val_accuracy: 0.9635
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1120 - accuracy: 0.9768 - val_loss: 0.1750 - val_accuracy: 0.9636
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1116 - accuracy: 0.9770 - val_loss: 0.1688 - val_accuracy: 0.9629
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1131 - accuracy: 0.9765 - val_loss: 0.1855 - val_accuracy: 0.9606
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1109 - accuracy: 0.9774 - val_loss: 0.1750 - val_accuracy: 0.9639
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1122 - accuracy: 0.9763 - val_loss: 0.1714 - val_accuracy: 0.9624
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1123 - accuracy: 0.9766 - val_loss: 0.1747 - val_accuracy: 0.9623
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9767 - val_loss: 0.1691 - val_accuracy: 0.9635
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1110 - accuracy: 0.9769 - val_loss: 0.1731 - val_accuracy: 0.9617
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1114 - accuracy: 0.9766 - val_loss: 0.1773 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1128 - accuracy: 0.9763 - val_loss: 0.1707 - val_accuracy: 0.9630
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1116 - accuracy: 0.9771 - val_loss: 0.1775 - val_accuracy: 0.9618
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1127 - accuracy: 0.9764 - val_loss: 0.1705 - val_accuracy: 0.9639
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1112 - accuracy: 0.9770 - val_loss: 0.1646 - val_accuracy: 0.9645
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9767 - val_loss: 0.1631 - val_accuracy: 0.9645
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1125 - accuracy: 0.9763 - val_loss: 0.1672 - val_accuracy: 0.9650
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9759 - val_loss: 0.1744 - val_accuracy: 0.9618
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1122 - accuracy: 0.9764 - val_loss: 0.1654 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9763 - val_loss: 0.1661 - val_accuracy: 0.9650
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1113 - accuracy: 0.9773 - val_loss: 0.1715 - val_accuracy: 0.9644
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1103 - accuracy: 0.9771 - val_loss: 0.1691 - val_accuracy: 0.9614
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1104 - accuracy: 0.9777 - val_loss: 0.1684 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1115 - accuracy: 0.9769 - val_loss: 0.1698 - val_accuracy: 0.9646
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1115 - accuracy: 0.9767 - val_loss: 0.1677 - val_accuracy: 0.9648
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1116 - accuracy: 0.9769 - val_loss: 0.1673 - val_accuracy: 0.9647
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1108 - accuracy: 0.9775 - val_loss: 0.1662 - val_accuracy: 0.9656
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 5s 15ms/step - loss: 5.8590e-04 - accuracy: 0.9999 - val_loss: 0.0975 - val_accuracy: 0.9833
[-0.         -0.         -0.         ...  0.40781373 -0.6702625
  0.        ]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6419e-04 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9843
[-0.         -0.         -0.         ...  0.41248244 -0.67458117
  0.        ]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8298e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9836
[-0.         -0.         -0.         ...  0.41890672 -0.6830235
 -0.        ]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4828e-05 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.42215222 -0.6881104
 -0.        ]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3218e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.42413443 -0.6933588
  0.        ]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7352e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.43164828 -0.69543904
 -0.        ]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 15ms/step - loss: 2.0002e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9843
[-0.        -0.        -0.        ...  0.4254886 -0.6936554 -0.       ]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5560e-05 - accuracy: 1.0000 - val_loss: 0.0983 - val_accuracy: 0.9837
[-0.         -0.         -0.         ...  0.42678383 -0.7083477
  0.        ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1227e-04 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9838
[-0.         -0.         -0.         ...  0.42854083 -0.70800877
 -0.        ]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 8.4910e-04 - accuracy: 0.9998 - val_loss: 0.1064 - val_accuracy: 0.9831
[-0.         -0.         -0.         ...  0.43906522 -0.6968431
 -0.        ]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 6.6107e-04 - accuracy: 0.9998 - val_loss: 0.1063 - val_accuracy: 0.9819
[-0.         -0.         -0.         ...  0.43379018 -0.6912776
 -0.        ]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4326e-04 - accuracy: 0.9998 - val_loss: 0.1106 - val_accuracy: 0.9829
[-0.         -0.         -0.         ...  0.43622416 -0.7270986
 -0.        ]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0611e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9832
[-0.         -0.         -0.         ...  0.43800637 -0.7380075
 -0.        ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3358e-04 - accuracy: 0.9999 - val_loss: 0.1059 - val_accuracy: 0.9830
[-0.         -0.         -0.         ...  0.4373247  -0.74203116
  0.        ]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 5.9579e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9834
[-0.        -0.        -0.        ...  0.4375715 -0.7389662 -0.       ]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5458e-05 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9835
[-0.         -0.         -0.         ...  0.43618035 -0.74026626
 -0.        ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2224e-05 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9842
[-0.         -0.         -0.         ...  0.43202552 -0.74114835
 -0.        ]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4911e-04 - accuracy: 0.9999 - val_loss: 0.1052 - val_accuracy: 0.9834
[-0.         -0.         -0.         ...  0.43032756 -0.7426719
 -0.        ]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0142e-04 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9835
[-0.         -0.         -0.         ...  0.42278704 -0.7434096
 -0.        ]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7043e-05 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9832
[-0.         -0.         -0.         ...  0.42467904 -0.7440927
 -0.        ]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3111e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9832
[-0.         -0.         -0.         ...  0.42599857 -0.7450728
  0.        ]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6380e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9839
[-0.        -0.        -0.        ...  0.4295014 -0.7415794 -0.       ]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3886e-05 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9837
[-0.         -0.         -0.         ...  0.42286417 -0.73140395
 -0.        ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2524e-05 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9836
[-0.         -0.         -0.         ...  0.42159817 -0.73365486
 -0.        ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5957e-06 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9836
[-0.         -0.         -0.         ...  0.42173636 -0.73345643
  0.        ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0742e-04 - accuracy: 0.9999 - val_loss: 0.1253 - val_accuracy: 0.9815
[-0.        -0.        -0.        ...  0.4075093 -0.7376263  0.       ]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1180e-04 - accuracy: 0.9999 - val_loss: 0.1041 - val_accuracy: 0.9847
[-0.         -0.         -0.         ...  0.41434512 -0.73106277
 -0.        ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 9.2438e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9837
[-0.         -0.         -0.         ...  0.4216225  -0.73189074
 -0.        ]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2084e-04 - accuracy: 0.9999 - val_loss: 0.1037 - val_accuracy: 0.9836
[-0.         -0.         -0.         ...  0.42373183 -0.74150616
 -0.        ]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9443e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9844
[-0.        -0.        -0.        ...  0.4275105 -0.7408081 -0.       ]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3234e-04 - accuracy: 1.0000 - val_loss: 0.1044 - val_accuracy: 0.9847
[-0.         -0.         -0.         ...  0.4299386  -0.73702914
 -0.        ]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5078e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9849
[-0.         -0.         -0.         ...  0.4322655  -0.73715705
 -0.        ]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9856e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9844
[-0.         -0.         -0.         ...  0.4370121  -0.73787594
 -0.        ]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7946e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9842
[-0.         -0.         -0.         ...  0.43955818 -0.73869187
  0.        ]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9338e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9841
[-0.         -0.         -0.         ...  0.43910703 -0.7376661
 -0.        ]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0792e-05 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9843
[-0.         -0.         -0.         ...  0.44001165 -0.7384584
 -0.        ]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 8.9759e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9837
[-0.         -0.         -0.         ...  0.44181287 -0.7369027
 -0.        ]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 5.9020e-06 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.44363716 -0.73844874
 -0.        ]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8925e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9844
[-0.         -0.         -0.         ...  0.44543317 -0.73909175
 -0.        ]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0013e-06 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9840
[-0.        -0.        -0.        ...  0.4471137 -0.7392173  0.       ]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4834e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.44829175 -0.7396168
 -0.        ]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9493e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9841
[-0.         -0.         -0.         ...  0.44931117 -0.74025536
  0.        ]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4696e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.45113707 -0.7405083
 -0.        ]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7436e-06 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9840
[-0.        -0.        -0.        ...  0.4510588 -0.7410652 -0.       ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2679e-06 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9837
[-0.         -0.         -0.         ...  0.45323738 -0.7415785
 -0.        ]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3470e-06 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.4553067  -0.74186593
 -0.        ]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0194e-06 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.45586807 -0.7420865
  0.        ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7708e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.4580327  -0.74233294
  0.        ]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0250e-06 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.46276712 -0.74283844
 -0.        ]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5852e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.46342826 -0.74296623
 -0.        ]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.1026 - val_accuracy: 0.9824
[-0.         -0.         -0.         ...  0.48178092 -0.6867249
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8456e-04 - accuracy: 0.9998 - val_loss: 0.0968 - val_accuracy: 0.9837
[-0.         -0.         -0.         ...  0.46431142 -0.68329424
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0848e-04 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.46596488 -0.686497
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6918e-05 - accuracy: 1.0000 - val_loss: 0.0983 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.46603048 -0.6869063
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0014e-04 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.46607792 -0.6856161
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7721e-05 - accuracy: 1.0000 - val_loss: 0.0998 - val_accuracy: 0.9842
[-0.         -0.         -0.         ...  0.46618637 -0.68781096
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3912e-05 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9838
[-0.         -0.         -0.         ...  0.46792325 -0.6875334
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6974e-05 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9838
[-0.         -0.         -0.         ...  0.46795285 -0.68625325
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6255e-05 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.45842677 -0.685627
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5480e-05 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.46004164 -0.6832966
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5547e-05 - accuracy: 1.0000 - val_loss: 0.1013 - val_accuracy: 0.9843
[-0.         -0.         -0.         ...  0.46158594 -0.6847793
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2447e-04 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9849
[-0.         -0.         -0.         ...  0.47115117 -0.68501985
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4952e-05 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 0.9849
[-0.         -0.         -0.         ...  0.47229823 -0.68571186
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7700e-05 - accuracy: 1.0000 - val_loss: 0.1010 - val_accuracy: 0.9847
[-0.         -0.         -0.         ...  0.47182652 -0.6856034
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7152e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9843
[-0.         -0.         -0.         ...  0.47207537 -0.6851744
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2995e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9838
[-0.         -0.         -0.         ...  0.47209558 -0.67741156
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2551e-04 - accuracy: 0.9999 - val_loss: 0.1073 - val_accuracy: 0.9841
[-0.         -0.         -0.         ...  0.47031793 -0.68388474
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8513e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9835
[-0.         -0.         -0.         ...  0.46896514 -0.6831152
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6792e-05 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9843
[-0.        -0.        -0.        ...  0.4695946 -0.689099  -0.       ]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4817e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9840
[-0.        -0.        -0.        ...  0.4688582 -0.6888881  0.       ]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1332e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.46795753 -0.6889045
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 9.8736e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.46993044 -0.68950456
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 8.8188e-06 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.47209388 -0.6897388
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 7.9155e-06 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9839
[-0.         -0.         -0.         ...  0.47312075 -0.689822
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6444e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9846
[-0.         -0.         -0.         ...  0.474284   -0.69014466
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6727e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9836
[-0.         -0.         -0.         ...  0.47651836 -0.69178396
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 7.9058e-06 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9838
[-0.         -0.         -0.         ...  0.47965217 -0.6917678
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8798e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9842
[-0.         -0.         -0.         ...  0.47856498 -0.6900147
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1881e-06 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9842
[-0.         -0.         -0.         ...  0.48199216 -0.6930432
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6779e-06 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9841
[-0.         -0.         -0.         ...  0.48422685 -0.69352424
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2228e-06 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9840
[-0.         -0.         -0.         ...  0.48736748 -0.69443977
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5577e-06 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9834
[-0.         -0.         -0.         ...  0.49136993 -0.6949908
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6086e-06 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9835
[-0.         -0.         -0.         ...  0.49325502 -0.6958957
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4208e-05 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9837
[-0.         -0.         -0.         ...  0.49402264 -0.6936562
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1432e-04 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9829
[-0.         -0.         -0.         ...  0.5013987  -0.70756125
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0645e-04 - accuracy: 0.9998 - val_loss: 0.1252 - val_accuracy: 0.9826
[-0.        -0.        -0.        ...  0.513968  -0.7227696 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6715e-04 - accuracy: 0.9999 - val_loss: 0.1138 - val_accuracy: 0.9847
[-0.         -0.         -0.         ...  0.52013856 -0.71966434
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5452e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9841
[-0.         -0.         -0.         ...  0.51841974 -0.7110601
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1851e-05 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9845
[-0.         -0.         -0.         ...  0.51782084 -0.71312135
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2202e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9845
[-0.        -0.        -0.        ...  0.5179846 -0.7156176  0.       ]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0490e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9845
[-0.        -0.        -0.        ...  0.5184238 -0.7164969 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 8.8522e-06 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9847
[-0.         -0.         -0.         ...  0.51874816 -0.7172678
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3946e-05 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9852
[-0.        -0.        -0.        ...  0.5191075 -0.7183986 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8614e-06 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9850
[-0.        -0.        -0.        ...  0.5189788 -0.7187473  0.       ]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1649e-06 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9851
[-0.         -0.         -0.         ...  0.5190248  -0.71854764
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8409e-06 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9849
[-0.        -0.        -0.        ...  0.5190666 -0.7194177 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4370e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9847
[-0.         -0.         -0.         ...  0.5193495  -0.72256726
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 4s 15ms/step - loss: 4.8004e-06 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9850
[-0.         -0.         -0.         ...  0.51888883 -0.72128445
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4695e-06 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9849
[-0.        -0.        -0.        ...  0.5184803 -0.7217318  0.       ]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3234e-06 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9850
[-0.         -0.         -0.         ...  0.51923686 -0.72159654
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9973 - val_loss: 0.1107 - val_accuracy: 0.9812
[-0.        -0.        -0.        ...  0.        -0.7090838 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3727e-04 - accuracy: 0.9998 - val_loss: 0.1043 - val_accuracy: 0.9811
[-0.         -0.         -0.         ...  0.         -0.70824796
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7084e-04 - accuracy: 0.9999 - val_loss: 0.1019 - val_accuracy: 0.9824
[-0.        -0.        -0.        ...  0.        -0.7100668 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2604e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9821
[-0.        -0.        -0.        ...  0.        -0.7165425 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4502e-04 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9821
[-0.        -0.        -0.        ...  0.        -0.7190276 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3564e-04 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9820
[-0.        -0.        -0.        ...  0.        -0.7206364 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4328e-04 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9818
[-0.        -0.        -0.        ... -0.        -0.7302709  0.       ]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6617e-04 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9816
[-0.         -0.         -0.         ...  0.         -0.73292136
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7988e-05 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9827
[-0.         -0.         -0.         ...  0.         -0.72863805
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3119e-04 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9821
[-0.         -0.         -0.         ... -0.         -0.72989786
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6388e-05 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9826
[-0.        -0.        -0.        ...  0.        -0.7320777 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3514e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9829
[-0.         -0.         -0.         ...  0.         -0.73680156
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7401e-05 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9827
[-0.        -0.        -0.        ...  0.        -0.7392906 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7103e-05 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9825
[-0.         -0.         -0.         ...  0.         -0.74020344
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0599e-05 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9829
[-0.         -0.         -0.         ...  0.         -0.74142325
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9157e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9830
[-0.         -0.         -0.         ...  0.         -0.74358857
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 9.9986e-05 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9836
[-0.        -0.        -0.        ...  0.        -0.7496931 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4055e-05 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9835
[-0.         -0.         -0.         ...  0.         -0.76006985
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1105e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9831
[-0.         -0.         -0.         ...  0.         -0.75977385
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4541e-05 - accuracy: 1.0000 - val_loss: 0.1041 - val_accuracy: 0.9833
[-0.        -0.        -0.        ...  0.        -0.7584823  0.       ]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3889e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9837
[-0.         -0.         -0.         ...  0.         -0.75596267
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 4s 15ms/step - loss: 2.2982e-05 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9835
[-0.         -0.         -0.         ... -0.         -0.75933176
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1371e-05 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9833
[-0.         -0.         -0.         ... -0.         -0.75925076
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1428e-05 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9834
[-0.        -0.        -0.        ...  0.        -0.7564262 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8006e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9832
[-0.        -0.        -0.        ...  0.        -0.7622098 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 15ms/step - loss: 8.9676e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9819
[-0.        -0.        -0.        ...  0.        -0.7654799  0.       ]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6773e-04 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9823
[-0.         -0.         -0.         ...  0.         -0.76584786
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0419e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9820
[-0.        -0.        -0.        ... -0.        -0.7734696  0.       ]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1962e-05 - accuracy: 1.0000 - val_loss: 0.1117 - val_accuracy: 0.9832
[-0.         -0.         -0.         ...  0.         -0.76772165
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1949e-05 - accuracy: 1.0000 - val_loss: 0.1118 - val_accuracy: 0.9828
[-0.        -0.        -0.        ...  0.        -0.7746529 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4181e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9832
[-0.        -0.        -0.        ...  0.        -0.7761626 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2879e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9833
[-0.        -0.        -0.        ...  0.        -0.7850816 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0176e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9834
[-0.        -0.        -0.        ...  0.        -0.7857657  0.       ]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0057e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9835
[-0.        -0.        -0.        ...  0.        -0.7858126  0.       ]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8259e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9835
[-0.        -0.        -0.        ...  0.        -0.7861163  0.       ]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7259e-06 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9826
[-0.         -0.         -0.         ... -0.         -0.78862035
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1734e-06 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9830
[-0.         -0.         -0.         ...  0.         -0.79434806
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 4s 15ms/step - loss: 5.9578e-06 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9832
[-0.         -0.         -0.         ...  0.         -0.79496825
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1332e-06 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9829
[-0.       -0.       -0.       ...  0.       -0.801339 -0.      ]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8694e-06 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9829
[-0.        -0.        -0.        ...  0.        -0.7974943 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1725e-05 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9826
[-0.        -0.        -0.        ...  0.        -0.8124483  0.       ]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1808e-05 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9821
[-0.         -0.         -0.         ... -0.         -0.81734043
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5202e-04 - accuracy: 0.9999 - val_loss: 0.1247 - val_accuracy: 0.9819
[-0.        -0.        -0.        ...  0.        -0.8370379 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3877e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9822
[-0.        -0.        -0.        ...  0.        -0.8426857 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8964e-04 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9816
[-0.        -0.        -0.        ...  0.        -0.8132085 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7583e-05 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9828
[-0.        -0.        -0.        ... -0.        -0.8088592  0.       ]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3995e-05 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9828
[-0.         -0.         -0.         ...  0.         -0.81171286
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 8.8134e-06 - accuracy: 1.0000 - val_loss: 0.1232 - val_accuracy: 0.9831
[-0.         -0.         -0.         ...  0.         -0.81200993
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3990e-05 - accuracy: 1.0000 - val_loss: 0.1235 - val_accuracy: 0.9827
[-0.         -0.         -0.         ...  0.         -0.81315994
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0048e-06 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9829
[-0.        -0.        -0.        ...  0.        -0.8032662  0.       ]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0236 - accuracy: 0.9932 - val_loss: 0.1159 - val_accuracy: 0.9791
[-0.         -0.         -0.         ...  0.         -0.80972826
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 0.1121 - val_accuracy: 0.9799
[-0.         -0.         -0.         ...  0.         -0.80957514
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9998 - val_loss: 0.1128 - val_accuracy: 0.9795
[-0.         -0.         -0.         ...  0.         -0.81041396
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 4s 15ms/step - loss: 9.4598e-04 - accuracy: 0.9999 - val_loss: 0.1126 - val_accuracy: 0.9798
[-0.         -0.         -0.         ... -0.         -0.80594814
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 15ms/step - loss: 7.9811e-04 - accuracy: 0.9999 - val_loss: 0.1133 - val_accuracy: 0.9800
[-0.         -0.         -0.         ... -0.         -0.81086886
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4480e-04 - accuracy: 0.9999 - val_loss: 0.1125 - val_accuracy: 0.9802
[-0.         -0.         -0.         ... -0.         -0.81333303
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2359e-04 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9802
[-0.         -0.         -0.         ...  0.         -0.81643355
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7733e-04 - accuracy: 0.9999 - val_loss: 0.1128 - val_accuracy: 0.9803
[-0.         -0.         -0.         ...  0.         -0.81359005
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6192e-04 - accuracy: 0.9999 - val_loss: 0.1137 - val_accuracy: 0.9807
[-0.        -0.        -0.        ...  0.        -0.8156675 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8211e-04 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9803
[-0.       -0.       -0.       ...  0.       -0.815073 -0.      ]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1896e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9808
[-0.       -0.       -0.       ... -0.       -0.827741 -0.      ]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 4s 15ms/step - loss: 2.8961e-04 - accuracy: 1.0000 - val_loss: 0.1128 - val_accuracy: 0.9808
[-0.        -0.        -0.        ...  0.        -0.8233135 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2188e-04 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9810
[-0.        -0.        -0.        ...  0.        -0.8265783 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9849e-04 - accuracy: 1.0000 - val_loss: 0.1139 - val_accuracy: 0.9806
[-0.         -0.         -0.         ... -0.         -0.83232355
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0699e-04 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9810
[-0.         -0.         -0.         ... -0.         -0.83675647
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2061e-04 - accuracy: 0.9999 - val_loss: 0.1158 - val_accuracy: 0.9810
[-0.        -0.        -0.        ... -0.        -0.8519762 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7052e-04 - accuracy: 0.9999 - val_loss: 0.1161 - val_accuracy: 0.9811
[-0.        -0.        -0.        ... -0.        -0.8522523 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8257e-04 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9812
[-0.        -0.        -0.        ...  0.        -0.8558983 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7170e-04 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9809
[-0.        -0.        -0.        ...  0.        -0.8509458 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2143e-04 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9814
[-0.        -0.        -0.        ...  0.        -0.8583202 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7302e-05 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9811
[-0.        -0.        -0.        ... -0.        -0.8568227  0.       ]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1187e-05 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9809
[-0.        -0.        -0.        ... -0.        -0.8577506 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6365e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9811
[-0.         -0.         -0.         ... -0.         -0.86152947
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 7.0476e-05 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9811
[-0.         -0.         -0.         ...  0.         -0.85924774
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 6.6880e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9817
[-0.        -0.        -0.        ... -0.        -0.8715754 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5983e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9817
[-0.        -0.        -0.        ...  0.        -0.8773704  0.       ]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0157e-04 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9820
[-0.        -0.        -0.        ...  0.        -0.8765876 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2829e-05 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9813
[-0.         -0.         -0.         ...  0.         -0.88136876
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0584e-05 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9815
[-0.        -0.        -0.        ... -0.        -0.8819839 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7264e-05 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9812
[-0.        -0.        -0.        ...  0.        -0.8932061 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1811e-05 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9823
[-0.        -0.        -0.        ... -0.        -0.8936125  0.       ]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7779e-05 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9820
[-0.        -0.        -0.        ...  0.        -0.8970623 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7727e-05 - accuracy: 1.0000 - val_loss: 0.1220 - val_accuracy: 0.9820
[-0.         -0.         -0.         ...  0.         -0.89811736
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0659e-05 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9819
[-0.        -0.        -0.        ...  0.        -0.8914719 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8258e-05 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9823
[-0.        -0.        -0.        ... -0.        -0.8914511 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2085e-05 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9818
[-0.         -0.         -0.         ... -0.         -0.89946485
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 4s 15ms/step - loss: 2.5564e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9816
[-0.        -0.        -0.        ... -0.        -0.8966538  0.       ]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 4s 16ms/step - loss: 2.3930e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9815
[-0.       -0.       -0.       ... -0.       -0.897169  0.      ]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0283e-05 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9822
[-0.        -0.        -0.        ... -0.        -0.9037009 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5179e-04 - accuracy: 0.9999 - val_loss: 0.1316 - val_accuracy: 0.9812
[-0.        -0.        -0.        ...  0.        -0.8919588  0.       ]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2473e-05 - accuracy: 1.0000 - val_loss: 0.1308 - val_accuracy: 0.9816
[-0.        -0.        -0.        ... -0.        -0.9211406  0.       ]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6800e-05 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9827
[-0.         -0.         -0.         ... -0.         -0.92473525
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5717e-04 - accuracy: 0.9999 - val_loss: 0.1301 - val_accuracy: 0.9815
[-0.         -0.         -0.         ...  0.         -0.88578576
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0455e-05 - accuracy: 1.0000 - val_loss: 0.1320 - val_accuracy: 0.9821
[-0.        -0.        -0.        ...  0.        -0.8870065 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4638e-05 - accuracy: 1.0000 - val_loss: 0.1337 - val_accuracy: 0.9823
[-0.         -0.         -0.         ... -0.         -0.88749945
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6074e-05 - accuracy: 1.0000 - val_loss: 0.1321 - val_accuracy: 0.9821
[-0.         -0.         -0.         ...  0.         -0.88724506
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0812e-05 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9823
[-0.        -0.        -0.        ...  0.        -0.8880428 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7899e-05 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9823
[-0.        -0.        -0.        ... -0.        -0.8888484 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5523e-05 - accuracy: 1.0000 - val_loss: 0.1329 - val_accuracy: 0.9817
[-0.        -0.        -0.        ...  0.        -0.8903026 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7097e-05 - accuracy: 1.0000 - val_loss: 0.1339 - val_accuracy: 0.9823
[-0.         -0.         -0.         ... -0.         -0.89018834
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0722 - accuracy: 0.9808 - val_loss: 0.1361 - val_accuracy: 0.9726 0s - loss: 0.0760 - ac
[-0. -0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0226 - accuracy: 0.9924 - val_loss: 0.1287 - val_accuracy: 0.9740
[-0. -0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0143 - accuracy: 0.9954 - val_loss: 0.1250 - val_accuracy: 0.9762
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0099 - accuracy: 0.9968 - val_loss: 0.1228 - val_accuracy: 0.9760
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9981 - val_loss: 0.1233 - val_accuracy: 0.9762
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0058 - accuracy: 0.9987 - val_loss: 0.1219 - val_accuracy: 0.9766
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0045 - accuracy: 0.9991 - val_loss: 0.1233 - val_accuracy: 0.9764
[-0. -0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.1236 - val_accuracy: 0.9768
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.1241 - val_accuracy: 0.9775
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9998 - val_loss: 0.1240 - val_accuracy: 0.9770
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9997 - val_loss: 0.1256 - val_accuracy: 0.9765
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 0.1265 - val_accuracy: 0.9769
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9998 - val_loss: 0.1271 - val_accuracy: 0.9770
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.1278 - val_accuracy: 0.9769
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1279 - val_accuracy: 0.9766
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1282 - val_accuracy: 0.9766
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1289 - val_accuracy: 0.9768
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1314 - val_accuracy: 0.9768
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3636e-04 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9769
[-0. -0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1944e-04 - accuracy: 0.9999 - val_loss: 0.1315 - val_accuracy: 0.9772
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5256e-04 - accuracy: 0.9999 - val_loss: 0.1340 - val_accuracy: 0.9766
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3477e-04 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9774
[-0. -0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4158e-04 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9770
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8427e-04 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9772
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4987e-04 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9773
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1665e-04 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9775
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4172e-04 - accuracy: 1.0000 - val_loss: 0.1395 - val_accuracy: 0.9771
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0189e-04 - accuracy: 1.0000 - val_loss: 0.1404 - val_accuracy: 0.9774
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4170e-04 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9776
[-0. -0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1854e-04 - accuracy: 1.0000 - val_loss: 0.1449 - val_accuracy: 0.9777
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1784e-04 - accuracy: 0.9999 - val_loss: 0.1474 - val_accuracy: 0.9768
[-0. -0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9013e-04 - accuracy: 1.0000 - val_loss: 0.1463 - val_accuracy: 0.9776
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7281e-04 - accuracy: 1.0000 - val_loss: 0.1500 - val_accuracy: 0.9764
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7038e-04 - accuracy: 1.0000 - val_loss: 0.1494 - val_accuracy: 0.9773
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1095e-04 - accuracy: 1.0000 - val_loss: 0.1491 - val_accuracy: 0.9770
[-0. -0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8936e-04 - accuracy: 1.0000 - val_loss: 0.1501 - val_accuracy: 0.9779
[-0. -0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7438e-04 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9762
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5712e-04 - accuracy: 1.0000 - val_loss: 0.1545 - val_accuracy: 0.9775
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8010e-04 - accuracy: 1.0000 - val_loss: 0.1557 - val_accuracy: 0.9773
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3616e-04 - accuracy: 1.0000 - val_loss: 0.1555 - val_accuracy: 0.9774
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7390e-04 - accuracy: 1.0000 - val_loss: 0.1565 - val_accuracy: 0.9780
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4767e-04 - accuracy: 1.0000 - val_loss: 0.1564 - val_accuracy: 0.9781
[-0. -0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1731e-04 - accuracy: 1.0000 - val_loss: 0.1586 - val_accuracy: 0.9776
[-0. -0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8444e-04 - accuracy: 1.0000 - val_loss: 0.1626 - val_accuracy: 0.9776
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4371e-04 - accuracy: 1.0000 - val_loss: 0.1626 - val_accuracy: 0.9775
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1538e-04 - accuracy: 1.0000 - val_loss: 0.1638 - val_accuracy: 0.9773
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 9.8988e-05 - accuracy: 1.0000 - val_loss: 0.1639 - val_accuracy: 0.9776
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2483e-04 - accuracy: 1.0000 - val_loss: 0.1658 - val_accuracy: 0.9775
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3103e-04 - accuracy: 1.0000 - val_loss: 0.1633 - val_accuracy: 0.9785
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1883e-05 - accuracy: 1.0000 - val_loss: 0.1668 - val_accuracy: 0.9770
[-0. -0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1710 - accuracy: 0.9593 - val_loss: 0.2129 - val_accuracy: 0.9583
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0814 - accuracy: 0.9768 - val_loss: 0.1857 - val_accuracy: 0.9624
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0626 - accuracy: 0.9809 - val_loss: 0.1703 - val_accuracy: 0.9640
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0527 - accuracy: 0.9831 - val_loss: 0.1621 - val_accuracy: 0.9652
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0470 - accuracy: 0.9845 - val_loss: 0.1549 - val_accuracy: 0.9664
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0418 - accuracy: 0.9861 - val_loss: 0.1507 - val_accuracy: 0.9670
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0377 - accuracy: 0.9878 - val_loss: 0.1478 - val_accuracy: 0.9670
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0348 - accuracy: 0.9886 - val_loss: 0.1458 - val_accuracy: 0.9671
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0323 - accuracy: 0.9894 - val_loss: 0.1441 - val_accuracy: 0.9674
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0304 - accuracy: 0.9902 - val_loss: 0.1428 - val_accuracy: 0.9672
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0286 - accuracy: 0.9907 - val_loss: 0.1413 - val_accuracy: 0.9680
[-0. -0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0263 - accuracy: 0.9915 - val_loss: 0.1403 - val_accuracy: 0.9679
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0253 - accuracy: 0.9916 - val_loss: 0.1398 - val_accuracy: 0.9694
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0235 - accuracy: 0.9924 - val_loss: 0.1396 - val_accuracy: 0.9690
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0216 - accuracy: 0.9930 - val_loss: 0.1410 - val_accuracy: 0.9692
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0203 - accuracy: 0.9935 - val_loss: 0.1406 - val_accuracy: 0.9693
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0191 - accuracy: 0.9941 - val_loss: 0.1402 - val_accuracy: 0.9695
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0182 - accuracy: 0.9945 - val_loss: 0.1427 - val_accuracy: 0.9689
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0172 - accuracy: 0.9949 - val_loss: 0.1426 - val_accuracy: 0.9696
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0164 - accuracy: 0.9950 - val_loss: 0.1434 - val_accuracy: 0.9689
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0159 - accuracy: 0.9953 - val_loss: 0.1441 - val_accuracy: 0.9688
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0144 - accuracy: 0.9958 - val_loss: 0.1455 - val_accuracy: 0.9687
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0142 - accuracy: 0.9958 - val_loss: 0.1461 - val_accuracy: 0.9695
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0134 - accuracy: 0.9963 - val_loss: 0.1464 - val_accuracy: 0.9696
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0130 - accuracy: 0.9963 - val_loss: 0.1476 - val_accuracy: 0.9696
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0122 - accuracy: 0.9966 - val_loss: 0.1483 - val_accuracy: 0.9693
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0117 - accuracy: 0.9968 - val_loss: 0.1503 - val_accuracy: 0.9690
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0108 - accuracy: 0.9971 - val_loss: 0.1521 - val_accuracy: 0.9690
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9972 - val_loss: 0.1537 - val_accuracy: 0.9695
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9973 - val_loss: 0.1550 - val_accuracy: 0.9693
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.1551 - val_accuracy: 0.9695
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0085 - accuracy: 0.9980 - val_loss: 0.1579 - val_accuracy: 0.9698
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9982 - val_loss: 0.1582 - val_accuracy: 0.9701
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0077 - accuracy: 0.9984 - val_loss: 0.1613 - val_accuracy: 0.9696
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0075 - accuracy: 0.9984 - val_loss: 0.1619 - val_accuracy: 0.9699
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0075 - accuracy: 0.9984 - val_loss: 0.1614 - val_accuracy: 0.9695
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9988 - val_loss: 0.1637 - val_accuracy: 0.9699
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0062 - accuracy: 0.9989 - val_loss: 0.1661 - val_accuracy: 0.9698
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0060 - accuracy: 0.9989 - val_loss: 0.1681 - val_accuracy: 0.9693
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0061 - accuracy: 0.9987 - val_loss: 0.1696 - val_accuracy: 0.9694
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0056 - accuracy: 0.9990 - val_loss: 0.1702 - val_accuracy: 0.9702
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0050 - accuracy: 0.9993 - val_loss: 0.1731 - val_accuracy: 0.9694
[-0. -0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0052 - accuracy: 0.9990 - val_loss: 0.1740 - val_accuracy: 0.9703
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 12ms/step - loss: 0.0049 - accuracy: 0.9993 - val_loss: 0.1768 - val_accuracy: 0.9698
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9992 - val_loss: 0.1783 - val_accuracy: 0.9700
[-0. -0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.1786 - val_accuracy: 0.9695
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0042 - accuracy: 0.9993 - val_loss: 0.1801 - val_accuracy: 0.9698
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.1801 - val_accuracy: 0.9699
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9995 - val_loss: 0.1824 - val_accuracy: 0.9701
[-0. -0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.1831 - val_accuracy: 0.9700
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5240 - accuracy: 0.8715 - val_loss: 0.4106 - val_accuracy: 0.8947
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.3049 - accuracy: 0.9134 - val_loss: 0.3294 - val_accuracy: 0.9167
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2606 - accuracy: 0.9238 - val_loss: 0.2947 - val_accuracy: 0.9232
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2365 - accuracy: 0.9298 - val_loss: 0.2738 - val_accuracy: 0.9271
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2221 - accuracy: 0.9330 - val_loss: 0.2613 - val_accuracy: 0.9303
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 12ms/step - loss: 0.2090 - accuracy: 0.9364 - val_loss: 0.2521 - val_accuracy: 0.9306
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2006 - accuracy: 0.9386 - val_loss: 0.2444 - val_accuracy: 0.9317
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1934 - accuracy: 0.9406 - val_loss: 0.2387 - val_accuracy: 0.9338
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1869 - accuracy: 0.9425 - val_loss: 0.2338 - val_accuracy: 0.9350
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1818 - accuracy: 0.9437 - val_loss: 0.2294 - val_accuracy: 0.9363
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1773 - accuracy: 0.9455 - val_loss: 0.2258 - val_accuracy: 0.9374
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1738 - accuracy: 0.9465 - val_loss: 0.2231 - val_accuracy: 0.9384
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1697 - accuracy: 0.9481 - val_loss: 0.2198 - val_accuracy: 0.9389
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1663 - accuracy: 0.9481 - val_loss: 0.2171 - val_accuracy: 0.9398
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1630 - accuracy: 0.9492 - val_loss: 0.2149 - val_accuracy: 0.9402
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1608 - accuracy: 0.9499 - val_loss: 0.2132 - val_accuracy: 0.9406
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1572 - accuracy: 0.9508 - val_loss: 0.2113 - val_accuracy: 0.9415
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1561 - accuracy: 0.9510 - val_loss: 0.2098 - val_accuracy: 0.9416
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1543 - accuracy: 0.9517 - val_loss: 0.2083 - val_accuracy: 0.9423
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1518 - accuracy: 0.9528 - val_loss: 0.2065 - val_accuracy: 0.9424
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1495 - accuracy: 0.9534 - val_loss: 0.2049 - val_accuracy: 0.9416
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1476 - accuracy: 0.9542 - val_loss: 0.2030 - val_accuracy: 0.9419
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1464 - accuracy: 0.9547 - val_loss: 0.2019 - val_accuracy: 0.9417
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1438 - accuracy: 0.9553 - val_loss: 0.2008 - val_accuracy: 0.9425
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9553 - val_loss: 0.1993 - val_accuracy: 0.9424
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9555 - val_loss: 0.1979 - val_accuracy: 0.9420
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1396 - accuracy: 0.9560 - val_loss: 0.1966 - val_accuracy: 0.9426
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9564 - val_loss: 0.1956 - val_accuracy: 0.9427
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9576 - val_loss: 0.1951 - val_accuracy: 0.9436
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9575 - val_loss: 0.1943 - val_accuracy: 0.9436
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9575 - val_loss: 0.1938 - val_accuracy: 0.9431
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9580 - val_loss: 0.1933 - val_accuracy: 0.9430
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9597 - val_loss: 0.1933 - val_accuracy: 0.9430
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9591 - val_loss: 0.1920 - val_accuracy: 0.9435
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9594 - val_loss: 0.1921 - val_accuracy: 0.9441
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9594 - val_loss: 0.1916 - val_accuracy: 0.9445
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9596 - val_loss: 0.1912 - val_accuracy: 0.9446
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1262 - accuracy: 0.9610 - val_loss: 0.1901 - val_accuracy: 0.9449
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9609 - val_loss: 0.1898 - val_accuracy: 0.9453
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9608 - val_loss: 0.1896 - val_accuracy: 0.9450
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.1889 - val_accuracy: 0.9454
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9616 - val_loss: 0.1884 - val_accuracy: 0.9449
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9619 - val_loss: 0.1878 - val_accuracy: 0.9458
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9619 - val_loss: 0.1875 - val_accuracy: 0.9457
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9618 - val_loss: 0.1873 - val_accuracy: 0.9463
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9624 - val_loss: 0.1868 - val_accuracy: 0.9465
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1197 - accuracy: 0.9622 - val_loss: 0.1867 - val_accuracy: 0.9467
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9624 - val_loss: 0.1859 - val_accuracy: 0.9469
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9630 - val_loss: 0.1862 - val_accuracy: 0.9468
[-0. -0. -0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9630 - val_loss: 0.1857 - val_accuracy: 0.9471
[-0. -0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1209 - accuracy: 0.6780 - val_loss: 0.9906 - val_accuracy: 0.6829
[-0. -0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9224 - accuracy: 0.6975 - val_loss: 0.9064 - val_accuracy: 0.6992
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8830 - accuracy: 0.7041 - val_loss: 0.8826 - val_accuracy: 0.7031
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8559 - accuracy: 0.7089 - val_loss: 0.8602 - val_accuracy: 0.7078
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8387 - accuracy: 0.7099 - val_loss: 0.8435 - val_accuracy: 0.7106
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8271 - accuracy: 0.7118 - val_loss: 0.8340 - val_accuracy: 0.7116
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8176 - accuracy: 0.7127 - val_loss: 0.8268 - val_accuracy: 0.7128
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8101 - accuracy: 0.7145 - val_loss: 0.8203 - val_accuracy: 0.7130
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8043 - accuracy: 0.7151 - val_loss: 0.8146 - val_accuracy: 0.7151
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7983 - accuracy: 0.7162 - val_loss: 0.8095 - val_accuracy: 0.7151
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7931 - accuracy: 0.7176 - val_loss: 0.8041 - val_accuracy: 0.7174
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7877 - accuracy: 0.7173 - val_loss: 0.7984 - val_accuracy: 0.7190
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7819 - accuracy: 0.7201 - val_loss: 0.7937 - val_accuracy: 0.7193
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7776 - accuracy: 0.7206 - val_loss: 0.7892 - val_accuracy: 0.7192
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7747 - accuracy: 0.7212 - val_loss: 0.7851 - val_accuracy: 0.7198
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7700 - accuracy: 0.7228 - val_loss: 0.7817 - val_accuracy: 0.7206
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 4s 15ms/step - loss: 0.7674 - accuracy: 0.7234 - val_loss: 0.7791 - val_accuracy: 0.7208
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7657 - accuracy: 0.7229 - val_loss: 0.7766 - val_accuracy: 0.7220
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7635 - accuracy: 0.7239 - val_loss: 0.7747 - val_accuracy: 0.7220
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7612 - accuracy: 0.7245 - val_loss: 0.7728 - val_accuracy: 0.7232
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7598 - accuracy: 0.7248 - val_loss: 0.7709 - val_accuracy: 0.7229
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7576 - accuracy: 0.7247 - val_loss: 0.7689 - val_accuracy: 0.7231
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7559 - accuracy: 0.7256 - val_loss: 0.7666 - val_accuracy: 0.7235
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7542 - accuracy: 0.7260 - val_loss: 0.7648 - val_accuracy: 0.7240
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7533 - accuracy: 0.7257 - val_loss: 0.7634 - val_accuracy: 0.7234
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7513 - accuracy: 0.7258 - val_loss: 0.7622 - val_accuracy: 0.7244
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7504 - accuracy: 0.7271 - val_loss: 0.7606 - val_accuracy: 0.7235
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 12ms/step - loss: 0.7492 - accuracy: 0.7261 - val_loss: 0.7594 - val_accuracy: 0.7237
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 3s 13ms/step - loss: 0.7485 - accuracy: 0.7258 - val_loss: 0.7589 - val_accuracy: 0.7245
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7468 - accuracy: 0.7267 - val_loss: 0.7579 - val_accuracy: 0.7250
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7461 - accuracy: 0.7270 - val_loss: 0.7571 - val_accuracy: 0.7248
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7454 - accuracy: 0.7265 - val_loss: 0.7565 - val_accuracy: 0.7244
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7449 - accuracy: 0.7268 - val_loss: 0.7556 - val_accuracy: 0.7255
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7437 - accuracy: 0.7268 - val_loss: 0.7548 - val_accuracy: 0.7250
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7437 - accuracy: 0.7272 - val_loss: 0.7541 - val_accuracy: 0.7257
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7428 - accuracy: 0.7278 - val_loss: 0.7534 - val_accuracy: 0.7257
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7425 - accuracy: 0.7269 - val_loss: 0.7530 - val_accuracy: 0.7258
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7419 - accuracy: 0.7275 - val_loss: 0.7525 - val_accuracy: 0.7263
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7415 - accuracy: 0.7276 - val_loss: 0.7516 - val_accuracy: 0.7262
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7404 - accuracy: 0.7277 - val_loss: 0.7514 - val_accuracy: 0.7276
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7407 - accuracy: 0.7281 - val_loss: 0.7504 - val_accuracy: 0.7270
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7401 - accuracy: 0.7281 - val_loss: 0.7503 - val_accuracy: 0.7271
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7394 - accuracy: 0.7279 - val_loss: 0.7503 - val_accuracy: 0.7269
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7395 - accuracy: 0.7276 - val_loss: 0.7498 - val_accuracy: 0.7266
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7391 - accuracy: 0.7286 - val_loss: 0.7493 - val_accuracy: 0.7274
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7394 - accuracy: 0.7282 - val_loss: 0.7493 - val_accuracy: 0.7275
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7386 - accuracy: 0.7282 - val_loss: 0.7491 - val_accuracy: 0.7277
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7383 - accuracy: 0.7279 - val_loss: 0.7486 - val_accuracy: 0.7279
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7383 - accuracy: 0.7283 - val_loss: 0.7489 - val_accuracy: 0.7280
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7372 - accuracy: 0.7283 - val_loss: 0.7481 - val_accuracy: 0.7279
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3443 - accuracy: 0.5385 - val_loss: 1.2891 - val_accuracy: 0.5471
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2449 - accuracy: 0.5578 - val_loss: 1.3165 - val_accuracy: 0.5183
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2270 - accuracy: 0.5602 - val_loss: 1.2269 - val_accuracy: 0.5563
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2182 - accuracy: 0.5625 - val_loss: 1.2220 - val_accuracy: 0.5582
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2173 - accuracy: 0.5634 - val_loss: 1.2234 - val_accuracy: 0.5595
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2137 - accuracy: 0.5656 - val_loss: 1.2132 - val_accuracy: 0.5657
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2116 - accuracy: 0.5647 - val_loss: 1.2134 - val_accuracy: 0.5610
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2093 - accuracy: 0.5680 - val_loss: 1.2129 - val_accuracy: 0.5697
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2071 - accuracy: 0.5697 - val_loss: 1.2138 - val_accuracy: 0.5665
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2054 - accuracy: 0.5696 - val_loss: 1.2083 - val_accuracy: 0.5711
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2033 - accuracy: 0.5705 - val_loss: 1.2078 - val_accuracy: 0.5708
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2030 - accuracy: 0.5706 - val_loss: 1.2089 - val_accuracy: 0.5649
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2025 - accuracy: 0.5706 - val_loss: 1.2084 - val_accuracy: 0.5644
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2018 - accuracy: 0.5702 - val_loss: 1.2027 - val_accuracy: 0.5632
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1992 - accuracy: 0.5719 - val_loss: 1.2023 - val_accuracy: 0.5697
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1977 - accuracy: 0.5719 - val_loss: 1.1996 - val_accuracy: 0.5704
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1919 - accuracy: 0.5748 - val_loss: 1.1910 - val_accuracy: 0.5740
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1896 - accuracy: 0.5744 - val_loss: 1.1866 - val_accuracy: 0.5737
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1873 - accuracy: 0.5754 - val_loss: 1.1873 - val_accuracy: 0.5747
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1872 - accuracy: 0.5752 - val_loss: 1.1874 - val_accuracy: 0.5743
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1848 - accuracy: 0.5766 - val_loss: 1.1888 - val_accuracy: 0.5710
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1879 - accuracy: 0.5744 - val_loss: 1.1842 - val_accuracy: 0.5689
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1847 - accuracy: 0.5758 - val_loss: 1.1857 - val_accuracy: 0.5747
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1857 - accuracy: 0.5756 - val_loss: 1.1830 - val_accuracy: 0.5732
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1835 - accuracy: 0.5763 - val_loss: 1.1837 - val_accuracy: 0.5747
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1836 - accuracy: 0.5763 - val_loss: 1.1833 - val_accuracy: 0.5748
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1836 - accuracy: 0.5765 - val_loss: 1.1824 - val_accuracy: 0.5739
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1832 - accuracy: 0.5777 - val_loss: 1.1828 - val_accuracy: 0.5754
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1821 - accuracy: 0.5770 - val_loss: 1.1826 - val_accuracy: 0.5760
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1811 - accuracy: 0.5772 - val_loss: 1.1828 - val_accuracy: 0.5767
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1799 - accuracy: 0.5788 - val_loss: 1.1826 - val_accuracy: 0.5782
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1795 - accuracy: 0.5786 - val_loss: 1.1835 - val_accuracy: 0.5744
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1806 - accuracy: 0.5782 - val_loss: 1.1809 - val_accuracy: 0.5790
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1793 - accuracy: 0.5783 - val_loss: 1.1797 - val_accuracy: 0.5791
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1790 - accuracy: 0.5788 - val_loss: 1.1781 - val_accuracy: 0.5779
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1782 - accuracy: 0.5791 - val_loss: 1.1774 - val_accuracy: 0.5770
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1773 - accuracy: 0.5786 - val_loss: 1.1783 - val_accuracy: 0.5778
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1774 - accuracy: 0.5791 - val_loss: 1.1782 - val_accuracy: 0.5783
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1763 - accuracy: 0.5789 - val_loss: 1.1782 - val_accuracy: 0.5784
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1774 - accuracy: 0.5794 - val_loss: 1.1780 - val_accuracy: 0.5788
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1766 - accuracy: 0.5802 - val_loss: 1.1758 - val_accuracy: 0.5787
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1763 - accuracy: 0.5810 - val_loss: 1.1770 - val_accuracy: 0.5787
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1746 - accuracy: 0.5804 - val_loss: 1.1800 - val_accuracy: 0.5796
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1761 - accuracy: 0.5802 - val_loss: 1.1792 - val_accuracy: 0.5797
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1763 - accuracy: 0.5800 - val_loss: 1.1767 - val_accuracy: 0.5799
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1752 - accuracy: 0.5798 - val_loss: 1.1739 - val_accuracy: 0.5788
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1752 - accuracy: 0.5797 - val_loss: 1.1756 - val_accuracy: 0.5797
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1745 - accuracy: 0.5811 - val_loss: 1.1772 - val_accuracy: 0.5809
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1744 - accuracy: 0.5804 - val_loss: 1.1778 - val_accuracy: 0.5810
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1742 - accuracy: 0.5806 - val_loss: 1.1754 - val_accuracy: 0.5809
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1754 - accuracy: 0.5799 - val_loss: 1.1742 - val_accuracy: 0.5790
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1748 - accuracy: 0.5797 - val_loss: 1.1758 - val_accuracy: 0.5807
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1740 - accuracy: 0.5810 - val_loss: 1.1755 - val_accuracy: 0.5807
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1743 - accuracy: 0.5810 - val_loss: 1.1750 - val_accuracy: 0.5811
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1739 - accuracy: 0.5807 - val_loss: 1.1748 - val_accuracy: 0.5812
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 12ms/step - loss: 1.1728 - accuracy: 0.5806 - val_loss: 1.1754 - val_accuracy: 0.5816
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1733 - accuracy: 0.5811 - val_loss: 1.1745 - val_accuracy: 0.5814
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1735 - accuracy: 0.5809 - val_loss: 1.1802 - val_accuracy: 0.5766
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1729 - accuracy: 0.5808 - val_loss: 1.1763 - val_accuracy: 0.5816
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1733 - accuracy: 0.5804 - val_loss: 1.1797 - val_accuracy: 0.5777
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1726 - accuracy: 0.5810 - val_loss: 1.1848 - val_accuracy: 0.5760
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1732 - accuracy: 0.5804 - val_loss: 1.1722 - val_accuracy: 0.5801
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1726 - accuracy: 0.5812 - val_loss: 1.1767 - val_accuracy: 0.5816
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1723 - accuracy: 0.5803 - val_loss: 1.1809 - val_accuracy: 0.5761
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1717 - accuracy: 0.5813 - val_loss: 1.1799 - val_accuracy: 0.5764
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1724 - accuracy: 0.5807 - val_loss: 1.1766 - val_accuracy: 0.5818
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1715 - accuracy: 0.5810 - val_loss: 1.1714 - val_accuracy: 0.5806
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1715 - accuracy: 0.5817 - val_loss: 1.1736 - val_accuracy: 0.5814
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1715 - accuracy: 0.5817 - val_loss: 1.1741 - val_accuracy: 0.5824
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1725 - accuracy: 0.5808 - val_loss: 1.1709 - val_accuracy: 0.5755
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1709 - accuracy: 0.5812 - val_loss: 1.1720 - val_accuracy: 0.5816
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1718 - accuracy: 0.5806 - val_loss: 1.1705 - val_accuracy: 0.5796
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1713 - accuracy: 0.5817 - val_loss: 1.1719 - val_accuracy: 0.5807
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1712 - accuracy: 0.5812 - val_loss: 1.1716 - val_accuracy: 0.5807
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1702 - accuracy: 0.5807 - val_loss: 1.1725 - val_accuracy: 0.5809
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1692 - accuracy: 0.5814 - val_loss: 1.1707 - val_accuracy: 0.5813
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1696 - accuracy: 0.5811 - val_loss: 1.1717 - val_accuracy: 0.5820
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1680 - accuracy: 0.5822 - val_loss: 1.1714 - val_accuracy: 0.5821
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1683 - accuracy: 0.5822 - val_loss: 1.1679 - val_accuracy: 0.5806
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1673 - accuracy: 0.5819 - val_loss: 1.1668 - val_accuracy: 0.5804
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1685 - accuracy: 0.5815 - val_loss: 1.1712 - val_accuracy: 0.5827
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1686 - accuracy: 0.5821 - val_loss: 1.1656 - val_accuracy: 0.5811
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1665 - accuracy: 0.5826 - val_loss: 1.1656 - val_accuracy: 0.5769
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1676 - accuracy: 0.5826 - val_loss: 1.1650 - val_accuracy: 0.5812
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1676 - accuracy: 0.5815 - val_loss: 1.1664 - val_accuracy: 0.5823
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1662 - accuracy: 0.5824 - val_loss: 1.1671 - val_accuracy: 0.5759
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1658 - accuracy: 0.5823 - val_loss: 1.1669 - val_accuracy: 0.5826
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1666 - accuracy: 0.5821 - val_loss: 1.1673 - val_accuracy: 0.5827
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1659 - accuracy: 0.5821 - val_loss: 1.1676 - val_accuracy: 0.5825
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1665 - accuracy: 0.5830 - val_loss: 1.1647 - val_accuracy: 0.5809
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1671 - accuracy: 0.5817 - val_loss: 1.1667 - val_accuracy: 0.5826
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1659 - accuracy: 0.5830 - val_loss: 1.1686 - val_accuracy: 0.5829
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1655 - accuracy: 0.5823 - val_loss: 1.1673 - val_accuracy: 0.5829
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1664 - accuracy: 0.5829 - val_loss: 1.1674 - val_accuracy: 0.5829
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1655 - accuracy: 0.5828 - val_loss: 1.1668 - val_accuracy: 0.5829
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1658 - accuracy: 0.5822 - val_loss: 1.1652 - val_accuracy: 0.5823
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1657 - accuracy: 0.5828 - val_loss: 1.1707 - val_accuracy: 0.5805
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1667 - accuracy: 0.5820 - val_loss: 1.1673 - val_accuracy: 0.5829
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1670 - accuracy: 0.5827 - val_loss: 1.1634 - val_accuracy: 0.5812
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1649 - accuracy: 0.5829 - val_loss: 1.1650 - val_accuracy: 0.5820
[-0. -0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 3s 9ms/step - loss: 0.8504 - accuracy: 0.9011 - val_loss: 0.8254 - val_accuracy: 0.9050
[ 0.          0.          0.         ... -0.          0.16811527
 -0.13677959]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8426 - accuracy: 0.9019 - val_loss: 0.8243 - val_accuracy: 0.9053
[ 0.          0.          0.         ... -0.          0.17128251
 -0.14557184]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8417 - accuracy: 0.9018 - val_loss: 0.8242 - val_accuracy: 0.9058
[ 0.          0.          0.         ... -0.          0.17324325
 -0.15144806]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8412 - accuracy: 0.9017 - val_loss: 0.8235 - val_accuracy: 0.9056
[ 0.          0.          0.         ...  0.          0.17502365
 -0.15516572]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8410 - accuracy: 0.9017 - val_loss: 0.8229 - val_accuracy: 0.9056
[ 0.          0.          0.         ...  0.          0.17621207
 -0.15789835]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8408 - accuracy: 0.9014 - val_loss: 0.8233 - val_accuracy: 0.9058
[ 0.          0.          0.         ...  0.          0.1772608
 -0.16021816]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.9013 - val_loss: 0.8228 - val_accuracy: 0.9055
[ 0.          0.          0.         ...  0.          0.17787425
 -0.16205059]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8407 - accuracy: 0.9012 - val_loss: 0.8231 - val_accuracy: 0.9053
[ 0.          0.          0.         ...  0.          0.17826185
 -0.16330725]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.9014 - val_loss: 0.8229 - val_accuracy: 0.9058
[ 0.          0.          0.         ...  0.          0.1783907
 -0.16434571]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.9011 - val_loss: 0.8232 - val_accuracy: 0.9057
[ 0.          0.          0.         ...  0.          0.17862828
 -0.16530217]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8404 - accuracy: 0.9010 - val_loss: 0.8225 - val_accuracy: 0.9061
[ 0.          0.          0.         ... -0.          0.17919378
 -0.16618681]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8403 - accuracy: 0.9013 - val_loss: 0.8227 - val_accuracy: 0.9057
[ 0.          0.          0.         ...  0.          0.1792585
 -0.16684856]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.9014 - val_loss: 0.8222 - val_accuracy: 0.9062
[ 0.          0.          0.         ... -0.          0.17921604
 -0.1675906 ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8403 - accuracy: 0.9011 - val_loss: 0.8226 - val_accuracy: 0.9056
[ 0.          0.          0.         ... -0.          0.17906949
 -0.1682019 ]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8404 - accuracy: 0.9012 - val_loss: 0.8226 - val_accuracy: 0.9056
[ 0.          0.          0.         ... -0.          0.17885174
 -0.16876608]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9013 - val_loss: 0.8224 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.          0.1788688
 -0.16930114]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9012 - val_loss: 0.8226 - val_accuracy: 0.9060
[ 0.          0.          0.         ...  0.          0.17855835
 -0.16947967]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9015 - val_loss: 0.8228 - val_accuracy: 0.9066
[ 0.          0.          0.         ...  0.          0.17795122
 -0.16989419]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8402 - accuracy: 0.9015 - val_loss: 0.8228 - val_accuracy: 0.9065
[ 0.          0.          0.         ...  0.          0.17753713
 -0.1700926 ]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8402 - accuracy: 0.9011 - val_loss: 0.8225 - val_accuracy: 0.9060
[ 0.          0.          0.         ...  0.          0.17768267
 -0.16992763]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9013 - val_loss: 0.8225 - val_accuracy: 0.9063
[ 0.          0.          0.         ...  0.          0.17728028
 -0.1700343 ]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9014 - val_loss: 0.8230 - val_accuracy: 0.9060
[ 0.          0.          0.         ...  0.          0.17684871
 -0.17018503]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8402 - accuracy: 0.9009 - val_loss: 0.8223 - val_accuracy: 0.9061
[ 0.          0.          0.         ... -0.          0.17635542
 -0.17043464]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9010 - val_loss: 0.8224 - val_accuracy: 0.9063
[ 0.          0.          0.         ...  0.          0.17584294
 -0.1702512 ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9012 - val_loss: 0.8225 - val_accuracy: 0.9067
[ 0.          0.          0.         ... -0.          0.17522226
 -0.1700652 ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9010 - val_loss: 0.8233 - val_accuracy: 0.9058
[ 0.          0.          0.         ...  0.          0.17510456
 -0.17014319]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9012 - val_loss: 0.8227 - val_accuracy: 0.9060
[ 0.          0.          0.         ... -0.          0.17467014
 -0.16989674]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9010 - val_loss: 0.8226 - val_accuracy: 0.9056
[ 0.          0.          0.         ...  0.          0.17442723
 -0.16998103]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9013 - val_loss: 0.8225 - val_accuracy: 0.9062
[ 0.          0.          0.         ...  0.          0.17421436
 -0.16975111]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9011 - val_loss: 0.8230 - val_accuracy: 0.9053
[ 0.          0.          0.         ...  0.          0.1738719
 -0.16971207]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9011 - val_loss: 0.8224 - val_accuracy: 0.9059
[ 0.          0.          0.         ...  0.          0.17350928
 -0.16968681]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9014 - val_loss: 0.8222 - val_accuracy: 0.9055
[ 0.          0.          0.         ...  0.          0.17327572
 -0.16972728]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9009 - val_loss: 0.8227 - val_accuracy: 0.9060
[ 0.          0.          0.         ...  0.          0.1732047
 -0.16942585]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9014 - val_loss: 0.8225 - val_accuracy: 0.9057
[ 0.          0.          0.         ...  0.          0.17304689
 -0.16922988]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9011 - val_loss: 0.8223 - val_accuracy: 0.9060
[ 0.          0.          0.         ...  0.          0.17273594
 -0.16896433]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9013 - val_loss: 0.8223 - val_accuracy: 0.9058
[ 0.          0.          0.         ...  0.          0.17272128
 -0.16926393]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8397 - accuracy: 0.9013 - val_loss: 0.8227 - val_accuracy: 0.9054
[ 0.          0.          0.         ...  0.          0.17238018
 -0.16912349]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9011 - val_loss: 0.8222 - val_accuracy: 0.9065
[ 0.          0.          0.         ...  0.          0.17212783
 -0.16881725]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9015 - val_loss: 0.8226 - val_accuracy: 0.9055
[ 0.          0.          0.         ...  0.          0.1721536
 -0.16896042]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9009 - val_loss: 0.8222 - val_accuracy: 0.9061
[ 0.          0.          0.         ...  0.          0.17186978
 -0.16895016]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9011 - val_loss: 0.8224 - val_accuracy: 0.9056
[ 0.          0.          0.         ...  0.          0.17177871
 -0.16888621]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9009 - val_loss: 0.8223 - val_accuracy: 0.9060
[ 0.          0.          0.         ...  0.          0.17175062
 -0.1686623 ]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9013 - val_loss: 0.8224 - val_accuracy: 0.9056
[ 0.          0.          0.         ...  0.          0.17163137
 -0.16859455]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9013 - val_loss: 0.8223 - val_accuracy: 0.9056
[ 0.          0.          0.         ...  0.          0.17155059
 -0.16872022]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9009 - val_loss: 0.8231 - val_accuracy: 0.9054
[ 0.          0.          0.         ...  0.          0.17116362
 -0.16858126]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8397 - accuracy: 0.9014 - val_loss: 0.8226 - val_accuracy: 0.9059
[ 0.          0.          0.         ...  0.          0.17106862
 -0.16847047]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9013 - val_loss: 0.8226 - val_accuracy: 0.9055
[ 0.          0.          0.         ...  0.          0.17087427
 -0.16864385]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9011 - val_loss: 0.8223 - val_accuracy: 0.9059
[ 0.          0.          0.         ...  0.          0.17073846
 -0.16894618]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9010 - val_loss: 0.8218 - val_accuracy: 0.9059
[ 0.          0.          0.         ...  0.          0.17078204
 -0.16859286]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9010 - val_loss: 0.8220 - val_accuracy: 0.9060
[ 0.          0.          0.         ...  0.          0.17055723
 -0.1686074 ]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8637 - accuracy: 0.9010 - val_loss: 0.8411 - val_accuracy: 0.9070
[ 0.          0.          0.         ...  0.          0.19908606
 -0.15758252]
Sparsity at: 0.6458221566523605
Epoch 52/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8587 - accuracy: 0.9018 - val_loss: 0.8400 - val_accuracy: 0.9071
[ 0.         0.         0.        ... -0.         0.198863  -0.1543502]
Sparsity at: 0.6458221566523605
Epoch 53/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8580 - accuracy: 0.9018 - val_loss: 0.8396 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.          0.19744878
 -0.1529593 ]
Sparsity at: 0.6458221566523605
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8579 - accuracy: 0.9020 - val_loss: 0.8395 - val_accuracy: 0.9077
[ 0.          0.          0.         ... -0.          0.19603457
 -0.15212259]
Sparsity at: 0.6458221566523605
Epoch 55/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9017 - val_loss: 0.8395 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.          0.19509275
 -0.15183629]
Sparsity at: 0.6458221566523605
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9017 - val_loss: 0.8392 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.19395871
 -0.15147427]
Sparsity at: 0.6458221566523605
Epoch 57/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9019 - val_loss: 0.8392 - val_accuracy: 0.9075
[ 0.          0.          0.         ...  0.          0.19318652
 -0.15153106]
Sparsity at: 0.6458221566523605
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9019 - val_loss: 0.8391 - val_accuracy: 0.9076
[ 0.         0.         0.        ... -0.         0.1923742 -0.1517531]
Sparsity at: 0.6458221566523605
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9017 - val_loss: 0.8391 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.          0.19181356
 -0.15214661]
Sparsity at: 0.6458221566523605
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8390 - val_accuracy: 0.9068
[ 0.          0.          0.         ...  0.          0.19142474
 -0.15268788]
Sparsity at: 0.6458221566523605
Epoch 61/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8390 - val_accuracy: 0.9074
[ 0.          0.          0.         ... -0.          0.19076611
 -0.15277241]
Sparsity at: 0.6458221566523605
Epoch 62/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9018 - val_loss: 0.8388 - val_accuracy: 0.9076
[ 0.          0.          0.         ... -0.          0.19026218
 -0.15305339]
Sparsity at: 0.6458221566523605
Epoch 63/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9019 - val_loss: 0.8389 - val_accuracy: 0.9078
[ 0.          0.          0.         ... -0.          0.19020143
 -0.15325448]
Sparsity at: 0.6458221566523605
Epoch 64/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9021 - val_loss: 0.8388 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.          0.18973781
 -0.15362369]
Sparsity at: 0.6458221566523605
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9019 - val_loss: 0.8387 - val_accuracy: 0.9076
[ 0.          0.          0.         ...  0.          0.18946482
 -0.15363835]
Sparsity at: 0.6458221566523605
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9021 - val_loss: 0.8390 - val_accuracy: 0.9068
[ 0.          0.          0.         ...  0.          0.18925597
 -0.15426892]
Sparsity at: 0.6458221566523605
Epoch 67/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9018 - val_loss: 0.8389 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.          0.18914074
 -0.15444979]
Sparsity at: 0.6458221566523605
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9019 - val_loss: 0.8390 - val_accuracy: 0.9067
[ 0.          0.          0.         ... -0.          0.18909943
 -0.15461834]
Sparsity at: 0.6458221566523605
Epoch 69/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9015 - val_loss: 0.8390 - val_accuracy: 0.9074
[ 0.          0.          0.         ... -0.          0.18912435
 -0.15472426]
Sparsity at: 0.6458221566523605
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8387 - val_accuracy: 0.9080
[ 0.          0.          0.         ... -0.          0.18866655
 -0.15482514]
Sparsity at: 0.6458221566523605
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9016 - val_loss: 0.8390 - val_accuracy: 0.9070
[ 0.          0.          0.         ...  0.          0.188647
 -0.15503229]
Sparsity at: 0.6458221566523605
Epoch 72/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9020 - val_loss: 0.8389 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.1883409
 -0.15536472]
Sparsity at: 0.6458221566523605
Epoch 73/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8389 - val_accuracy: 0.9075
[ 0.          0.          0.         ... -0.          0.18809536
 -0.15534422]
Sparsity at: 0.6458221566523605
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9020 - val_loss: 0.8387 - val_accuracy: 0.9074
[ 0.          0.          0.         ... -0.          0.18784447
 -0.15556799]
Sparsity at: 0.6458221566523605
Epoch 75/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8391 - val_accuracy: 0.9077
[ 0.          0.          0.         ... -0.          0.1879865
 -0.15584293]
Sparsity at: 0.6458221566523605
Epoch 76/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8575 - accuracy: 0.9022 - val_loss: 0.8391 - val_accuracy: 0.9074
[ 0.          0.          0.         ... -0.          0.18773167
 -0.15580164]
Sparsity at: 0.6458221566523605
Epoch 77/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9017 - val_loss: 0.8389 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.18749973
 -0.15580127]
Sparsity at: 0.6458221566523605
Epoch 78/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8388 - val_accuracy: 0.9076
[ 0.          0.          0.         ...  0.          0.1875836
 -0.15615831]
Sparsity at: 0.6458221566523605
Epoch 79/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9022 - val_loss: 0.8390 - val_accuracy: 0.9075
[ 0.          0.          0.         ... -0.          0.18765588
 -0.15625747]
Sparsity at: 0.6458221566523605
Epoch 80/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9018 - val_loss: 0.8391 - val_accuracy: 0.9067
[ 0.          0.          0.         ...  0.          0.18752357
 -0.1563788 ]
Sparsity at: 0.6458221566523605
Epoch 81/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9019 - val_loss: 0.8388 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.          0.18733993
 -0.15630093]
Sparsity at: 0.6458221566523605
Epoch 82/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9018 - val_loss: 0.8387 - val_accuracy: 0.9077
[ 0.          0.          0.         ...  0.          0.18747677
 -0.15648025]
Sparsity at: 0.6458221566523605
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9020 - val_loss: 0.8388 - val_accuracy: 0.9072
[ 0.          0.          0.         ...  0.          0.18736362
 -0.15648675]
Sparsity at: 0.6458221566523605
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9019 - val_loss: 0.8391 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.          0.18744686
 -0.15673442]
Sparsity at: 0.6458221566523605
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8386 - val_accuracy: 0.9075
[ 0.          0.          0.         ... -0.          0.1875569
 -0.15656821]
Sparsity at: 0.6458221566523605
Epoch 86/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9019 - val_loss: 0.8387 - val_accuracy: 0.9075
[ 0.          0.          0.         ...  0.          0.18746947
 -0.15665805]
Sparsity at: 0.6458221566523605
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9020 - val_loss: 0.8387 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.18707334
 -0.15669207]
Sparsity at: 0.6458221566523605
Epoch 88/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9019 - val_loss: 0.8388 - val_accuracy: 0.9075
[ 0.          0.          0.         ...  0.          0.18721037
 -0.15701029]
Sparsity at: 0.6458221566523605
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9021 - val_loss: 0.8388 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.18700533
 -0.15699638]
Sparsity at: 0.6458221566523605
Epoch 90/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8572 - accuracy: 0.9021 - val_loss: 0.8388 - val_accuracy: 0.9078
[ 0.          0.          0.         ... -0.          0.18705624
 -0.15692218]
Sparsity at: 0.6458221566523605
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8390 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.18720259
 -0.15708348]
Sparsity at: 0.6458221566523605
Epoch 92/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9018 - val_loss: 0.8390 - val_accuracy: 0.9076
[ 0.          0.          0.         ... -0.          0.1871036
 -0.15728116]
Sparsity at: 0.6458221566523605
Epoch 93/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8573 - accuracy: 0.9020 - val_loss: 0.8390 - val_accuracy: 0.9076
[ 0.          0.          0.         ... -0.          0.18702503
 -0.15715864]
Sparsity at: 0.6458221566523605
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8389 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.18698291
 -0.15728067]
Sparsity at: 0.6458221566523605
Epoch 95/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9019 - val_loss: 0.8385 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.18698455
 -0.15698901]
Sparsity at: 0.6458221566523605
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9016 - val_loss: 0.8390 - val_accuracy: 0.9075
[ 0.          0.          0.         ... -0.          0.18697833
 -0.15728378]
Sparsity at: 0.6458221566523605
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9019 - val_loss: 0.8389 - val_accuracy: 0.9076
[ 0.          0.          0.         ...  0.          0.18708514
 -0.15736823]
Sparsity at: 0.6458221566523605
Epoch 98/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9019 - val_loss: 0.8385 - val_accuracy: 0.9073
[ 0.         0.         0.        ... -0.         0.1871723 -0.1571671]
Sparsity at: 0.6458221566523605
Epoch 99/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9015 - val_loss: 0.8389 - val_accuracy: 0.9076
[ 0.          0.          0.         ...  0.          0.18711779
 -0.15732996]
Sparsity at: 0.6458221566523605
Epoch 100/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9014 - val_loss: 0.8388 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.18694152
 -0.15755166]
Sparsity at: 0.6458221566523605
Epoch 101/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9030 - accuracy: 0.8974 - val_loss: 0.8779 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.          0.17429511
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 102/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8934 - accuracy: 0.8997 - val_loss: 0.8761 - val_accuracy: 0.9068
[ 0.         0.         0.        ... -0.         0.1673576 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 103/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8922 - accuracy: 0.9000 - val_loss: 0.8749 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.          0.16486122
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 104/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8916 - accuracy: 0.9001 - val_loss: 0.8744 - val_accuracy: 0.9067
[ 0.          0.          0.         ... -0.          0.16406393
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8912 - accuracy: 0.9002 - val_loss: 0.8741 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.          0.16378316
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8909 - accuracy: 0.9003 - val_loss: 0.8737 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.16357249
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8907 - accuracy: 0.9003 - val_loss: 0.8731 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.          0.16338661
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 108/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8905 - accuracy: 0.9002 - val_loss: 0.8732 - val_accuracy: 0.9073
[ 0.         0.         0.        ... -0.         0.1634927 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 109/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8904 - accuracy: 0.9003 - val_loss: 0.8733 - val_accuracy: 0.9076
[ 0.          0.          0.         ... -0.          0.16317376
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8903 - accuracy: 0.9002 - val_loss: 0.8731 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.16306138
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8902 - accuracy: 0.9003 - val_loss: 0.8728 - val_accuracy: 0.9074
[ 0.         0.         0.        ... -0.         0.1627625 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8900 - accuracy: 0.9004 - val_loss: 0.8727 - val_accuracy: 0.9074
[ 0.          0.          0.         ... -0.          0.16274951
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 113/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8901 - accuracy: 0.9000 - val_loss: 0.8726 - val_accuracy: 0.9074
[ 0.          0.          0.         ... -0.          0.16239916
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9001 - val_loss: 0.8725 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.          0.16219334
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 115/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9004 - val_loss: 0.8723 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.16218741
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9002 - val_loss: 0.8724 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.16198026
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 117/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9001 - val_loss: 0.8726 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.16187611
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 118/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8898 - accuracy: 0.9003 - val_loss: 0.8723 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.16165973
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9003 - val_loss: 0.8725 - val_accuracy: 0.9076
[ 0.          0.          0.         ... -0.          0.16152291
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.8999 - val_loss: 0.8724 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.16130815
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 121/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9003 - val_loss: 0.8722 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.          0.16128692
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 122/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8898 - accuracy: 0.9000 - val_loss: 0.8724 - val_accuracy: 0.9068
[ 0.         0.         0.        ... -0.         0.1612902 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9001 - val_loss: 0.8722 - val_accuracy: 0.9067
[ 0.         0.         0.        ... -0.         0.1610719 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 124/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8721 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.16088878
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 125/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8722 - val_accuracy: 0.9071
[ 0.        0.        0.       ... -0.        0.160795 -0.      ]
Sparsity at: 0.7593381169527897
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8721 - val_accuracy: 0.9071
[ 0.          0.          0.         ... -0.          0.16080746
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 127/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8723 - val_accuracy: 0.9073
[ 0.         0.         0.        ... -0.         0.1607249 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8898 - accuracy: 0.9000 - val_loss: 0.8723 - val_accuracy: 0.9071
[ 0.         0.         0.        ... -0.         0.1606852 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 129/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.8999 - val_loss: 0.8722 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.16052708
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8721 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.16039129
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.8999 - val_loss: 0.8720 - val_accuracy: 0.9071
[ 0.         0.         0.        ... -0.         0.1604776 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8722 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.          0.16041921
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 133/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8721 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.16027421
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 134/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9002 - val_loss: 0.8722 - val_accuracy: 0.9074
[ 0.         0.         0.        ... -0.         0.1602242 -0.       ]
Sparsity at: 0.7593381169527897
Epoch 135/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8721 - val_accuracy: 0.9074
[ 0.          0.          0.         ... -0.          0.16034126
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9002 - val_loss: 0.8718 - val_accuracy: 0.9068
[ 0.          0.          0.         ... -0.          0.16016617
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 137/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8721 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.16017987
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 138/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.8999 - val_loss: 0.8722 - val_accuracy: 0.9076
[ 0.          0.          0.         ... -0.          0.16022566
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 139/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8720 - val_accuracy: 0.9075
[ 0.          0.          0.         ... -0.          0.16022818
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 140/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8722 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.16040853
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 141/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9002 - val_loss: 0.8720 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.16042857
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8721 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.16041853
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 143/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.8999 - val_loss: 0.8720 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.16039829
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 144/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8721 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.16032799
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8718 - val_accuracy: 0.9067
[ 0.          0.          0.         ... -0.          0.16039006
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 146/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8895 - accuracy: 0.9001 - val_loss: 0.8718 - val_accuracy: 0.9072
[ 0.          0.          0.         ... -0.          0.16030921
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 147/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.8998 - val_loss: 0.8718 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.          0.16018404
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 148/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9002 - val_loss: 0.8719 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.16011456
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8895 - accuracy: 0.9001 - val_loss: 0.8718 - val_accuracy: 0.9070
[ 0.          0.          0.         ... -0.          0.16032843
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 150/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8720 - val_accuracy: 0.9073
[ 0.          0.          0.         ... -0.          0.16025607
 -0.        ]
Sparsity at: 0.7593381169527897
Epoch 151/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9816 - accuracy: 0.8928 - val_loss: 0.9493 - val_accuracy: 0.9004
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 152/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9653 - accuracy: 0.8969 - val_loss: 0.9464 - val_accuracy: 0.9014
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 153/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9634 - accuracy: 0.8972 - val_loss: 0.9454 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 154/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9625 - accuracy: 0.8973 - val_loss: 0.9445 - val_accuracy: 0.9015
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 155/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9618 - accuracy: 0.8974 - val_loss: 0.9441 - val_accuracy: 0.9015
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 156/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.8975 - val_loss: 0.9435 - val_accuracy: 0.9008
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9609 - accuracy: 0.8975 - val_loss: 0.9432 - val_accuracy: 0.9014
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9606 - accuracy: 0.8978 - val_loss: 0.9430 - val_accuracy: 0.9015
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9604 - accuracy: 0.8978 - val_loss: 0.9429 - val_accuracy: 0.9013
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9601 - accuracy: 0.8981 - val_loss: 0.9428 - val_accuracy: 0.9016
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9600 - accuracy: 0.8979 - val_loss: 0.9425 - val_accuracy: 0.9019
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9599 - accuracy: 0.8979 - val_loss: 0.9425 - val_accuracy: 0.9016
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9597 - accuracy: 0.8979 - val_loss: 0.9423 - val_accuracy: 0.9022
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9596 - accuracy: 0.8980 - val_loss: 0.9422 - val_accuracy: 0.9017
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9595 - accuracy: 0.8979 - val_loss: 0.9422 - val_accuracy: 0.9024
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9595 - accuracy: 0.8979 - val_loss: 0.9421 - val_accuracy: 0.9018
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9594 - accuracy: 0.8981 - val_loss: 0.9421 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9594 - accuracy: 0.8979 - val_loss: 0.9421 - val_accuracy: 0.9012
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9594 - accuracy: 0.8980 - val_loss: 0.9421 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9594 - accuracy: 0.8980 - val_loss: 0.9420 - val_accuracy: 0.9017
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8980 - val_loss: 0.9419 - val_accuracy: 0.9019
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 172/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8981 - val_loss: 0.9420 - val_accuracy: 0.9015
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9418 - val_accuracy: 0.9017
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8979 - val_loss: 0.9421 - val_accuracy: 0.9018
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9018
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 176/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9418 - val_accuracy: 0.9017
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9014
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8980 - val_loss: 0.9419 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8978 - val_loss: 0.9419 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8980 - val_loss: 0.9419 - val_accuracy: 0.9015
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 181/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9021
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 182/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8981 - val_loss: 0.9419 - val_accuracy: 0.9018
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8981 - val_loss: 0.9419 - val_accuracy: 0.9021
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 184/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9418 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 185/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9017
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 186/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8981 - val_loss: 0.9420 - val_accuracy: 0.9019
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9417 - val_accuracy: 0.9021
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8980 - val_loss: 0.9418 - val_accuracy: 0.9018
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 189/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8980 - val_loss: 0.9418 - val_accuracy: 0.9017
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8978 - val_loss: 0.9416 - val_accuracy: 0.9019
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 191/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8981 - val_loss: 0.9417 - val_accuracy: 0.9019
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 192/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8981 - val_loss: 0.9418 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9417 - val_accuracy: 0.9022
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 194/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9017
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 195/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8980 - val_loss: 0.9417 - val_accuracy: 0.9018
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9417 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 197/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9589 - accuracy: 0.8978 - val_loss: 0.9418 - val_accuracy: 0.9019
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8978 - val_loss: 0.9417 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 199/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9417 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8980 - val_loss: 0.9417 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8447894313304721
Epoch 201/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1061 - accuracy: 0.8662 - val_loss: 1.0505 - val_accuracy: 0.8924
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 202/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0626 - accuracy: 0.8906 - val_loss: 1.0416 - val_accuracy: 0.8962
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 203/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0576 - accuracy: 0.8922 - val_loss: 1.0388 - val_accuracy: 0.8972
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0556 - accuracy: 0.8931 - val_loss: 1.0370 - val_accuracy: 0.8982
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0543 - accuracy: 0.8935 - val_loss: 1.0359 - val_accuracy: 0.8987
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0535 - accuracy: 0.8937 - val_loss: 1.0353 - val_accuracy: 0.8988
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 207/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0530 - accuracy: 0.8937 - val_loss: 1.0348 - val_accuracy: 0.8992
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0525 - accuracy: 0.8936 - val_loss: 1.0343 - val_accuracy: 0.8992
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0522 - accuracy: 0.8939 - val_loss: 1.0341 - val_accuracy: 0.8996
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0520 - accuracy: 0.8939 - val_loss: 1.0338 - val_accuracy: 0.8993
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 211/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0517 - accuracy: 0.8941 - val_loss: 1.0337 - val_accuracy: 0.8996
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 212/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0516 - accuracy: 0.8941 - val_loss: 1.0335 - val_accuracy: 0.8994
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 213/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0514 - accuracy: 0.8938 - val_loss: 1.0332 - val_accuracy: 0.8998
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 214/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0512 - accuracy: 0.8938 - val_loss: 1.0331 - val_accuracy: 0.8996
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 215/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0510 - accuracy: 0.8941 - val_loss: 1.0326 - val_accuracy: 0.8995
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 216/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0507 - accuracy: 0.8943 - val_loss: 1.0322 - val_accuracy: 0.8996
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0504 - accuracy: 0.8942 - val_loss: 1.0320 - val_accuracy: 0.9002
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0502 - accuracy: 0.8945 - val_loss: 1.0318 - val_accuracy: 0.9000
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0500 - accuracy: 0.8943 - val_loss: 1.0315 - val_accuracy: 0.9004
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0499 - accuracy: 0.8943 - val_loss: 1.0316 - val_accuracy: 0.8999
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0498 - accuracy: 0.8942 - val_loss: 1.0315 - val_accuracy: 0.8998
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 222/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0497 - accuracy: 0.8940 - val_loss: 1.0313 - val_accuracy: 0.8998
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8942 - val_loss: 1.0314 - val_accuracy: 0.8997
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8937 - val_loss: 1.0311 - val_accuracy: 0.9000
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 225/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8939 - val_loss: 1.0311 - val_accuracy: 0.8997
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 226/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8935 - val_loss: 1.0312 - val_accuracy: 0.8991
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 227/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8936 - val_loss: 1.0311 - val_accuracy: 0.8994
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0493 - accuracy: 0.8934 - val_loss: 1.0309 - val_accuracy: 0.8993
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 229/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0492 - accuracy: 0.8935 - val_loss: 1.0310 - val_accuracy: 0.8987
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 230/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0492 - accuracy: 0.8933 - val_loss: 1.0309 - val_accuracy: 0.8983
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 231/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0491 - accuracy: 0.8932 - val_loss: 1.0307 - val_accuracy: 0.8986
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0489 - accuracy: 0.8929 - val_loss: 1.0306 - val_accuracy: 0.8988
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 233/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0488 - accuracy: 0.8932 - val_loss: 1.0304 - val_accuracy: 0.8989
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0488 - accuracy: 0.8930 - val_loss: 1.0303 - val_accuracy: 0.8984
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0487 - accuracy: 0.8928 - val_loss: 1.0301 - val_accuracy: 0.8987
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 236/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0487 - accuracy: 0.8931 - val_loss: 1.0302 - val_accuracy: 0.8985
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 237/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0486 - accuracy: 0.8930 - val_loss: 1.0300 - val_accuracy: 0.8987
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 238/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0485 - accuracy: 0.8932 - val_loss: 1.0297 - val_accuracy: 0.8990
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 239/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0484 - accuracy: 0.8932 - val_loss: 1.0294 - val_accuracy: 0.8985
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0481 - accuracy: 0.8933 - val_loss: 1.0291 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0477 - accuracy: 0.8932 - val_loss: 1.0287 - val_accuracy: 0.8981
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 242/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0474 - accuracy: 0.8932 - val_loss: 1.0285 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 243/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0472 - accuracy: 0.8934 - val_loss: 1.0284 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0470 - accuracy: 0.8935 - val_loss: 1.0284 - val_accuracy: 0.8981
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 245/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0470 - accuracy: 0.8934 - val_loss: 1.0282 - val_accuracy: 0.8981
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 246/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0469 - accuracy: 0.8938 - val_loss: 1.0282 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0469 - accuracy: 0.8936 - val_loss: 1.0282 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 248/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0469 - accuracy: 0.8938 - val_loss: 1.0282 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 249/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0468 - accuracy: 0.8935 - val_loss: 1.0282 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0468 - accuracy: 0.8936 - val_loss: 1.0281 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 251/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1488 - accuracy: 0.8790 - val_loss: 1.1053 - val_accuracy: 0.8929
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1236 - accuracy: 0.8870 - val_loss: 1.1032 - val_accuracy: 0.8933
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 253/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1225 - accuracy: 0.8874 - val_loss: 1.1027 - val_accuracy: 0.8941
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 254/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1220 - accuracy: 0.8876 - val_loss: 1.1025 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1218 - accuracy: 0.8878 - val_loss: 1.1021 - val_accuracy: 0.8940
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 256/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1216 - accuracy: 0.8877 - val_loss: 1.1020 - val_accuracy: 0.8941
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 257/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1214 - accuracy: 0.8878 - val_loss: 1.1020 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 258/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1213 - accuracy: 0.8878 - val_loss: 1.1019 - val_accuracy: 0.8942
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1212 - accuracy: 0.8878 - val_loss: 1.1019 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1212 - accuracy: 0.8878 - val_loss: 1.1018 - val_accuracy: 0.8942
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 261/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8878 - val_loss: 1.1018 - val_accuracy: 0.8940
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 262/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8943
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8877 - val_loss: 1.1017 - val_accuracy: 0.8941
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 264/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8942
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 265/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8877 - val_loss: 1.1017 - val_accuracy: 0.8941
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 267/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 268/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1210 - accuracy: 0.8879 - val_loss: 1.1017 - val_accuracy: 0.8937
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8877 - val_loss: 1.1017 - val_accuracy: 0.8937
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8877 - val_loss: 1.1017 - val_accuracy: 0.8942
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 273/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8940
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 274/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8941
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 275/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 276/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8876 - val_loss: 1.1016 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9468884120171673
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8940
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8880 - val_loss: 1.1016 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8941
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 282/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8940
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 284/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8880 - val_loss: 1.1017 - val_accuracy: 0.8937
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 286/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8940
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 287/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 288/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1015 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8942
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 290/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 291/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 292/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8940
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8941
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 294/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1015 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 295/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8939
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8880 - val_loss: 1.1016 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 297/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8880 - val_loss: 1.1016 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 298/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 299/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8938
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9468884120171673
Epoch 300/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8940
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9468884120171673
Epoch 301/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3442 - accuracy: 0.8374 - val_loss: 1.2899 - val_accuracy: 0.8602
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 302/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2947 - accuracy: 0.8574 - val_loss: 1.2787 - val_accuracy: 0.8628
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 303/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2883 - accuracy: 0.8578 - val_loss: 1.2734 - val_accuracy: 0.8631
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 304/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2842 - accuracy: 0.8575 - val_loss: 1.2690 - val_accuracy: 0.8617
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 305/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2810 - accuracy: 0.8578 - val_loss: 1.2664 - val_accuracy: 0.8625
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 306/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2792 - accuracy: 0.8582 - val_loss: 1.2651 - val_accuracy: 0.8619
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 307/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2783 - accuracy: 0.8580 - val_loss: 1.2645 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2779 - accuracy: 0.8579 - val_loss: 1.2642 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 309/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2776 - accuracy: 0.8580 - val_loss: 1.2640 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 310/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2774 - accuracy: 0.8578 - val_loss: 1.2639 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 311/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2773 - accuracy: 0.8577 - val_loss: 1.2638 - val_accuracy: 0.8626
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 312/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2772 - accuracy: 0.8578 - val_loss: 1.2638 - val_accuracy: 0.8625
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 313/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2771 - accuracy: 0.8576 - val_loss: 1.2637 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 314/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2771 - accuracy: 0.8575 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 315/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2770 - accuracy: 0.8575 - val_loss: 1.2637 - val_accuracy: 0.8618
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 316/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2770 - accuracy: 0.8575 - val_loss: 1.2637 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 317/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2770 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 318/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2770 - accuracy: 0.8575 - val_loss: 1.2636 - val_accuracy: 0.8620
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 319/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 320/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8573 - val_loss: 1.2636 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 321/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 322/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 323/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8570 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 324/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 325/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 326/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 327/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8573 - val_loss: 1.2636 - val_accuracy: 0.8620
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8625
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8625
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 330/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8573 - val_loss: 1.2636 - val_accuracy: 0.8621
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 332/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8621
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8621
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 334/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 336/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2635 - val_accuracy: 0.8620
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8619
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 341/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 342/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 343/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 344/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8573 - val_loss: 1.2636 - val_accuracy: 0.8624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 345/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8621
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 347/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8620
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716671137339056
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8570 - val_loss: 1.2636 - val_accuracy: 0.8623
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8620
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716671137339056
Epoch 351/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6439 - accuracy: 0.6389 - val_loss: 1.5786 - val_accuracy: 0.6641
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9845761802575107
Epoch 352/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5816 - accuracy: 0.6601 - val_loss: 1.5614 - val_accuracy: 0.6657
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9845761802575107
Epoch 353/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5727 - accuracy: 0.6612 - val_loss: 1.5563 - val_accuracy: 0.6646
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 354/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5698 - accuracy: 0.6605 - val_loss: 1.5542 - val_accuracy: 0.6637
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 355/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5686 - accuracy: 0.6596 - val_loss: 1.5532 - val_accuracy: 0.6616
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 356/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5680 - accuracy: 0.6560 - val_loss: 1.5527 - val_accuracy: 0.6610
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 357/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5677 - accuracy: 0.6559 - val_loss: 1.5523 - val_accuracy: 0.6608
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5675 - accuracy: 0.6562 - val_loss: 1.5521 - val_accuracy: 0.6609
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5674 - accuracy: 0.6559 - val_loss: 1.5519 - val_accuracy: 0.6611
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 360/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5673 - accuracy: 0.6558 - val_loss: 1.5518 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5672 - accuracy: 0.6556 - val_loss: 1.5517 - val_accuracy: 0.6611
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 362/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5672 - accuracy: 0.6557 - val_loss: 1.5516 - val_accuracy: 0.6615
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5671 - accuracy: 0.6558 - val_loss: 1.5515 - val_accuracy: 0.6611
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5671 - accuracy: 0.6557 - val_loss: 1.5515 - val_accuracy: 0.6612
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 365/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5671 - accuracy: 0.6557 - val_loss: 1.5515 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.6558 - val_loss: 1.5514 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.6556 - val_loss: 1.5514 - val_accuracy: 0.6612
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.6558 - val_loss: 1.5514 - val_accuracy: 0.6615
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6616
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6559 - val_loss: 1.5513 - val_accuracy: 0.6617
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 372/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6560 - val_loss: 1.5513 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 373/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 374/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6559 - val_loss: 1.5513 - val_accuracy: 0.6612
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6559 - val_loss: 1.5513 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 376/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6612
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 377/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 378/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6559 - val_loss: 1.5513 - val_accuracy: 0.6612
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6560 - val_loss: 1.5512 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6611
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 381/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6560 - val_loss: 1.5512 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 382/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6556 - val_loss: 1.5513 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6615
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 385/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 387/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6612
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 388/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6612
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6556 - val_loss: 1.5512 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 391/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6556 - val_loss: 1.5512 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 392/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 395/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9845761802575107
Epoch 396/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6612
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6616
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6556 - val_loss: 1.5512 - val_accuracy: 0.6613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6556 - val_loss: 1.5512 - val_accuracy: 0.6614
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9845761802575107
Epoch 401/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7968 - accuracy: 0.5550 - val_loss: 1.7333 - val_accuracy: 0.5746
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 402/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7445 - accuracy: 0.5697 - val_loss: 1.7187 - val_accuracy: 0.5784
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 403/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7377 - accuracy: 0.5708 - val_loss: 1.7153 - val_accuracy: 0.5788
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 404/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7359 - accuracy: 0.5706 - val_loss: 1.7141 - val_accuracy: 0.5783
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7353 - accuracy: 0.5706 - val_loss: 1.7134 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 406/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7349 - accuracy: 0.5707 - val_loss: 1.7129 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 407/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7346 - accuracy: 0.5709 - val_loss: 1.7127 - val_accuracy: 0.5781
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 408/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7345 - accuracy: 0.5711 - val_loss: 1.7125 - val_accuracy: 0.5784
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7344 - accuracy: 0.5712 - val_loss: 1.7123 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 410/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7343 - accuracy: 0.5714 - val_loss: 1.7121 - val_accuracy: 0.5775
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 411/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7342 - accuracy: 0.5714 - val_loss: 1.7120 - val_accuracy: 0.5774
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7341 - accuracy: 0.5714 - val_loss: 1.7120 - val_accuracy: 0.5782
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 413/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7341 - accuracy: 0.5716 - val_loss: 1.7119 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7340 - accuracy: 0.5717 - val_loss: 1.7119 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7340 - accuracy: 0.5717 - val_loss: 1.7118 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 416/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7340 - accuracy: 0.5718 - val_loss: 1.7118 - val_accuracy: 0.5782
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 417/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7117 - val_accuracy: 0.5783
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 418/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5718 - val_loss: 1.7117 - val_accuracy: 0.5783
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 419/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5718 - val_loss: 1.7117 - val_accuracy: 0.5782
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5717 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 423/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 426/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 427/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 428/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 429/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 430/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 431/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5717 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 433/500
235/235 [==============================] - 2s 10ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 435/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 436/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5782
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 437/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 439/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 440/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 442/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5777
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 443/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5717 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 447/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 448/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5721 - val_loss: 1.7115 - val_accuracy: 0.5780
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 449/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5782
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 451/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7115 - val_accuracy: 0.5779
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5782
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 455/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 456/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 457/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 458/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 459/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 461/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 462/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 463/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 464/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5721 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 465/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5782
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 466/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 467/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 468/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 469/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 470/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5777
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 471/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 473/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 474/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 475/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 476/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 477/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5721 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 478/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 479/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 480/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5783
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 482/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5783
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 483/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 484/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 485/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 486/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 487/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 488/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 489/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 490/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5721 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 491/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 492/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 493/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 495/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7115 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 499/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 500/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5781
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 1/500
235/235 [==============================] - 4s 9ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.2682 - val_accuracy: 0.9702
[-0.        -0.        -0.        ...  0.         0.588045  -1.0087581]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 8.4622e-04 - accuracy: 0.9997 - val_loss: 0.2677 - val_accuracy: 0.9710
[-0.         -0.         -0.         ... -0.          0.58606076
 -1.0111476 ]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.2603 - val_accuracy: 0.9721
[-0.        -0.        -0.        ... -0.         0.5863574 -1.004833 ]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7883e-04 - accuracy: 0.9999 - val_loss: 0.2586 - val_accuracy: 0.9724
[-0.         -0.         -0.         ...  0.          0.58543134
 -1.0041473 ]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 2s 9ms/step - loss: 7.9474e-05 - accuracy: 1.0000 - val_loss: 0.2578 - val_accuracy: 0.9728
[-0.         -0.         -0.         ... -0.          0.58749086
 -1.0070748 ]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8397e-05 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9731
[-0.        -0.        -0.        ... -0.         0.5875895 -1.0069286]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3631e-05 - accuracy: 1.0000 - val_loss: 0.2565 - val_accuracy: 0.9729
[-0.        -0.        -0.        ... -0.         0.5876826 -1.0070059]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1671e-05 - accuracy: 1.0000 - val_loss: 0.2565 - val_accuracy: 0.9731
[-0.         -0.         -0.         ... -0.          0.58773714
 -1.0070993 ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0310e-05 - accuracy: 1.0000 - val_loss: 0.2566 - val_accuracy: 0.9729
[-0.         -0.         -0.         ... -0.          0.58779305
 -1.0072013 ]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 9.2633e-06 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9729
[-0.        -0.        -0.        ... -0.         0.5878583 -1.0073192]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 2s 9ms/step - loss: 8.4163e-06 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9730
[-0.        -0.        -0.        ... -0.         0.5879386 -1.0074524]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7079e-06 - accuracy: 1.0000 - val_loss: 0.2568 - val_accuracy: 0.9728
[-0.         -0.         -0.         ...  0.          0.58802783
 -1.0076001 ]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 2s 9ms/step - loss: 7.0999e-06 - accuracy: 1.0000 - val_loss: 0.2569 - val_accuracy: 0.9728
[-0.         -0.         -0.         ...  0.          0.58813417
 -1.0077622 ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5709e-06 - accuracy: 1.0000 - val_loss: 0.2570 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.         0.5882569 -1.0079422]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 2s 9ms/step - loss: 6.0996e-06 - accuracy: 1.0000 - val_loss: 0.2571 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.         0.5883937 -1.0081402]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 5.6795e-06 - accuracy: 1.0000 - val_loss: 0.2572 - val_accuracy: 0.9728
[-0.         -0.         -0.         ... -0.          0.58855295
 -1.0083567 ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 5.2962e-06 - accuracy: 1.0000 - val_loss: 0.2573 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.         0.5887338 -1.0085902]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 4.9466e-06 - accuracy: 1.0000 - val_loss: 0.2574 - val_accuracy: 0.9728
[-0.         -0.         -0.         ...  0.          0.58893025
 -1.0088445 ]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6260e-06 - accuracy: 1.0000 - val_loss: 0.2575 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.         0.5891489 -1.0091203]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3279e-06 - accuracy: 1.0000 - val_loss: 0.2576 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.         0.5893861 -1.0094138]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 2s 9ms/step - loss: 4.0523e-06 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9731
[-0.         -0.         -0.         ...  0.          0.58964604
 -1.0097291 ]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7957e-06 - accuracy: 1.0000 - val_loss: 0.2578 - val_accuracy: 0.9730
[-0.         -0.         -0.         ...  0.          0.58992475
 -1.0100688 ]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5538e-06 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9731
[-0.         -0.         -0.         ...  0.          0.59022635
 -1.0104278 ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3285e-06 - accuracy: 1.0000 - val_loss: 0.2581 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.5905556 -1.0108074]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1177e-06 - accuracy: 1.0000 - val_loss: 0.2583 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.         0.5909022 -1.0112115]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9191e-06 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.         0.5912768 -1.0116395]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7320e-06 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9732
[-0.         -0.         -0.         ...  0.          0.59167427
 -1.0120896 ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5565e-06 - accuracy: 1.0000 - val_loss: 0.2588 - val_accuracy: 0.9733
[-0.         -0.         -0.         ...  0.          0.59210324
 -1.0125631 ]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3911e-06 - accuracy: 1.0000 - val_loss: 0.2590 - val_accuracy: 0.9731
[-0.         -0.         -0.         ...  0.          0.59255624
 -1.0130609 ]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2350e-06 - accuracy: 1.0000 - val_loss: 0.2592 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.5930469 -1.01359  ]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0887e-06 - accuracy: 1.0000 - val_loss: 0.2594 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.5935675 -1.0141453]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9506e-06 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.5941217 -1.014727 ]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8209e-06 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9733
[-0.        -0.        -0.        ...  0.         0.5947151 -1.0153388]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6986e-06 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9733
[-0.        -0.        -0.        ...  0.         0.5953617 -1.0159765]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5839e-06 - accuracy: 1.0000 - val_loss: 0.2605 - val_accuracy: 0.9732
[-0.         -0.         -0.         ...  0.          0.59604454
 -1.0166512 ]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4758e-06 - accuracy: 1.0000 - val_loss: 0.2608 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.         0.5967779 -1.0173626]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3743e-06 - accuracy: 1.0000 - val_loss: 0.2612 - val_accuracy: 0.9732
[-0.         -0.         -0.         ...  0.          0.59756666
 -1.0181156 ]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2791e-06 - accuracy: 1.0000 - val_loss: 0.2616 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.5984132 -1.0189025]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1894e-06 - accuracy: 1.0000 - val_loss: 0.2620 - val_accuracy: 0.9731
[-0.         -0.         -0.         ...  0.          0.59931725
 -1.0197209 ]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1054e-06 - accuracy: 1.0000 - val_loss: 0.2624 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.         0.60028   -1.0206004]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0269e-06 - accuracy: 1.0000 - val_loss: 0.2628 - val_accuracy: 0.9733
[-0.        -0.        -0.        ...  0.         0.6013041 -1.0215133]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5300e-07 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.         0.6023893 -1.0224849]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 8.8409e-07 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.         0.6035547 -1.0234933]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 8.1921e-07 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9732
[-0.         -0.         -0.         ...  0.          0.60478413
 -1.0245447 ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5908e-07 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.6060863 -1.0256447]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 7.0278e-07 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.6074693 -1.0267905]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5020e-07 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9731
[-0.         -0.         -0.         ...  0.          0.60895497
 -1.0279917 ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 6.0088e-07 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.6104907 -1.0292248]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 5.5500e-07 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9731
[-0.         -0.         -0.         ...  0.          0.61209035
 -1.0305238 ]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 5.1238e-07 - accuracy: 1.0000 - val_loss: 0.2679 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.         0.6137657 -1.0318636]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0218 - accuracy: 0.9934 - val_loss: 0.2384 - val_accuracy: 0.9693
[-0.        -0.        -0.        ...  0.        -0.        -1.0177894]
Sparsity at: 0.6458724517167382
Epoch 52/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.2237 - val_accuracy: 0.9712
[-0.        -0.        -0.        ...  0.        -0.        -1.0254325]
Sparsity at: 0.6458724517167382
Epoch 53/500
235/235 [==============================] - 2s 9ms/step - loss: 6.3563e-04 - accuracy: 0.9999 - val_loss: 0.2196 - val_accuracy: 0.9714
[-0.        -0.        -0.        ...  0.        -0.        -1.0293853]
Sparsity at: 0.6458724517167382
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 3.1608e-04 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9714
[-0.        -0.        -0.        ...  0.        -0.        -1.0345147]
Sparsity at: 0.6458724517167382
Epoch 55/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4074e-04 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9718
[-0.        -0.        -0.        ...  0.        -0.        -1.0372287]
Sparsity at: 0.6458724517167382
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0348e-04 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9720
[-0.        -0.        -0.        ...  0.        -0.        -1.0399623]
Sparsity at: 0.6458724517167382
Epoch 57/500
235/235 [==============================] - 2s 10ms/step - loss: 1.7805e-04 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9722
[-0.        -0.        -0.        ...  0.        -0.        -1.0427305]
Sparsity at: 0.6458724517167382
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5843e-04 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9721
[-0.       -0.       -0.       ...  0.       -0.       -1.045603]
Sparsity at: 0.6458724517167382
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4256e-04 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9722
[-0.        -0.        -0.        ...  0.        -0.        -1.0485637]
Sparsity at: 0.6458724517167382
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2919e-04 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9725
[-0.        -0.        -0.        ...  0.        -0.        -1.0516682]
Sparsity at: 0.6458724517167382
Epoch 61/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1759e-04 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9725
[-0.        -0.        -0.        ...  0.        -0.        -1.0549709]
Sparsity at: 0.6458724517167382
Epoch 62/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0744e-04 - accuracy: 1.0000 - val_loss: 0.2183 - val_accuracy: 0.9724
[-0.        -0.        -0.        ...  0.        -0.        -1.0584064]
Sparsity at: 0.6458724517167382
Epoch 63/500
235/235 [==============================] - 2s 9ms/step - loss: 9.8410e-05 - accuracy: 1.0000 - val_loss: 0.2185 - val_accuracy: 0.9726
[-0.        -0.        -0.        ...  0.        -0.        -1.0620241]
Sparsity at: 0.6458724517167382
Epoch 64/500
235/235 [==============================] - 2s 9ms/step - loss: 9.0344e-05 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9727
[-0.        -0.        -0.        ...  0.        -0.        -1.0658306]
Sparsity at: 0.6458724517167382
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 8.3001e-05 - accuracy: 1.0000 - val_loss: 0.2190 - val_accuracy: 0.9724
[-0.        -0.        -0.        ...  0.        -0.        -1.0698181]
Sparsity at: 0.6458724517167382
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 7.6394e-05 - accuracy: 1.0000 - val_loss: 0.2193 - val_accuracy: 0.9727
[-0.       -0.       -0.       ...  0.       -0.       -1.073993]
Sparsity at: 0.6458724517167382
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0280e-05 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.        -0.        -1.0783528]
Sparsity at: 0.6458724517167382
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 6.4717e-05 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9730
[-0.        -0.        -0.        ...  0.        -0.        -1.0828927]
Sparsity at: 0.6458724517167382
Epoch 69/500
235/235 [==============================] - 2s 9ms/step - loss: 5.9601e-05 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9730
[-0.        -0.        -0.        ...  0.        -0.        -1.0876178]
Sparsity at: 0.6458724517167382
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 5.4882e-05 - accuracy: 1.0000 - val_loss: 0.2208 - val_accuracy: 0.9729
[-0.        -0.        -0.        ...  0.        -0.        -1.0925248]
Sparsity at: 0.6458724517167382
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0519e-05 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.        -0.        -1.0975989]
Sparsity at: 0.6458724517167382
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6483e-05 - accuracy: 1.0000 - val_loss: 0.2217 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.        -0.        -1.1028473]
Sparsity at: 0.6458724517167382
Epoch 73/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2740e-05 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9727
[-0.        -0.        -0.        ...  0.        -0.        -1.1082634]
Sparsity at: 0.6458724517167382
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 3.9286e-05 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.        -0.        -1.1138316]
Sparsity at: 0.6458724517167382
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6089e-05 - accuracy: 1.0000 - val_loss: 0.2234 - val_accuracy: 0.9729
[-0.        -0.        -0.        ...  0.        -0.        -1.1195369]
Sparsity at: 0.6458724517167382
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3102e-05 - accuracy: 1.0000 - val_loss: 0.2240 - val_accuracy: 0.9728
[-0.        -0.        -0.        ...  0.        -0.        -1.1253556]
Sparsity at: 0.6458724517167382
Epoch 77/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0370e-05 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9726
[-0.        -0.        -0.        ...  0.        -0.        -1.1313614]
Sparsity at: 0.6458724517167382
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7813e-05 - accuracy: 1.0000 - val_loss: 0.2254 - val_accuracy: 0.9726
[-0.        -0.        -0.        ...  0.        -0.        -1.1376103]
Sparsity at: 0.6458724517167382
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5477e-05 - accuracy: 1.0000 - val_loss: 0.2262 - val_accuracy: 0.9726
[-0.        -0.        -0.        ...  0.        -0.        -1.1439307]
Sparsity at: 0.6458724517167382
Epoch 80/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3296e-05 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9725
[-0.        -0.        -0.        ...  0.        -0.        -1.1504414]
Sparsity at: 0.6458724517167382
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1289e-05 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9724
[-0.        -0.        -0.        ...  0.        -0.        -1.1570404]
Sparsity at: 0.6458724517167382
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9422e-05 - accuracy: 1.0000 - val_loss: 0.2288 - val_accuracy: 0.9724
[-0.        -0.        -0.        ...  0.        -0.        -1.1638609]
Sparsity at: 0.6458724517167382
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7727e-05 - accuracy: 1.0000 - val_loss: 0.2297 - val_accuracy: 0.9725
[-0.        -0.        -0.        ...  0.        -0.        -1.1708266]
Sparsity at: 0.6458724517167382
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6154e-05 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9726
[-0.        -0.        -0.        ...  0.        -0.        -1.1779188]
Sparsity at: 0.6458724517167382
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4705e-05 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9726
[-0.        -0.        -0.        ...  0.        -0.        -1.1851437]
Sparsity at: 0.6458724517167382
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3383e-05 - accuracy: 1.0000 - val_loss: 0.2329 - val_accuracy: 0.9726
[-0.        -0.        -0.        ...  0.        -0.        -1.1925136]
Sparsity at: 0.6458724517167382
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2163e-05 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9727
[-0.        -0.        -0.        ...  0.        -0.        -1.2000003]
Sparsity at: 0.6458724517167382
Epoch 88/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1048e-05 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 0.9729
[-0.       -0.       -0.       ...  0.       -0.       -1.207626]
Sparsity at: 0.6458724517167382
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0026e-05 - accuracy: 1.0000 - val_loss: 0.2363 - val_accuracy: 0.9729
[-0.        -0.        -0.        ...  0.        -0.        -1.2153797]
Sparsity at: 0.6458724517167382
Epoch 90/500
235/235 [==============================] - 2s 9ms/step - loss: 9.1006e-06 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9731
[-0.      -0.      -0.      ...  0.      -0.      -1.22327]
Sparsity at: 0.6458724517167382
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 8.2382e-06 - accuracy: 1.0000 - val_loss: 0.2388 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.        -0.        -1.2312164]
Sparsity at: 0.6458724517167382
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 7.4625e-06 - accuracy: 1.0000 - val_loss: 0.2401 - val_accuracy: 0.9730
[-0.       -0.       -0.       ...  0.       -0.       -1.239322]
Sparsity at: 0.6458724517167382
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7547e-06 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9730
[-0.        -0.        -0.        ...  0.        -0.        -1.2475187]
Sparsity at: 0.6458724517167382
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1086e-06 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9732
[-0.      -0.      -0.      ...  0.      -0.      -1.25583]
Sparsity at: 0.6458724517167382
Epoch 95/500
235/235 [==============================] - 2s 9ms/step - loss: 5.5207e-06 - accuracy: 1.0000 - val_loss: 0.2443 - val_accuracy: 0.9731
[-0.        -0.        -0.        ...  0.        -0.        -1.2643262]
Sparsity at: 0.6458724517167382
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 4.9827e-06 - accuracy: 1.0000 - val_loss: 0.2458 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.        -0.        -1.2729176]
Sparsity at: 0.6458724517167382
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4983e-06 - accuracy: 1.0000 - val_loss: 0.2472 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.        -0.        -1.2815895]
Sparsity at: 0.6458724517167382
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0591e-06 - accuracy: 1.0000 - val_loss: 0.2487 - val_accuracy: 0.9732
[-0.        -0.        -0.        ...  0.        -0.        -1.2902172]
Sparsity at: 0.6458724517167382
Epoch 99/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6569e-06 - accuracy: 1.0000 - val_loss: 0.2502 - val_accuracy: 0.9730
[-0.        -0.        -0.        ...  0.        -0.        -1.2989717]
Sparsity at: 0.6458724517167382
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2964e-06 - accuracy: 1.0000 - val_loss: 0.2517 - val_accuracy: 0.9730
[-0.        -0.        -0.        ...  0.        -0.        -1.3079267]
Sparsity at: 0.6458724517167382
Epoch 101/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0437 - accuracy: 0.9874 - val_loss: 0.1909 - val_accuracy: 0.9685
[-0.        -0.        -0.        ...  0.        -0.        -1.3727199]
Sparsity at: 0.759438707081545
Epoch 102/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.1891 - val_accuracy: 0.9690
[-0.        -0.        -0.        ...  0.        -0.        -1.3784107]
Sparsity at: 0.759438707081545
Epoch 103/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 0.1869 - val_accuracy: 0.9693
[-0.        -0.        -0.        ...  0.        -0.        -1.3845935]
Sparsity at: 0.759438707081545
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9992 - val_loss: 0.1845 - val_accuracy: 0.9696
[-0.        -0.        -0.        ...  0.        -0.        -1.3902153]
Sparsity at: 0.759438707081545
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0032 - accuracy: 0.9997 - val_loss: 0.1837 - val_accuracy: 0.9700
[-0.        -0.        -0.        ...  0.        -0.        -1.3957659]
Sparsity at: 0.759438707081545
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 0.9999 - val_loss: 0.1831 - val_accuracy: 0.9706
[-0.        -0.        -0.        ...  0.        -0.        -1.4009901]
Sparsity at: 0.759438707081545
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0020 - accuracy: 0.9999 - val_loss: 0.1830 - val_accuracy: 0.9709
[-0.        -0.        -0.        ...  0.        -0.        -1.4061068]
Sparsity at: 0.759438707081545
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9707
[-0.       -0.       -0.       ...  0.       -0.       -1.411626]
Sparsity at: 0.759438707081545
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1837 - val_accuracy: 0.9712
[-0.        -0.        -0.        ...  0.        -0.        -1.4176506]
Sparsity at: 0.759438707081545
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9713
[-0.        -0.        -0.        ...  0.        -0.        -1.4237087]
Sparsity at: 0.759438707081545
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1849 - val_accuracy: 0.9715
[-0.        -0.        -0.        ...  0.        -0.        -1.4301274]
Sparsity at: 0.759438707081545
Epoch 112/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9714
[-0.        -0.        -0.        ...  0.        -0.        -1.4365122]
Sparsity at: 0.759438707081545
Epoch 113/500
235/235 [==============================] - 2s 9ms/step - loss: 8.9607e-04 - accuracy: 1.0000 - val_loss: 0.1864 - val_accuracy: 0.9716
[-0.        -0.        -0.        ...  0.        -0.        -1.4432533]
Sparsity at: 0.759438707081545
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 8.0414e-04 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9717
[-0.        -0.        -0.        ...  0.        -0.        -1.4501265]
Sparsity at: 0.759438707081545
Epoch 115/500
235/235 [==============================] - 2s 9ms/step - loss: 7.2333e-04 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9713
[-0.        -0.        -0.        ...  0.        -0.        -1.4571528]
Sparsity at: 0.759438707081545
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5286e-04 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9711
[-0.        -0.        -0.        ...  0.        -0.        -1.4645787]
Sparsity at: 0.759438707081545
Epoch 117/500
235/235 [==============================] - 2s 9ms/step - loss: 5.9129e-04 - accuracy: 1.0000 - val_loss: 0.1901 - val_accuracy: 0.9711
[-0.        -0.        -0.        ...  0.        -0.        -1.4722421]
Sparsity at: 0.759438707081545
Epoch 118/500
235/235 [==============================] - 2s 9ms/step - loss: 5.3535e-04 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9712
[-0.        -0.        -0.        ...  0.        -0.        -1.4799819]
Sparsity at: 0.759438707081545
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 4.8516e-04 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9715
[-0.      -0.      -0.      ...  0.      -0.      -1.48814]
Sparsity at: 0.759438707081545
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4021e-04 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9713
[-0.        -0.        -0.        ...  0.        -0.        -1.4964588]
Sparsity at: 0.759438707081545
Epoch 121/500
235/235 [==============================] - 2s 9ms/step - loss: 4.0022e-04 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9714
[-0.        -0.        -0.        ...  0.        -0.        -1.5050299]
Sparsity at: 0.759438707081545
Epoch 122/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6395e-04 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9715
[-0.        -0.        -0.        ...  0.        -0.        -1.5138777]
Sparsity at: 0.759438707081545
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3128e-04 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9714
[-0.        -0.        -0.        ...  0.        -0.        -1.5228974]
Sparsity at: 0.759438707081545
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0125e-04 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9711
[-0.        -0.        -0.        ...  0.        -0.        -1.5323297]
Sparsity at: 0.759438707081545
Epoch 125/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7364e-04 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9712
[-0.        -0.        -0.        ...  0.        -0.        -1.5417112]
Sparsity at: 0.759438707081545
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4883e-04 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9710
[-0.        -0.        -0.        ...  0.        -0.        -1.5515243]
Sparsity at: 0.759438707081545
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2657e-04 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9710
[-0.       -0.       -0.       ...  0.       -0.       -1.561527]
Sparsity at: 0.759438707081545
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0609e-04 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9710
[-0.        -0.        -0.        ...  0.        -0.        -1.5719991]
Sparsity at: 0.759438707081545
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8699e-04 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9709
[-0.        -0.        -0.        ...  0.        -0.        -1.5824245]
Sparsity at: 0.759438707081545
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7025e-04 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9710
[-0.        -0.        -0.        ...  0.        -0.        -1.5932593]
Sparsity at: 0.759438707081545
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5413e-04 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9710
[-0.        -0.        -0.        ...  0.        -0.        -1.6041205]
Sparsity at: 0.759438707081545
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3987e-04 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9710
[-0.       -0.       -0.       ...  0.       -0.       -1.615292]
Sparsity at: 0.759438707081545
Epoch 133/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2686e-04 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9712
[-0.        -0.        -0.        ...  0.        -0.        -1.6266656]
Sparsity at: 0.759438707081545
Epoch 134/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1521e-04 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9713
[-0.        -0.        -0.        ...  0.        -0.        -1.6383166]
Sparsity at: 0.759438707081545
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0424e-04 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9713
[-0.       -0.       -0.       ...  0.       -0.       -1.650125]
Sparsity at: 0.759438707081545
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 9.4507e-05 - accuracy: 1.0000 - val_loss: 0.2169 - val_accuracy: 0.9713
[-0.        -0.        -0.        ...  0.        -0.        -1.6617621]
Sparsity at: 0.759438707081545
Epoch 137/500
235/235 [==============================] - 2s 9ms/step - loss: 8.5613e-05 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9713
[-0.        -0.        -0.        ...  0.        -0.        -1.6738933]
Sparsity at: 0.759438707081545
Epoch 138/500
235/235 [==============================] - 2s 9ms/step - loss: 7.7297e-05 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9713
[-0.        -0.        -0.        ...  0.        -0.        -1.6862351]
Sparsity at: 0.759438707081545
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 6.9997e-05 - accuracy: 1.0000 - val_loss: 0.2223 - val_accuracy: 0.9712
[-0.        -0.        -0.        ...  0.        -0.        -1.6985171]
Sparsity at: 0.759438707081545
Epoch 140/500
235/235 [==============================] - 2s 9ms/step - loss: 6.3077e-05 - accuracy: 1.0000 - val_loss: 0.2242 - val_accuracy: 0.9710
[-0.        -0.        -0.        ...  0.        -0.        -1.7110292]
Sparsity at: 0.759438707081545
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6960e-05 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9715
[-0.        -0.        -0.        ...  0.        -0.        -1.7236226]
Sparsity at: 0.759438707081545
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 5.1429e-05 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9714
[-0.       -0.       -0.       ...  0.       -0.       -1.736623]
Sparsity at: 0.759438707081545
Epoch 143/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6352e-05 - accuracy: 1.0000 - val_loss: 0.2300 - val_accuracy: 0.9713
[-0.        -0.        -0.        ...  0.        -0.        -1.7494847]
Sparsity at: 0.759438707081545
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1827e-05 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9712
[-0.        -0.        -0.        ...  0.        -0.        -1.7626666]
Sparsity at: 0.759438707081545
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7765e-05 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9716
[-0.        -0.        -0.        ...  0.        -0.        -1.7755849]
Sparsity at: 0.759438707081545
Epoch 146/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3946e-05 - accuracy: 1.0000 - val_loss: 0.2357 - val_accuracy: 0.9716
[-0.        -0.        -0.        ...  0.        -0.        -1.7887508]
Sparsity at: 0.759438707081545
Epoch 147/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0543e-05 - accuracy: 1.0000 - val_loss: 0.2378 - val_accuracy: 0.9716
[-0.        -0.        -0.        ...  0.        -0.        -1.8019708]
Sparsity at: 0.759438707081545
Epoch 148/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7562e-05 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9716
[-0.        -0.        -0.        ...  0.        -0.        -1.8155415]
Sparsity at: 0.759438707081545
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4806e-05 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9718
[-0.        -0.        -0.        ...  0.        -0.        -1.8289564]
Sparsity at: 0.759438707081545
Epoch 150/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2303e-05 - accuracy: 1.0000 - val_loss: 0.2438 - val_accuracy: 0.9718
[-0.        -0.        -0.        ...  0.        -0.        -1.8424696]
Sparsity at: 0.759438707081545
Epoch 151/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1061 - accuracy: 0.9722 - val_loss: 0.1920 - val_accuracy: 0.9637
[-0.        -0.        -0.        ... -0.        -0.        -1.7898865]
Sparsity at: 0.8448229613733905
Epoch 152/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0401 - accuracy: 0.9864 - val_loss: 0.1795 - val_accuracy: 0.9664
[-0.        -0.        -0.        ...  0.        -0.        -1.7822611]
Sparsity at: 0.8448229613733905
Epoch 153/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0303 - accuracy: 0.9897 - val_loss: 0.1740 - val_accuracy: 0.9672
[-0.       -0.       -0.       ...  0.       -0.       -1.781373]
Sparsity at: 0.8448229613733905
Epoch 154/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0248 - accuracy: 0.9916 - val_loss: 0.1711 - val_accuracy: 0.9678
[-0.        -0.        -0.        ...  0.        -0.        -1.7810035]
Sparsity at: 0.8448229613733905
Epoch 155/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0211 - accuracy: 0.9933 - val_loss: 0.1695 - val_accuracy: 0.9683
[-0.        -0.        -0.        ...  0.        -0.        -1.7809491]
Sparsity at: 0.8448229613733905
Epoch 156/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0185 - accuracy: 0.9946 - val_loss: 0.1682 - val_accuracy: 0.9682
[-0.        -0.        -0.        ...  0.        -0.        -1.7818695]
Sparsity at: 0.8448229613733905
Epoch 157/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0164 - accuracy: 0.9953 - val_loss: 0.1675 - val_accuracy: 0.9689
[-0.        -0.        -0.        ...  0.        -0.        -1.7831535]
Sparsity at: 0.8448229613733905
Epoch 158/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0148 - accuracy: 0.9959 - val_loss: 0.1670 - val_accuracy: 0.9682
[-0.        -0.        -0.        ...  0.        -0.        -1.7854861]
Sparsity at: 0.8448229613733905
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0134 - accuracy: 0.9966 - val_loss: 0.1669 - val_accuracy: 0.9683
[-0.        -0.        -0.        ...  0.        -0.        -1.7882736]
Sparsity at: 0.8448229613733905
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0122 - accuracy: 0.9969 - val_loss: 0.1669 - val_accuracy: 0.9689
[-0.        -0.        -0.        ...  0.        -0.        -1.7923082]
Sparsity at: 0.8448229613733905
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0112 - accuracy: 0.9972 - val_loss: 0.1671 - val_accuracy: 0.9690
[-0.        -0.        -0.        ...  0.        -0.        -1.7968659]
Sparsity at: 0.8448229613733905
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0103 - accuracy: 0.9975 - val_loss: 0.1672 - val_accuracy: 0.9691
[-0.        -0.        -0.        ...  0.        -0.        -1.8023915]
Sparsity at: 0.8448229613733905
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0096 - accuracy: 0.9979 - val_loss: 0.1676 - val_accuracy: 0.9692
[-0.        -0.        -0.        ...  0.        -0.        -1.8082978]
Sparsity at: 0.8448229613733905
Epoch 164/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0089 - accuracy: 0.9982 - val_loss: 0.1682 - val_accuracy: 0.9692
[-0.        -0.        -0.        ...  0.        -0.        -1.8143963]
Sparsity at: 0.8448229613733905
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0082 - accuracy: 0.9985 - val_loss: 0.1686 - val_accuracy: 0.9693
[-0.       -0.       -0.       ...  0.       -0.       -1.821233]
Sparsity at: 0.8448229613733905
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0077 - accuracy: 0.9986 - val_loss: 0.1694 - val_accuracy: 0.9693
[-0.        -0.        -0.        ...  0.        -0.        -1.8284523]
Sparsity at: 0.8448229613733905
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0072 - accuracy: 0.9989 - val_loss: 0.1701 - val_accuracy: 0.9697
[-0.        -0.        -0.        ...  0.        -0.        -1.8361725]
Sparsity at: 0.8448229613733905
Epoch 168/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0067 - accuracy: 0.9991 - val_loss: 0.1708 - val_accuracy: 0.9697
[-0.        -0.        -0.        ...  0.         0.        -1.8441927]
Sparsity at: 0.8448229613733905
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0062 - accuracy: 0.9992 - val_loss: 0.1717 - val_accuracy: 0.9696
[-0.       -0.       -0.       ...  0.        0.       -1.852512]
Sparsity at: 0.8448229613733905
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0058 - accuracy: 0.9994 - val_loss: 0.1727 - val_accuracy: 0.9697
[-0.       -0.       -0.       ...  0.       -0.       -1.861486]
Sparsity at: 0.8448229613733905
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0055 - accuracy: 0.9995 - val_loss: 0.1736 - val_accuracy: 0.9698
[-0.       -0.       -0.       ...  0.       -0.       -1.870796]
Sparsity at: 0.8448229613733905
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0051 - accuracy: 0.9997 - val_loss: 0.1747 - val_accuracy: 0.9700
[-0.        -0.        -0.        ...  0.        -0.        -1.8802803]
Sparsity at: 0.8448229613733905
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0048 - accuracy: 0.9998 - val_loss: 0.1758 - val_accuracy: 0.9701
[-0.       -0.       -0.       ...  0.       -0.       -1.890179]
Sparsity at: 0.8448229613733905
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0045 - accuracy: 0.9998 - val_loss: 0.1768 - val_accuracy: 0.9704
[-0.        -0.        -0.        ...  0.        -0.        -1.9011841]
Sparsity at: 0.8448229613733905
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0042 - accuracy: 0.9998 - val_loss: 0.1783 - val_accuracy: 0.9698
[-0.        -0.        -0.        ...  0.        -0.        -1.9122967]
Sparsity at: 0.8448229613733905
Epoch 176/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0039 - accuracy: 0.9998 - val_loss: 0.1793 - val_accuracy: 0.9702
[-0.        -0.        -0.        ...  0.         0.        -1.9246377]
Sparsity at: 0.8448229613733905
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0037 - accuracy: 0.9999 - val_loss: 0.1807 - val_accuracy: 0.9703
[-0.        -0.        -0.        ...  0.         0.        -1.9374578]
Sparsity at: 0.8448229613733905
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0035 - accuracy: 0.9999 - val_loss: 0.1819 - val_accuracy: 0.9706
[-0.      -0.      -0.      ...  0.       0.      -1.95094]
Sparsity at: 0.8448229613733905
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0033 - accuracy: 0.9999 - val_loss: 0.1832 - val_accuracy: 0.9701
[-0.       -0.       -0.       ...  0.       -0.       -1.964488]
Sparsity at: 0.8448229613733905
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0031 - accuracy: 0.9999 - val_loss: 0.1846 - val_accuracy: 0.9700
[-0.        -0.        -0.        ...  0.        -0.        -1.9783467]
Sparsity at: 0.8448229613733905
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9999 - val_loss: 0.1860 - val_accuracy: 0.9701
[-0.        -0.        -0.        ...  0.         0.        -1.9927193]
Sparsity at: 0.8448229613733905
Epoch 182/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9701
[-0.        -0.        -0.        ...  0.         0.        -2.0069108]
Sparsity at: 0.8448229613733905
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9699
[-0.       -0.       -0.       ...  0.       -0.       -2.021466]
Sparsity at: 0.8448229613733905
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9697
[-0.        -0.        -0.        ...  0.         0.        -2.0360525]
Sparsity at: 0.8448229613733905
Epoch 185/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9698
[-0.        -0.        -0.        ...  0.         0.        -2.0507748]
Sparsity at: 0.8448229613733905
Epoch 186/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9699
[-0.        -0.        -0.        ...  0.         0.        -2.0660896]
Sparsity at: 0.8448229613733905
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9698
[-0.        -0.        -0.        ...  0.         0.        -2.0810618]
Sparsity at: 0.8448229613733905
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9699
[-0.       -0.       -0.       ...  0.        0.       -2.096153]
Sparsity at: 0.8448229613733905
Epoch 189/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9696
[-0.       -0.       -0.       ...  0.        0.       -2.111315]
Sparsity at: 0.8448229613733905
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9694
[-0.       -0.       -0.       ...  0.        0.       -2.126816]
Sparsity at: 0.8448229613733905
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9693
[-0.        -0.        -0.        ...  0.         0.        -2.1418226]
Sparsity at: 0.8448229613733905
Epoch 192/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9692
[-0.        -0.        -0.        ...  0.         0.        -2.1577709]
Sparsity at: 0.8448229613733905
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9693
[-0.        -0.        -0.        ...  0.         0.        -2.1732016]
Sparsity at: 0.8448229613733905
Epoch 194/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9692
[-0.        -0.        -0.        ...  0.        -0.        -2.1891115]
Sparsity at: 0.8448229613733905
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9692
[-0.        -0.        -0.        ...  0.         0.        -2.2051053]
Sparsity at: 0.8448229613733905
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2100 - val_accuracy: 0.9693
[-0.       -0.       -0.       ...  0.        0.       -2.220661]
Sparsity at: 0.8448229613733905
Epoch 197/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2118 - val_accuracy: 0.9691
[-0.        -0.        -0.        ...  0.         0.        -2.2366815]
Sparsity at: 0.8448229613733905
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9691
[-0.        -0.        -0.        ...  0.         0.        -2.2523768]
Sparsity at: 0.8448229613733905
Epoch 199/500
235/235 [==============================] - 2s 9ms/step - loss: 9.9128e-04 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9690
[-0.        -0.        -0.        ...  0.         0.        -2.2688015]
Sparsity at: 0.8448229613733905
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3407e-04 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9690
[-0.       -0.       -0.       ...  0.        0.       -2.284866]
Sparsity at: 0.8448229613733905
Epoch 201/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2044 - accuracy: 0.9470 - val_loss: 0.2320 - val_accuracy: 0.9506
[-0.        -0.        -0.        ...  0.        -0.        -2.3545854]
Sparsity at: 0.9059985246781116
Epoch 202/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1069 - accuracy: 0.9672 - val_loss: 0.2082 - val_accuracy: 0.9559
[-0.       -0.       -0.       ...  0.       -0.       -2.344783]
Sparsity at: 0.9059985246781116
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0897 - accuracy: 0.9713 - val_loss: 0.1966 - val_accuracy: 0.9585
[-0.       -0.       -0.       ...  0.       -0.       -2.334993]
Sparsity at: 0.9059985246781116
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0800 - accuracy: 0.9740 - val_loss: 0.1894 - val_accuracy: 0.9588
[-0.        -0.        -0.        ...  0.        -0.        -2.3254402]
Sparsity at: 0.9059985246781116
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0735 - accuracy: 0.9761 - val_loss: 0.1841 - val_accuracy: 0.9602
[-0.        -0.        -0.        ...  0.        -0.        -2.3171513]
Sparsity at: 0.9059985246781116
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0687 - accuracy: 0.9776 - val_loss: 0.1800 - val_accuracy: 0.9608
[-0.        -0.        -0.        ...  0.        -0.        -2.3098316]
Sparsity at: 0.9059985246781116
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0648 - accuracy: 0.9787 - val_loss: 0.1767 - val_accuracy: 0.9609
[-0.        -0.        -0.        ...  0.        -0.        -2.3037033]
Sparsity at: 0.9059985246781116
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0617 - accuracy: 0.9795 - val_loss: 0.1738 - val_accuracy: 0.9611
[-0.        -0.        -0.        ...  0.        -0.        -2.2981453]
Sparsity at: 0.9059985246781116
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0591 - accuracy: 0.9802 - val_loss: 0.1714 - val_accuracy: 0.9613
[-0.        -0.        -0.        ...  0.        -0.        -2.2935739]
Sparsity at: 0.9059985246781116
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0569 - accuracy: 0.9808 - val_loss: 0.1694 - val_accuracy: 0.9618
[-0.        -0.        -0.        ...  0.        -0.        -2.2898388]
Sparsity at: 0.9059985246781116
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0549 - accuracy: 0.9816 - val_loss: 0.1675 - val_accuracy: 0.9619
[-0.        -0.        -0.        ...  0.        -0.        -2.2872412]
Sparsity at: 0.9059985246781116
Epoch 212/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0532 - accuracy: 0.9821 - val_loss: 0.1658 - val_accuracy: 0.9626
[-0.        -0.        -0.        ...  0.        -0.        -2.2850935]
Sparsity at: 0.9059985246781116
Epoch 213/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0516 - accuracy: 0.9826 - val_loss: 0.1643 - val_accuracy: 0.9628
[-0.       -0.       -0.       ...  0.       -0.       -2.283854]
Sparsity at: 0.9059985246781116
Epoch 214/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0502 - accuracy: 0.9829 - val_loss: 0.1630 - val_accuracy: 0.9631
[-0.        -0.        -0.        ...  0.        -0.        -2.2835102]
Sparsity at: 0.9059985246781116
Epoch 215/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0489 - accuracy: 0.9834 - val_loss: 0.1618 - val_accuracy: 0.9631
[-0.       -0.       -0.       ... -0.       -0.       -2.283599]
Sparsity at: 0.9059985246781116
Epoch 216/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0477 - accuracy: 0.9837 - val_loss: 0.1607 - val_accuracy: 0.9631
[-0.        -0.        -0.        ...  0.        -0.        -2.2844095]
Sparsity at: 0.9059985246781116
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0467 - accuracy: 0.9841 - val_loss: 0.1597 - val_accuracy: 0.9635
[-0.        -0.        -0.        ... -0.        -0.        -2.2854853]
Sparsity at: 0.9059985246781116
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0457 - accuracy: 0.9844 - val_loss: 0.1589 - val_accuracy: 0.9638
[-0.        -0.        -0.        ...  0.        -0.        -2.2873783]
Sparsity at: 0.9059985246781116
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0448 - accuracy: 0.9847 - val_loss: 0.1581 - val_accuracy: 0.9640
[-0.        -0.        -0.        ... -0.        -0.        -2.2896485]
Sparsity at: 0.9059985246781116
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0439 - accuracy: 0.9851 - val_loss: 0.1574 - val_accuracy: 0.9638
[-0.        -0.        -0.        ...  0.        -0.        -2.2931924]
Sparsity at: 0.9059985246781116
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0431 - accuracy: 0.9853 - val_loss: 0.1567 - val_accuracy: 0.9639
[-0.        -0.        -0.        ...  0.        -0.        -2.2963223]
Sparsity at: 0.9059985246781116
Epoch 222/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0424 - accuracy: 0.9858 - val_loss: 0.1562 - val_accuracy: 0.9640
[-0.        -0.        -0.        ... -0.        -0.        -2.2997863]
Sparsity at: 0.9059985246781116
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0416 - accuracy: 0.9860 - val_loss: 0.1557 - val_accuracy: 0.9639
[-0.        -0.        -0.        ...  0.        -0.        -2.3034658]
Sparsity at: 0.9059985246781116
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0410 - accuracy: 0.9862 - val_loss: 0.1552 - val_accuracy: 0.9642
[-0.       -0.       -0.       ... -0.       -0.       -2.307398]
Sparsity at: 0.9059985246781116
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0404 - accuracy: 0.9865 - val_loss: 0.1548 - val_accuracy: 0.9642
[-0.        -0.        -0.        ... -0.        -0.        -2.3117638]
Sparsity at: 0.9059985246781116
Epoch 226/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0398 - accuracy: 0.9868 - val_loss: 0.1545 - val_accuracy: 0.9644
[-0.       -0.       -0.       ... -0.       -0.       -2.316233]
Sparsity at: 0.9059985246781116
Epoch 227/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0392 - accuracy: 0.9870 - val_loss: 0.1541 - val_accuracy: 0.9645
[-0.        -0.        -0.        ... -0.        -0.        -2.3206987]
Sparsity at: 0.9059985246781116
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0386 - accuracy: 0.9873 - val_loss: 0.1539 - val_accuracy: 0.9647
[-0.        -0.        -0.        ... -0.        -0.        -2.3256187]
Sparsity at: 0.9059985246781116
Epoch 229/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0381 - accuracy: 0.9874 - val_loss: 0.1537 - val_accuracy: 0.9649
[-0.        -0.        -0.        ... -0.        -0.        -2.3306527]
Sparsity at: 0.9059985246781116
Epoch 230/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0376 - accuracy: 0.9878 - val_loss: 0.1535 - val_accuracy: 0.9650
[-0.       -0.       -0.       ... -0.       -0.       -2.336068]
Sparsity at: 0.9059985246781116
Epoch 231/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0372 - accuracy: 0.9880 - val_loss: 0.1534 - val_accuracy: 0.9650
[-0.        -0.        -0.        ... -0.        -0.        -2.3414526]
Sparsity at: 0.9059985246781116
Epoch 232/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0367 - accuracy: 0.9881 - val_loss: 0.1533 - val_accuracy: 0.9650
[-0.       -0.       -0.       ... -0.       -0.       -2.347127]
Sparsity at: 0.9059985246781116
Epoch 233/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0363 - accuracy: 0.9884 - val_loss: 0.1532 - val_accuracy: 0.9650
[-0.       -0.       -0.       ... -0.       -0.       -2.353049]
Sparsity at: 0.9059985246781116
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0359 - accuracy: 0.9886 - val_loss: 0.1531 - val_accuracy: 0.9652
[-0.       -0.       -0.       ... -0.       -0.       -2.359361]
Sparsity at: 0.9059985246781116
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0355 - accuracy: 0.9888 - val_loss: 0.1531 - val_accuracy: 0.9651
[-0.        -0.        -0.        ... -0.        -0.        -2.3664706]
Sparsity at: 0.9059985246781116
Epoch 236/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0351 - accuracy: 0.9890 - val_loss: 0.1531 - val_accuracy: 0.9650
[-0.       -0.       -0.       ... -0.       -0.       -2.372792]
Sparsity at: 0.9059985246781116
Epoch 237/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0347 - accuracy: 0.9890 - val_loss: 0.1532 - val_accuracy: 0.9650
[-0.        -0.        -0.        ... -0.        -0.        -2.3790238]
Sparsity at: 0.9059985246781116
Epoch 238/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0343 - accuracy: 0.9893 - val_loss: 0.1532 - val_accuracy: 0.9650
[-0.        -0.        -0.        ... -0.        -0.        -2.3854904]
Sparsity at: 0.9059985246781116
Epoch 239/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0340 - accuracy: 0.9893 - val_loss: 0.1533 - val_accuracy: 0.9650
[-0.        -0.        -0.        ... -0.        -0.        -2.3920777]
Sparsity at: 0.9059985246781116
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0336 - accuracy: 0.9895 - val_loss: 0.1534 - val_accuracy: 0.9650
[-0.        -0.        -0.        ... -0.        -0.        -2.3988426]
Sparsity at: 0.9059985246781116
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0333 - accuracy: 0.9897 - val_loss: 0.1535 - val_accuracy: 0.9649
[-0.        -0.        -0.        ... -0.        -0.        -2.4055161]
Sparsity at: 0.9059985246781116
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0330 - accuracy: 0.9898 - val_loss: 0.1537 - val_accuracy: 0.9648
[-0.       -0.       -0.       ... -0.       -0.       -2.412254]
Sparsity at: 0.9059985246781116
Epoch 243/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0327 - accuracy: 0.9899 - val_loss: 0.1539 - val_accuracy: 0.9646
[-0.        -0.        -0.        ... -0.        -0.        -2.4191165]
Sparsity at: 0.9059985246781116
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0324 - accuracy: 0.9900 - val_loss: 0.1540 - val_accuracy: 0.9646
[-0.        -0.        -0.        ... -0.        -0.        -2.4261296]
Sparsity at: 0.9059985246781116
Epoch 245/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0321 - accuracy: 0.9900 - val_loss: 0.1542 - val_accuracy: 0.9646
[-0.       -0.       -0.       ... -0.       -0.       -2.433433]
Sparsity at: 0.9059985246781116
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0318 - accuracy: 0.9901 - val_loss: 0.1543 - val_accuracy: 0.9647
[-0.      -0.      -0.      ... -0.      -0.      -2.44054]
Sparsity at: 0.9059985246781116
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0315 - accuracy: 0.9902 - val_loss: 0.1546 - val_accuracy: 0.9649
[-0.       -0.       -0.       ... -0.       -0.       -2.447858]
Sparsity at: 0.9059985246781116
Epoch 248/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0313 - accuracy: 0.9903 - val_loss: 0.1548 - val_accuracy: 0.9651
[-0.        -0.        -0.        ... -0.        -0.        -2.4547138]
Sparsity at: 0.9059985246781116
Epoch 249/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0310 - accuracy: 0.9904 - val_loss: 0.1551 - val_accuracy: 0.9653
[-0.       -0.       -0.       ... -0.       -0.       -2.461772]
Sparsity at: 0.9059985246781116
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0307 - accuracy: 0.9906 - val_loss: 0.1553 - val_accuracy: 0.9653
[-0.       -0.       -0.       ... -0.       -0.       -2.468659]
Sparsity at: 0.9059985246781116
Epoch 251/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4910 - accuracy: 0.8537 - val_loss: 0.3351 - val_accuracy: 0.8997
[-0.        -0.        -0.        ... -0.         0.        -2.4395401]
Sparsity at: 0.9469890021459227
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 0.3028 - accuracy: 0.9039 - val_loss: 0.2901 - val_accuracy: 0.9124
[-0.       -0.       -0.       ... -0.        0.       -2.486727]
Sparsity at: 0.9469890021459227
Epoch 253/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2664 - accuracy: 0.9149 - val_loss: 0.2685 - val_accuracy: 0.9192
[-0.        -0.        -0.        ... -0.         0.        -2.5269258]
Sparsity at: 0.9469890021459227
Epoch 254/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2465 - accuracy: 0.9213 - val_loss: 0.2554 - val_accuracy: 0.9237
[-0.        -0.        -0.        ... -0.         0.        -2.5579612]
Sparsity at: 0.9469890021459227
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2340 - accuracy: 0.9255 - val_loss: 0.2465 - val_accuracy: 0.9255
[-0.        -0.        -0.        ... -0.         0.        -2.5808158]
Sparsity at: 0.9469890021459227
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2251 - accuracy: 0.9285 - val_loss: 0.2399 - val_accuracy: 0.9274
[-0.       -0.       -0.       ... -0.        0.       -2.599114]
Sparsity at: 0.9469890021459227
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2184 - accuracy: 0.9304 - val_loss: 0.2346 - val_accuracy: 0.9290
[-0.        -0.        -0.        ... -0.         0.        -2.6143634]
Sparsity at: 0.9469890021459227
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2129 - accuracy: 0.9324 - val_loss: 0.2302 - val_accuracy: 0.9308
[-0.       -0.       -0.       ... -0.        0.       -2.627936]
Sparsity at: 0.9469890021459227
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2083 - accuracy: 0.9336 - val_loss: 0.2264 - val_accuracy: 0.9319
[-0.        -0.        -0.        ... -0.         0.        -2.6397662]
Sparsity at: 0.9469890021459227
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2043 - accuracy: 0.9348 - val_loss: 0.2231 - val_accuracy: 0.9324
[-0.        -0.        -0.        ... -0.         0.        -2.6511111]
Sparsity at: 0.9469890021459227
Epoch 261/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2008 - accuracy: 0.9357 - val_loss: 0.2201 - val_accuracy: 0.9326
[-0.        -0.        -0.        ... -0.         0.        -2.6615276]
Sparsity at: 0.9469890021459227
Epoch 262/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1976 - accuracy: 0.9370 - val_loss: 0.2175 - val_accuracy: 0.9332
[-0.        -0.        -0.        ... -0.         0.        -2.6712673]
Sparsity at: 0.9469890021459227
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1948 - accuracy: 0.9379 - val_loss: 0.2151 - val_accuracy: 0.9340
[-0.        -0.        -0.        ... -0.         0.        -2.6802719]
Sparsity at: 0.9469890021459227
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1922 - accuracy: 0.9387 - val_loss: 0.2130 - val_accuracy: 0.9350
[-0.        -0.        -0.        ... -0.         0.        -2.6885839]
Sparsity at: 0.9469890021459227
Epoch 265/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1898 - accuracy: 0.9398 - val_loss: 0.2110 - val_accuracy: 0.9355
[-0.       -0.       -0.       ... -0.        0.       -2.696388]
Sparsity at: 0.9469890021459227
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1876 - accuracy: 0.9406 - val_loss: 0.2092 - val_accuracy: 0.9365
[-0.       -0.       -0.       ... -0.        0.       -2.703596]
Sparsity at: 0.9469890021459227
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1856 - accuracy: 0.9411 - val_loss: 0.2076 - val_accuracy: 0.9376
[-0.        -0.        -0.        ... -0.         0.        -2.7103546]
Sparsity at: 0.9469890021459227
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1838 - accuracy: 0.9419 - val_loss: 0.2061 - val_accuracy: 0.9379
[-0.        -0.        -0.        ... -0.         0.        -2.7161982]
Sparsity at: 0.9469890021459227
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1821 - accuracy: 0.9427 - val_loss: 0.2048 - val_accuracy: 0.9383
[-0.        -0.        -0.        ... -0.         0.        -2.7220504]
Sparsity at: 0.9469890021459227
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1805 - accuracy: 0.9430 - val_loss: 0.2036 - val_accuracy: 0.9391
[-0.       -0.       -0.       ... -0.        0.       -2.727254]
Sparsity at: 0.9469890021459227
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1790 - accuracy: 0.9436 - val_loss: 0.2025 - val_accuracy: 0.9399
[-0.       -0.       -0.       ... -0.        0.       -2.732266]
Sparsity at: 0.9469890021459227
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1776 - accuracy: 0.9438 - val_loss: 0.2015 - val_accuracy: 0.9401
[-0.        -0.        -0.        ... -0.         0.        -2.7367654]
Sparsity at: 0.9469890021459227
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1763 - accuracy: 0.9442 - val_loss: 0.2006 - val_accuracy: 0.9403
[-0.      -0.      -0.      ... -0.       0.      -2.74053]
Sparsity at: 0.9469890021459227
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1751 - accuracy: 0.9445 - val_loss: 0.1998 - val_accuracy: 0.9402
[-0.        -0.        -0.        ... -0.         0.        -2.7444315]
Sparsity at: 0.9469890021459227
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1740 - accuracy: 0.9445 - val_loss: 0.1990 - val_accuracy: 0.9409
[-0.       -0.       -0.       ... -0.        0.       -2.747985]
Sparsity at: 0.9469890021459227
Epoch 276/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1729 - accuracy: 0.9449 - val_loss: 0.1983 - val_accuracy: 0.9409
[-0.        -0.        -0.        ... -0.         0.        -2.7510498]
Sparsity at: 0.9469890021459227
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1718 - accuracy: 0.9453 - val_loss: 0.1976 - val_accuracy: 0.9412
[-0.       -0.       -0.       ... -0.        0.       -2.753793]
Sparsity at: 0.9469890021459227
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1709 - accuracy: 0.9455 - val_loss: 0.1970 - val_accuracy: 0.9412
[-0.        -0.        -0.        ... -0.         0.        -2.7562249]
Sparsity at: 0.9469890021459227
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1699 - accuracy: 0.9459 - val_loss: 0.1965 - val_accuracy: 0.9418
[-0.       -0.       -0.       ... -0.        0.       -2.758989]
Sparsity at: 0.9469890021459227
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1691 - accuracy: 0.9462 - val_loss: 0.1960 - val_accuracy: 0.9419
[-0.        -0.        -0.        ... -0.         0.        -2.7609184]
Sparsity at: 0.9469890021459227
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1682 - accuracy: 0.9464 - val_loss: 0.1955 - val_accuracy: 0.9421
[-0.        -0.        -0.        ... -0.         0.        -2.7632003]
Sparsity at: 0.9469890021459227
Epoch 282/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1674 - accuracy: 0.9467 - val_loss: 0.1950 - val_accuracy: 0.9424
[-0.        -0.        -0.        ... -0.         0.        -2.7653513]
Sparsity at: 0.9469890021459227
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1666 - accuracy: 0.9470 - val_loss: 0.1946 - val_accuracy: 0.9425
[-0.        -0.        -0.        ... -0.         0.        -2.7672694]
Sparsity at: 0.9469890021459227
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1658 - accuracy: 0.9473 - val_loss: 0.1942 - val_accuracy: 0.9426
[-0.        -0.        -0.        ... -0.         0.        -2.7693048]
Sparsity at: 0.9469890021459227
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1651 - accuracy: 0.9475 - val_loss: 0.1939 - val_accuracy: 0.9427
[-0.        -0.        -0.        ... -0.         0.        -2.7712476]
Sparsity at: 0.9469890021459227
Epoch 286/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1644 - accuracy: 0.9478 - val_loss: 0.1935 - val_accuracy: 0.9425
[-0.        -0.        -0.        ... -0.         0.        -2.7729177]
Sparsity at: 0.9469890021459227
Epoch 287/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1638 - accuracy: 0.9479 - val_loss: 0.1932 - val_accuracy: 0.9427
[-0.        -0.        -0.        ... -0.         0.        -2.7741919]
Sparsity at: 0.9469890021459227
Epoch 288/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1631 - accuracy: 0.9480 - val_loss: 0.1928 - val_accuracy: 0.9427
[-0.        -0.        -0.        ... -0.         0.        -2.7758865]
Sparsity at: 0.9469890021459227
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1625 - accuracy: 0.9482 - val_loss: 0.1926 - val_accuracy: 0.9428
[-0.        -0.        -0.        ... -0.         0.        -2.7775726]
Sparsity at: 0.9469890021459227
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1620 - accuracy: 0.9484 - val_loss: 0.1923 - val_accuracy: 0.9429
[-0.        -0.        -0.        ... -0.         0.        -2.7786777]
Sparsity at: 0.9469890021459227
Epoch 291/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1614 - accuracy: 0.9486 - val_loss: 0.1920 - val_accuracy: 0.9431
[-0.       -0.       -0.       ... -0.        0.       -2.780527]
Sparsity at: 0.9469890021459227
Epoch 292/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1608 - accuracy: 0.9489 - val_loss: 0.1918 - val_accuracy: 0.9429
[-0.        -0.        -0.        ... -0.         0.        -2.7816768]
Sparsity at: 0.9469890021459227
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1603 - accuracy: 0.9491 - val_loss: 0.1915 - val_accuracy: 0.9428
[-0.        -0.        -0.        ... -0.         0.        -2.7832756]
Sparsity at: 0.9469890021459227
Epoch 294/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1598 - accuracy: 0.9493 - val_loss: 0.1913 - val_accuracy: 0.9428
[-0.        -0.        -0.        ... -0.         0.        -2.7852101]
Sparsity at: 0.9469890021459227
Epoch 295/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1593 - accuracy: 0.9494 - val_loss: 0.1910 - val_accuracy: 0.9432
[-0.        -0.        -0.        ... -0.         0.        -2.7865098]
Sparsity at: 0.9469890021459227
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1588 - accuracy: 0.9496 - val_loss: 0.1908 - val_accuracy: 0.9434
[-0.     -0.     -0.     ... -0.      0.     -2.7879]
Sparsity at: 0.9469890021459227
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1583 - accuracy: 0.9497 - val_loss: 0.1906 - val_accuracy: 0.9436
[-0.        -0.        -0.        ... -0.         0.        -2.7897391]
Sparsity at: 0.9469890021459227
Epoch 298/500
235/235 [==============================] - 2s 10ms/step - loss: 0.1579 - accuracy: 0.9500 - val_loss: 0.1904 - val_accuracy: 0.9438
[-0.        -0.        -0.        ... -0.         0.        -2.7914326]
Sparsity at: 0.9469890021459227
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1574 - accuracy: 0.9500 - val_loss: 0.1903 - val_accuracy: 0.9438
[-0.        -0.        -0.        ...  0.         0.        -2.7926548]
Sparsity at: 0.9469890021459227
Epoch 300/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1570 - accuracy: 0.9502 - val_loss: 0.1901 - val_accuracy: 0.9438
[-0.       -0.       -0.       ... -0.        0.       -2.794541]
Sparsity at: 0.9469890021459227
Epoch 301/500
235/235 [==============================] - 2s 9ms/step - loss: 0.7943 - accuracy: 0.7459 - val_loss: 0.6487 - val_accuracy: 0.8029
[-0.        -0.        -0.        ...  0.         0.        -2.6900098]
Sparsity at: 0.9718515289699571
Epoch 302/500
235/235 [==============================] - 2s 9ms/step - loss: 0.6332 - accuracy: 0.8027 - val_loss: 0.6057 - val_accuracy: 0.8191
[-0.        -0.        -0.        ...  0.         0.        -2.6023936]
Sparsity at: 0.9718515289699571
Epoch 303/500
235/235 [==============================] - 2s 9ms/step - loss: 0.6028 - accuracy: 0.8134 - val_loss: 0.5846 - val_accuracy: 0.8249
[-0.        -0.        -0.        ...  0.         0.        -2.5434372]
Sparsity at: 0.9718515289699571
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5856 - accuracy: 0.8182 - val_loss: 0.5715 - val_accuracy: 0.8291
[-0.        -0.        -0.        ...  0.         0.        -2.5059433]
Sparsity at: 0.9718515289699571
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5739 - accuracy: 0.8221 - val_loss: 0.5624 - val_accuracy: 0.8326
[-0.       -0.       -0.       ...  0.        0.       -2.483021]
Sparsity at: 0.9718515289699571
Epoch 306/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5651 - accuracy: 0.8250 - val_loss: 0.5554 - val_accuracy: 0.8351
[-0.       -0.       -0.       ...  0.        0.       -2.468537]
Sparsity at: 0.9718515289699571
Epoch 307/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5581 - accuracy: 0.8270 - val_loss: 0.5498 - val_accuracy: 0.8365
[-0.        -0.        -0.        ...  0.         0.        -2.4610317]
Sparsity at: 0.9718515289699571
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5520 - accuracy: 0.8290 - val_loss: 0.5448 - val_accuracy: 0.8385
[-0.        -0.        -0.        ...  0.         0.        -2.4578817]
Sparsity at: 0.9718515289699571
Epoch 309/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5467 - accuracy: 0.8309 - val_loss: 0.5407 - val_accuracy: 0.8393
[-0.        -0.        -0.        ...  0.         0.        -2.4593544]
Sparsity at: 0.9718515289699571
Epoch 310/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5423 - accuracy: 0.8327 - val_loss: 0.5373 - val_accuracy: 0.8404
[-0.        -0.        -0.        ...  0.         0.        -2.4633198]
Sparsity at: 0.9718515289699571
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5387 - accuracy: 0.8337 - val_loss: 0.5343 - val_accuracy: 0.8411
[-0.       -0.       -0.       ...  0.        0.       -2.467665]
Sparsity at: 0.9718515289699571
Epoch 312/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5355 - accuracy: 0.8352 - val_loss: 0.5317 - val_accuracy: 0.8425
[-0.        -0.        -0.        ...  0.         0.        -2.4722986]
Sparsity at: 0.9718515289699571
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5327 - accuracy: 0.8360 - val_loss: 0.5292 - val_accuracy: 0.8434
[-0.       -0.       -0.       ...  0.        0.       -2.477545]
Sparsity at: 0.9718515289699571
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5300 - accuracy: 0.8365 - val_loss: 0.5268 - val_accuracy: 0.8438
[-0.        -0.        -0.        ...  0.         0.        -2.4834504]
Sparsity at: 0.9718515289699571
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5274 - accuracy: 0.8375 - val_loss: 0.5244 - val_accuracy: 0.8449
[-0.        -0.        -0.        ...  0.         0.        -2.4892974]
Sparsity at: 0.9718515289699571
Epoch 316/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5249 - accuracy: 0.8386 - val_loss: 0.5221 - val_accuracy: 0.8455
[-0.        -0.        -0.        ...  0.         0.        -2.4956393]
Sparsity at: 0.9718515289699571
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5224 - accuracy: 0.8395 - val_loss: 0.5197 - val_accuracy: 0.8466
[-0.        -0.        -0.        ...  0.         0.        -2.5028048]
Sparsity at: 0.9718515289699571
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5200 - accuracy: 0.8399 - val_loss: 0.5177 - val_accuracy: 0.8471
[-0.       -0.       -0.       ...  0.        0.       -2.510462]
Sparsity at: 0.9718515289699571
Epoch 319/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5179 - accuracy: 0.8403 - val_loss: 0.5159 - val_accuracy: 0.8475
[-0.        -0.        -0.        ...  0.         0.        -2.5186467]
Sparsity at: 0.9718515289699571
Epoch 320/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5160 - accuracy: 0.8408 - val_loss: 0.5144 - val_accuracy: 0.8485
[-0.        -0.        -0.        ...  0.         0.        -2.5276606]
Sparsity at: 0.9718515289699571
Epoch 321/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5144 - accuracy: 0.8414 - val_loss: 0.5130 - val_accuracy: 0.8484
[-0.        -0.        -0.        ...  0.         0.        -2.5373785]
Sparsity at: 0.9718515289699571
Epoch 322/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5129 - accuracy: 0.8416 - val_loss: 0.5117 - val_accuracy: 0.8488
[-0.        -0.        -0.        ...  0.         0.        -2.5469127]
Sparsity at: 0.9718515289699571
Epoch 323/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5115 - accuracy: 0.8421 - val_loss: 0.5106 - val_accuracy: 0.8495
[-0.        -0.        -0.        ...  0.         0.        -2.5566618]
Sparsity at: 0.9718515289699571
Epoch 324/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5102 - accuracy: 0.8425 - val_loss: 0.5095 - val_accuracy: 0.8502
[-0.        -0.        -0.        ...  0.         0.        -2.5659082]
Sparsity at: 0.9718515289699571
Epoch 325/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5091 - accuracy: 0.8430 - val_loss: 0.5085 - val_accuracy: 0.8508
[-0.       -0.       -0.       ...  0.        0.       -2.575369]
Sparsity at: 0.9718515289699571
Epoch 326/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5080 - accuracy: 0.8430 - val_loss: 0.5075 - val_accuracy: 0.8512
[-0.        -0.        -0.        ...  0.         0.        -2.5848129]
Sparsity at: 0.9718515289699571
Epoch 327/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5070 - accuracy: 0.8433 - val_loss: 0.5066 - val_accuracy: 0.8515
[-0.     -0.     -0.     ... -0.      0.     -2.5937]
Sparsity at: 0.9718515289699571
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5061 - accuracy: 0.8435 - val_loss: 0.5058 - val_accuracy: 0.8517
[-0.        -0.        -0.        ... -0.         0.        -2.6022801]
Sparsity at: 0.9718515289699571
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5051 - accuracy: 0.8436 - val_loss: 0.5050 - val_accuracy: 0.8522
[-0.       -0.       -0.       ...  0.        0.       -2.610608]
Sparsity at: 0.9718515289699571
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5043 - accuracy: 0.8440 - val_loss: 0.5043 - val_accuracy: 0.8524
[-0.        -0.        -0.        ... -0.         0.        -2.6188123]
Sparsity at: 0.9718515289699571
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5035 - accuracy: 0.8443 - val_loss: 0.5036 - val_accuracy: 0.8533
[-0.        -0.        -0.        ... -0.         0.        -2.6268635]
Sparsity at: 0.9718515289699571
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5028 - accuracy: 0.8444 - val_loss: 0.5028 - val_accuracy: 0.8539
[-0.        -0.        -0.        ... -0.         0.        -2.6347785]
Sparsity at: 0.9718515289699571
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5021 - accuracy: 0.8447 - val_loss: 0.5022 - val_accuracy: 0.8540
[-0.        -0.        -0.        ... -0.         0.        -2.6427677]
Sparsity at: 0.9718515289699571
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5014 - accuracy: 0.8449 - val_loss: 0.5016 - val_accuracy: 0.8540
[-0.        -0.        -0.        ... -0.         0.        -2.6499667]
Sparsity at: 0.9718515289699571
Epoch 335/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5007 - accuracy: 0.8453 - val_loss: 0.5010 - val_accuracy: 0.8543
[-0.        -0.        -0.        ... -0.         0.        -2.6574116]
Sparsity at: 0.9718515289699571
Epoch 336/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5001 - accuracy: 0.8454 - val_loss: 0.5004 - val_accuracy: 0.8543
[-0.        -0.        -0.        ... -0.         0.        -2.6643114]
Sparsity at: 0.9718515289699571
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4995 - accuracy: 0.8456 - val_loss: 0.4999 - val_accuracy: 0.8544
[-0.        -0.        -0.        ... -0.         0.        -2.6710615]
Sparsity at: 0.9718515289699571
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4989 - accuracy: 0.8457 - val_loss: 0.4993 - val_accuracy: 0.8549
[-0.       -0.       -0.       ... -0.        0.       -2.677571]
Sparsity at: 0.9718515289699571
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4982 - accuracy: 0.8461 - val_loss: 0.4986 - val_accuracy: 0.8547
[-0.       -0.       -0.       ... -0.        0.       -2.684011]
Sparsity at: 0.9718515289699571
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4976 - accuracy: 0.8465 - val_loss: 0.4980 - val_accuracy: 0.8547
[-0.        -0.        -0.        ... -0.         0.        -2.6896653]
Sparsity at: 0.9718515289699571
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4967 - accuracy: 0.8470 - val_loss: 0.4970 - val_accuracy: 0.8548
[-0.        -0.        -0.        ... -0.         0.        -2.6944613]
Sparsity at: 0.9718515289699571
Epoch 342/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4959 - accuracy: 0.8471 - val_loss: 0.4961 - val_accuracy: 0.8547
[-0.       -0.       -0.       ... -0.        0.       -2.698111]
Sparsity at: 0.9718515289699571
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4952 - accuracy: 0.8473 - val_loss: 0.4956 - val_accuracy: 0.8550
[-0.        -0.        -0.        ... -0.         0.        -2.7018592]
Sparsity at: 0.9718515289699571
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4946 - accuracy: 0.8476 - val_loss: 0.4951 - val_accuracy: 0.8552
[-0.        -0.        -0.        ... -0.         0.        -2.7057436]
Sparsity at: 0.9718515289699571
Epoch 345/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4941 - accuracy: 0.8476 - val_loss: 0.4947 - val_accuracy: 0.8552
[-0.        -0.        -0.        ... -0.         0.        -2.7095397]
Sparsity at: 0.9718515289699571
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4937 - accuracy: 0.8477 - val_loss: 0.4943 - val_accuracy: 0.8554
[-0.       -0.       -0.       ... -0.        0.       -2.714191]
Sparsity at: 0.9718515289699571
Epoch 347/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4932 - accuracy: 0.8479 - val_loss: 0.4939 - val_accuracy: 0.8553
[-0.        -0.        -0.        ... -0.         0.        -2.7192228]
Sparsity at: 0.9718515289699571
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4928 - accuracy: 0.8482 - val_loss: 0.4936 - val_accuracy: 0.8554
[-0.        -0.        -0.        ... -0.         0.        -2.7239852]
Sparsity at: 0.9718515289699571
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4924 - accuracy: 0.8482 - val_loss: 0.4933 - val_accuracy: 0.8554
[-0.       -0.       -0.       ... -0.        0.       -2.728846]
Sparsity at: 0.9718515289699571
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4920 - accuracy: 0.8484 - val_loss: 0.4930 - val_accuracy: 0.8557
[-0.        -0.        -0.        ... -0.         0.        -2.7337844]
Sparsity at: 0.9718515289699571
Epoch 351/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3577 - accuracy: 0.5720 - val_loss: 1.2285 - val_accuracy: 0.5999
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 352/500
235/235 [==============================] - 2s 10ms/step - loss: 1.2413 - accuracy: 0.5983 - val_loss: 1.2112 - val_accuracy: 0.6024
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2272 - accuracy: 0.5972 - val_loss: 1.2045 - val_accuracy: 0.6030
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 354/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2200 - accuracy: 0.5986 - val_loss: 1.2002 - val_accuracy: 0.6044
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 355/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2152 - accuracy: 0.6003 - val_loss: 1.1967 - val_accuracy: 0.6056
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2114 - accuracy: 0.6006 - val_loss: 1.1937 - val_accuracy: 0.6060
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2081 - accuracy: 0.6015 - val_loss: 1.1908 - val_accuracy: 0.6072
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2048 - accuracy: 0.6021 - val_loss: 1.1874 - val_accuracy: 0.6082
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2002 - accuracy: 0.6031 - val_loss: 1.1826 - val_accuracy: 0.6094
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.6043 - val_loss: 1.1799 - val_accuracy: 0.6110
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1932 - accuracy: 0.6038 - val_loss: 1.1782 - val_accuracy: 0.6107
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1914 - accuracy: 0.6042 - val_loss: 1.1766 - val_accuracy: 0.6107
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1899 - accuracy: 0.6041 - val_loss: 1.1752 - val_accuracy: 0.6110
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1886 - accuracy: 0.6043 - val_loss: 1.1741 - val_accuracy: 0.6112
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 365/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1874 - accuracy: 0.6044 - val_loss: 1.1730 - val_accuracy: 0.6112
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1863 - accuracy: 0.6044 - val_loss: 1.1721 - val_accuracy: 0.6116
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1853 - accuracy: 0.6046 - val_loss: 1.1713 - val_accuracy: 0.6119
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1845 - accuracy: 0.6051 - val_loss: 1.1705 - val_accuracy: 0.6121
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1836 - accuracy: 0.6051 - val_loss: 1.1699 - val_accuracy: 0.6121
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1829 - accuracy: 0.6048 - val_loss: 1.1693 - val_accuracy: 0.6128
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1823 - accuracy: 0.6055 - val_loss: 1.1688 - val_accuracy: 0.6129
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1817 - accuracy: 0.6055 - val_loss: 1.1684 - val_accuracy: 0.6130
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 373/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1811 - accuracy: 0.6060 - val_loss: 1.1679 - val_accuracy: 0.6130
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1806 - accuracy: 0.6062 - val_loss: 1.1676 - val_accuracy: 0.6131
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1801 - accuracy: 0.6061 - val_loss: 1.1672 - val_accuracy: 0.6132
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1797 - accuracy: 0.6060 - val_loss: 1.1669 - val_accuracy: 0.6138
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1793 - accuracy: 0.6060 - val_loss: 1.1666 - val_accuracy: 0.6137
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1789 - accuracy: 0.6065 - val_loss: 1.1663 - val_accuracy: 0.6142
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1786 - accuracy: 0.6066 - val_loss: 1.1660 - val_accuracy: 0.6145
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1782 - accuracy: 0.6068 - val_loss: 1.1657 - val_accuracy: 0.6145
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1779 - accuracy: 0.6069 - val_loss: 1.1655 - val_accuracy: 0.6150
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1776 - accuracy: 0.6073 - val_loss: 1.1653 - val_accuracy: 0.6149
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1773 - accuracy: 0.6069 - val_loss: 1.1650 - val_accuracy: 0.6148
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1770 - accuracy: 0.6075 - val_loss: 1.1648 - val_accuracy: 0.6149
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1767 - accuracy: 0.6073 - val_loss: 1.1646 - val_accuracy: 0.6150
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1764 - accuracy: 0.6072 - val_loss: 1.1643 - val_accuracy: 0.6151
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 387/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1762 - accuracy: 0.6079 - val_loss: 1.1641 - val_accuracy: 0.6148
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1759 - accuracy: 0.6079 - val_loss: 1.1639 - val_accuracy: 0.6152
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1756 - accuracy: 0.6077 - val_loss: 1.1636 - val_accuracy: 0.6151
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1753 - accuracy: 0.6078 - val_loss: 1.1634 - val_accuracy: 0.6152
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 391/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1751 - accuracy: 0.6083 - val_loss: 1.1632 - val_accuracy: 0.6153
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1748 - accuracy: 0.6079 - val_loss: 1.1630 - val_accuracy: 0.6153
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1746 - accuracy: 0.6085 - val_loss: 1.1629 - val_accuracy: 0.6154
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1743 - accuracy: 0.6082 - val_loss: 1.1627 - val_accuracy: 0.6154
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 395/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1741 - accuracy: 0.6085 - val_loss: 1.1626 - val_accuracy: 0.6154
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1740 - accuracy: 0.6082 - val_loss: 1.1624 - val_accuracy: 0.6153
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1738 - accuracy: 0.6084 - val_loss: 1.1623 - val_accuracy: 0.6154
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1736 - accuracy: 0.6085 - val_loss: 1.1622 - val_accuracy: 0.6154
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1734 - accuracy: 0.6087 - val_loss: 1.1621 - val_accuracy: 0.6152
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 400/500
235/235 [==============================] - 2s 10ms/step - loss: 1.1732 - accuracy: 0.6088 - val_loss: 1.1620 - val_accuracy: 0.6154
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 401/500
235/235 [==============================] - 2s 10ms/step - loss: 1.7721 - accuracy: 0.3932 - val_loss: 1.6076 - val_accuracy: 0.4268
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 402/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6161 - accuracy: 0.4288 - val_loss: 1.5961 - val_accuracy: 0.4307
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 403/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6074 - accuracy: 0.4329 - val_loss: 1.5890 - val_accuracy: 0.4325
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 404/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6017 - accuracy: 0.4351 - val_loss: 1.5839 - val_accuracy: 0.4321
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5974 - accuracy: 0.4370 - val_loss: 1.5797 - val_accuracy: 0.4335
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 406/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5939 - accuracy: 0.4392 - val_loss: 1.5763 - val_accuracy: 0.4545
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 407/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5908 - accuracy: 0.4448 - val_loss: 1.5732 - val_accuracy: 0.4555
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 408/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5880 - accuracy: 0.4505 - val_loss: 1.5706 - val_accuracy: 0.4564
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5856 - accuracy: 0.4554 - val_loss: 1.5684 - val_accuracy: 0.4571
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5836 - accuracy: 0.4580 - val_loss: 1.5666 - val_accuracy: 0.4575
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5819 - accuracy: 0.4604 - val_loss: 1.5652 - val_accuracy: 0.4577
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5806 - accuracy: 0.4616 - val_loss: 1.5641 - val_accuracy: 0.4581
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5795 - accuracy: 0.4615 - val_loss: 1.5630 - val_accuracy: 0.4584
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5784 - accuracy: 0.4616 - val_loss: 1.5622 - val_accuracy: 0.4591
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5775 - accuracy: 0.4615 - val_loss: 1.5614 - val_accuracy: 0.4590
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5768 - accuracy: 0.4610 - val_loss: 1.5607 - val_accuracy: 0.4599
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 417/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5760 - accuracy: 0.4609 - val_loss: 1.5601 - val_accuracy: 0.4610
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 418/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5754 - accuracy: 0.4605 - val_loss: 1.5596 - val_accuracy: 0.4607
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5748 - accuracy: 0.4603 - val_loss: 1.5589 - val_accuracy: 0.4619
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5743 - accuracy: 0.4600 - val_loss: 1.5585 - val_accuracy: 0.4599
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5738 - accuracy: 0.4598 - val_loss: 1.5582 - val_accuracy: 0.4595
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5734 - accuracy: 0.4596 - val_loss: 1.5578 - val_accuracy: 0.4600
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5730 - accuracy: 0.4594 - val_loss: 1.5575 - val_accuracy: 0.4601
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5726 - accuracy: 0.4587 - val_loss: 1.5571 - val_accuracy: 0.4602
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5722 - accuracy: 0.4584 - val_loss: 1.5569 - val_accuracy: 0.4605
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5719 - accuracy: 0.4580 - val_loss: 1.5565 - val_accuracy: 0.4607
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 427/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5716 - accuracy: 0.4577 - val_loss: 1.5563 - val_accuracy: 0.4609
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 428/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5713 - accuracy: 0.4575 - val_loss: 1.5561 - val_accuracy: 0.4609
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 429/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5710 - accuracy: 0.4575 - val_loss: 1.5559 - val_accuracy: 0.4611
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 430/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5708 - accuracy: 0.4577 - val_loss: 1.5557 - val_accuracy: 0.4608
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 431/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5706 - accuracy: 0.4576 - val_loss: 1.5555 - val_accuracy: 0.4611
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5703 - accuracy: 0.4575 - val_loss: 1.5553 - val_accuracy: 0.4613
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5701 - accuracy: 0.4575 - val_loss: 1.5551 - val_accuracy: 0.4618
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5699 - accuracy: 0.4574 - val_loss: 1.5549 - val_accuracy: 0.4616
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5697 - accuracy: 0.4573 - val_loss: 1.5548 - val_accuracy: 0.4611
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5695 - accuracy: 0.4571 - val_loss: 1.5547 - val_accuracy: 0.4612
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5693 - accuracy: 0.4568 - val_loss: 1.5545 - val_accuracy: 0.4613
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5691 - accuracy: 0.4566 - val_loss: 1.5544 - val_accuracy: 0.4614
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5690 - accuracy: 0.4567 - val_loss: 1.5541 - val_accuracy: 0.4617
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5688 - accuracy: 0.4562 - val_loss: 1.5540 - val_accuracy: 0.4616
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5686 - accuracy: 0.4563 - val_loss: 1.5539 - val_accuracy: 0.4615
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 442/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5685 - accuracy: 0.4564 - val_loss: 1.5538 - val_accuracy: 0.4615
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 443/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5683 - accuracy: 0.4563 - val_loss: 1.5537 - val_accuracy: 0.4617
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5681 - accuracy: 0.4560 - val_loss: 1.5534 - val_accuracy: 0.4618
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5680 - accuracy: 0.4560 - val_loss: 1.5533 - val_accuracy: 0.4619
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5678 - accuracy: 0.4558 - val_loss: 1.5532 - val_accuracy: 0.4621
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 447/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5677 - accuracy: 0.4557 - val_loss: 1.5531 - val_accuracy: 0.4620
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 448/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5676 - accuracy: 0.4555 - val_loss: 1.5530 - val_accuracy: 0.4624
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5675 - accuracy: 0.4556 - val_loss: 1.5528 - val_accuracy: 0.4624
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5673 - accuracy: 0.4556 - val_loss: 1.5527 - val_accuracy: 0.4625
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 451/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5672 - accuracy: 0.4555 - val_loss: 1.5527 - val_accuracy: 0.4623
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 452/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5671 - accuracy: 0.4552 - val_loss: 1.5525 - val_accuracy: 0.4622
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.4553 - val_loss: 1.5524 - val_accuracy: 0.4622
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5669 - accuracy: 0.4554 - val_loss: 1.5523 - val_accuracy: 0.4622
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5668 - accuracy: 0.4553 - val_loss: 1.5522 - val_accuracy: 0.4623
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 456/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5667 - accuracy: 0.4551 - val_loss: 1.5521 - val_accuracy: 0.4627
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 457/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5666 - accuracy: 0.4551 - val_loss: 1.5520 - val_accuracy: 0.4627
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 458/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5665 - accuracy: 0.4549 - val_loss: 1.5520 - val_accuracy: 0.4635
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 459/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5664 - accuracy: 0.4549 - val_loss: 1.5519 - val_accuracy: 0.4637
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 460/500
235/235 [==============================] - 4s 17ms/step - loss: 1.5663 - accuracy: 0.4547 - val_loss: 1.5517 - val_accuracy: 0.4634
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 461/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5662 - accuracy: 0.4548 - val_loss: 1.5516 - val_accuracy: 0.4638
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 462/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5661 - accuracy: 0.4547 - val_loss: 1.5516 - val_accuracy: 0.4643
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 463/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5660 - accuracy: 0.4547 - val_loss: 1.5515 - val_accuracy: 0.4643
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 464/500
235/235 [==============================] - 3s 11ms/step - loss: 1.5659 - accuracy: 0.4546 - val_loss: 1.5513 - val_accuracy: 0.4643
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 465/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5658 - accuracy: 0.4545 - val_loss: 1.5512 - val_accuracy: 0.4649
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 466/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5657 - accuracy: 0.4545 - val_loss: 1.5512 - val_accuracy: 0.4647
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 467/500
235/235 [==============================] - 3s 12ms/step - loss: 1.5656 - accuracy: 0.4543 - val_loss: 1.5511 - val_accuracy: 0.4645
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 468/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5656 - accuracy: 0.4543 - val_loss: 1.5510 - val_accuracy: 0.4642
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 469/500
235/235 [==============================] - 3s 12ms/step - loss: 1.5655 - accuracy: 0.4542 - val_loss: 1.5509 - val_accuracy: 0.4643
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 470/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5654 - accuracy: 0.4541 - val_loss: 1.5508 - val_accuracy: 0.4645
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 471/500
235/235 [==============================] - 3s 11ms/step - loss: 1.5653 - accuracy: 0.4539 - val_loss: 1.5507 - val_accuracy: 0.4647
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 472/500
235/235 [==============================] - 3s 12ms/step - loss: 1.5652 - accuracy: 0.4541 - val_loss: 1.5508 - val_accuracy: 0.4647
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 473/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5652 - accuracy: 0.4538 - val_loss: 1.5506 - val_accuracy: 0.4647
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 474/500
235/235 [==============================] - 3s 12ms/step - loss: 1.5651 - accuracy: 0.4538 - val_loss: 1.5505 - val_accuracy: 0.4648
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 475/500
235/235 [==============================] - 3s 11ms/step - loss: 1.5650 - accuracy: 0.4539 - val_loss: 1.5505 - val_accuracy: 0.4649
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 476/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5650 - accuracy: 0.4539 - val_loss: 1.5505 - val_accuracy: 0.4646
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 477/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5649 - accuracy: 0.4536 - val_loss: 1.5504 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 478/500
235/235 [==============================] - 2s 11ms/step - loss: 1.5648 - accuracy: 0.4537 - val_loss: 1.5504 - val_accuracy: 0.4652
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 479/500
235/235 [==============================] - 3s 12ms/step - loss: 1.5647 - accuracy: 0.4537 - val_loss: 1.5503 - val_accuracy: 0.4652
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 480/500
235/235 [==============================] - 3s 12ms/step - loss: 1.5647 - accuracy: 0.4538 - val_loss: 1.5502 - val_accuracy: 0.4652
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 481/500
235/235 [==============================] - 3s 11ms/step - loss: 1.5646 - accuracy: 0.4536 - val_loss: 1.5501 - val_accuracy: 0.4653
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 482/500
235/235 [==============================] - 3s 11ms/step - loss: 1.5645 - accuracy: 0.4534 - val_loss: 1.5500 - val_accuracy: 0.4657
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 483/500
235/235 [==============================] - 2s 11ms/step - loss: 1.5645 - accuracy: 0.4535 - val_loss: 1.5500 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 484/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5644 - accuracy: 0.4535 - val_loss: 1.5499 - val_accuracy: 0.4652
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 485/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5644 - accuracy: 0.4533 - val_loss: 1.5499 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 486/500
235/235 [==============================] - 3s 12ms/step - loss: 1.5643 - accuracy: 0.4533 - val_loss: 1.5499 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 487/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5642 - accuracy: 0.4533 - val_loss: 1.5498 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 488/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5641 - accuracy: 0.4532 - val_loss: 1.5498 - val_accuracy: 0.4653
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 489/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5641 - accuracy: 0.4533 - val_loss: 1.5497 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 490/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5641 - accuracy: 0.4534 - val_loss: 1.5496 - val_accuracy: 0.4653
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 491/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5640 - accuracy: 0.4532 - val_loss: 1.5497 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 492/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5639 - accuracy: 0.4534 - val_loss: 1.5495 - val_accuracy: 0.4653
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 493/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5639 - accuracy: 0.4533 - val_loss: 1.5495 - val_accuracy: 0.4653
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5638 - accuracy: 0.4534 - val_loss: 1.5495 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 495/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5637 - accuracy: 0.4532 - val_loss: 1.5494 - val_accuracy: 0.4656
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 496/500
235/235 [==============================] - 3s 11ms/step - loss: 1.5637 - accuracy: 0.4533 - val_loss: 1.5494 - val_accuracy: 0.4655
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 497/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5636 - accuracy: 0.4532 - val_loss: 1.5493 - val_accuracy: 0.4654
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5636 - accuracy: 0.4534 - val_loss: 1.5493 - val_accuracy: 0.4653
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 499/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5636 - accuracy: 0.4534 - val_loss: 1.5493 - val_accuracy: 0.4652
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 500/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5635 - accuracy: 0.4534 - val_loss: 1.5492 - val_accuracy: 0.4651
[-0. -0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 1/200
235/235 [==============================] - 5s 16ms/step - loss: 2.1563 - accuracy: 0.9278 - val_loss: 1.5487 - val_accuracy: 0.7609
Epoch 2/200
235/235 [==============================] - 4s 15ms/step - loss: 0.4329 - accuracy: 0.9586 - val_loss: 0.4927 - val_accuracy: 0.9365
Epoch 3/200
235/235 [==============================] - 4s 15ms/step - loss: 0.3091 - accuracy: 0.9628 - val_loss: 0.3538 - val_accuracy: 0.9429
Epoch 4/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2810 - accuracy: 0.9647 - val_loss: 0.3075 - val_accuracy: 0.9512
Epoch 5/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2640 - accuracy: 0.9672 - val_loss: 0.2891 - val_accuracy: 0.9564
Epoch 6/200
235/235 [==============================] - 4s 16ms/step - loss: 0.2530 - accuracy: 0.9683 - val_loss: 0.3675 - val_accuracy: 0.9282- loss: 0.2
Epoch 7/200
235/235 [==============================] - 3s 15ms/step - loss: 0.2438 - accuracy: 0.9685 - val_loss: 0.3105 - val_accuracy: 0.9434
Epoch 8/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2342 - accuracy: 0.9695 - val_loss: 0.2781 - val_accuracy: 0.9518
Epoch 9/200
235/235 [==============================] - 4s 16ms/step - loss: 0.2314 - accuracy: 0.9697 - val_loss: 0.2811 - val_accuracy: 0.9516
Epoch 10/200
235/235 [==============================] - 4s 16ms/step - loss: 0.2200 - accuracy: 0.9702 - val_loss: 0.2590 - val_accuracy: 0.9568
Epoch 11/200
235/235 [==============================] - 4s 17ms/step - loss: 0.2146 - accuracy: 0.9713 - val_loss: 0.2549 - val_accuracy: 0.9554
Epoch 12/200
235/235 [==============================] - 4s 18ms/step - loss: 0.2103 - accuracy: 0.9712 - val_loss: 0.2408 - val_accuracy: 0.9619
Epoch 13/200
235/235 [==============================] - 4s 17ms/step - loss: 0.2058 - accuracy: 0.9715 - val_loss: 0.2518 - val_accuracy: 0.9568
Epoch 14/200
235/235 [==============================] - 4s 19ms/step - loss: 0.2006 - accuracy: 0.9728 - val_loss: 0.2371 - val_accuracy: 0.9607
Epoch 15/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1993 - accuracy: 0.9726 - val_loss: 0.2331 - val_accuracy: 0.9623
Epoch 16/200
235/235 [==============================] - 4s 16ms/step - loss: 0.2019 - accuracy: 0.9709 - val_loss: 0.2733 - val_accuracy: 0.9453
Epoch 17/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1963 - accuracy: 0.9721 - val_loss: 0.2418 - val_accuracy: 0.9584
Epoch 18/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1880 - accuracy: 0.9740 - val_loss: 0.2775 - val_accuracy: 0.9437
Epoch 19/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1914 - accuracy: 0.9728 - val_loss: 0.2290 - val_accuracy: 0.9606
Epoch 20/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1885 - accuracy: 0.9734 - val_loss: 0.2374 - val_accuracy: 0.9568
Epoch 21/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1857 - accuracy: 0.9728 - val_loss: 0.2321 - val_accuracy: 0.9580
Epoch 22/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1855 - accuracy: 0.9732 - val_loss: 0.2163 - val_accuracy: 0.9651
Epoch 23/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1825 - accuracy: 0.9736 - val_loss: 0.2319 - val_accuracy: 0.9600
Epoch 24/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1786 - accuracy: 0.9739 - val_loss: 0.2428 - val_accuracy: 0.9542
Epoch 25/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1804 - accuracy: 0.9736 - val_loss: 0.2278 - val_accuracy: 0.9598
Epoch 26/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1770 - accuracy: 0.9733 - val_loss: 0.2115 - val_accuracy: 0.9639
Epoch 27/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1736 - accuracy: 0.9747 - val_loss: 0.2142 - val_accuracy: 0.9624
Epoch 28/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1702 - accuracy: 0.9748 - val_loss: 0.1980 - val_accuracy: 0.9678
Epoch 29/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1720 - accuracy: 0.9740 - val_loss: 0.2170 - val_accuracy: 0.9591
Epoch 30/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1715 - accuracy: 0.9741 - val_loss: 0.2142 - val_accuracy: 0.9614
Epoch 31/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1695 - accuracy: 0.9752 - val_loss: 0.2019 - val_accuracy: 0.9662
Epoch 32/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1690 - accuracy: 0.9749 - val_loss: 0.2369 - val_accuracy: 0.9528
Epoch 33/200
235/235 [==============================] - 5s 22ms/step - loss: 0.1675 - accuracy: 0.9750 - val_loss: 0.2308 - val_accuracy: 0.9551
Epoch 34/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1663 - accuracy: 0.9750 - val_loss: 0.1973 - val_accuracy: 0.9653
Epoch 35/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1641 - accuracy: 0.9756 - val_loss: 0.2213 - val_accuracy: 0.9591
Epoch 36/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1673 - accuracy: 0.9751 - val_loss: 0.2254 - val_accuracy: 0.9565
Epoch 37/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1642 - accuracy: 0.9754 - val_loss: 0.2350 - val_accuracy: 0.9533
Epoch 38/200
235/235 [==============================] - 3s 12ms/step - loss: 0.1634 - accuracy: 0.9757 - val_loss: 0.2080 - val_accuracy: 0.9637
Epoch 39/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1659 - accuracy: 0.9750 - val_loss: 0.2270 - val_accuracy: 0.9589
Epoch 40/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1649 - accuracy: 0.9749 - val_loss: 0.2151 - val_accuracy: 0.9628
Epoch 41/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1609 - accuracy: 0.9759 - val_loss: 0.2335 - val_accuracy: 0.9563
Epoch 42/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1632 - accuracy: 0.9752 - val_loss: 0.2220 - val_accuracy: 0.9582
Epoch 43/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1631 - accuracy: 0.9755 - val_loss: 0.2194 - val_accuracy: 0.9640
Epoch 44/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1628 - accuracy: 0.9752 - val_loss: 0.2003 - val_accuracy: 0.9657
Epoch 45/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1605 - accuracy: 0.9758 - val_loss: 0.2410 - val_accuracy: 0.9534
Epoch 46/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1623 - accuracy: 0.9754 - val_loss: 0.2012 - val_accuracy: 0.9660
Epoch 47/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1600 - accuracy: 0.9763 - val_loss: 0.2303 - val_accuracy: 0.9549
Epoch 48/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1569 - accuracy: 0.9774 - val_loss: 0.2149 - val_accuracy: 0.9629
Epoch 49/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1590 - accuracy: 0.9762 - val_loss: 0.2601 - val_accuracy: 0.9483
Epoch 50/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1613 - accuracy: 0.9761 - val_loss: 0.2183 - val_accuracy: 0.9584
Epoch 51/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1563 - accuracy: 0.9771 - val_loss: 0.2606 - val_accuracy: 0.9436
Epoch 52/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1585 - accuracy: 0.9760 - val_loss: 0.2201 - val_accuracy: 0.9570
Epoch 53/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1625 - accuracy: 0.9756 - val_loss: 0.2679 - val_accuracy: 0.9459
Epoch 54/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1584 - accuracy: 0.9765 - val_loss: 0.2247 - val_accuracy: 0.9588
Epoch 55/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1611 - accuracy: 0.9761 - val_loss: 0.2229 - val_accuracy: 0.9591
Epoch 56/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1568 - accuracy: 0.9769 - val_loss: 0.2196 - val_accuracy: 0.9608
Epoch 57/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1591 - accuracy: 0.9763 - val_loss: 0.2238 - val_accuracy: 0.9583
Epoch 58/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1576 - accuracy: 0.9768 - val_loss: 0.2122 - val_accuracy: 0.9595
Epoch 59/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1566 - accuracy: 0.9763 - val_loss: 0.2427 - val_accuracy: 0.9515
Epoch 60/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1599 - accuracy: 0.9761 - val_loss: 0.2005 - val_accuracy: 0.9634
Epoch 61/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1560 - accuracy: 0.9764 - val_loss: 0.1895 - val_accuracy: 0.9668
Epoch 62/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1552 - accuracy: 0.9769 - val_loss: 0.2209 - val_accuracy: 0.9567
Epoch 63/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1578 - accuracy: 0.9764 - val_loss: 0.2241 - val_accuracy: 0.9580
Epoch 64/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1550 - accuracy: 0.9768 - val_loss: 0.2036 - val_accuracy: 0.9636
Epoch 65/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1573 - accuracy: 0.9761 - val_loss: 0.2113 - val_accuracy: 0.9605 
Epoch 66/200
235/235 [==============================] - 4s 17ms/step - loss: 0.1573 - accuracy: 0.9768 - val_loss: 0.2026 - val_accuracy: 0.9647
Epoch 67/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1558 - accuracy: 0.9768 - val_loss: 0.2148 - val_accuracy: 0.9592
Epoch 68/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1535 - accuracy: 0.9780 - val_loss: 0.2248 - val_accuracy: 0.9557
Epoch 69/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1528 - accuracy: 0.9778 - val_loss: 0.2044 - val_accuracy: 0.9640
Epoch 70/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1529 - accuracy: 0.9772 - val_loss: 0.2182 - val_accuracy: 0.9576
Epoch 71/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1526 - accuracy: 0.9772 - val_loss: 0.1934 - val_accuracy: 0.9653
Epoch 72/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1525 - accuracy: 0.9770 - val_loss: 0.2119 - val_accuracy: 0.9581
Epoch 73/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1567 - accuracy: 0.9762 - val_loss: 0.2232 - val_accuracy: 0.9585
Epoch 74/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1537 - accuracy: 0.9779 - val_loss: 0.2287 - val_accuracy: 0.9562
Epoch 75/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9771 - val_loss: 0.2028 - val_accuracy: 0.9621
Epoch 76/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1546 - accuracy: 0.9769 - val_loss: 0.1987 - val_accuracy: 0.9645
Epoch 77/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1516 - accuracy: 0.9779 - val_loss: 0.2166 - val_accuracy: 0.9571
Epoch 78/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1536 - accuracy: 0.9774 - val_loss: 0.2286 - val_accuracy: 0.9528
Epoch 79/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1499 - accuracy: 0.9778 - val_loss: 0.2278 - val_accuracy: 0.9555
Epoch 80/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1559 - accuracy: 0.9760 - val_loss: 0.2686 - val_accuracy: 0.9455
Epoch 81/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1535 - accuracy: 0.9768 - val_loss: 0.2449 - val_accuracy: 0.9506
Epoch 82/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1518 - accuracy: 0.9775 - val_loss: 0.2028 - val_accuracy: 0.9631
Epoch 83/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1528 - accuracy: 0.9772 - val_loss: 0.2105 - val_accuracy: 0.9589
Epoch 84/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1531 - accuracy: 0.9769 - val_loss: 0.2109 - val_accuracy: 0.9613
Epoch 85/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1503 - accuracy: 0.9773 - val_loss: 0.1985 - val_accuracy: 0.9638
Epoch 86/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1508 - accuracy: 0.9776 - val_loss: 0.2155 - val_accuracy: 0.9585
Epoch 87/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1529 - accuracy: 0.9765 - val_loss: 0.2003 - val_accuracy: 0.9646
Epoch 88/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1498 - accuracy: 0.9777 - val_loss: 0.2252 - val_accuracy: 0.9548
Epoch 89/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1521 - accuracy: 0.9771 - val_loss: 0.2159 - val_accuracy: 0.9585
Epoch 90/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1523 - accuracy: 0.9765 - val_loss: 0.2400 - val_accuracy: 0.9491
Epoch 91/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1505 - accuracy: 0.9778 - val_loss: 0.2337 - val_accuracy: 0.9546
Epoch 92/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1510 - accuracy: 0.9773 - val_loss: 0.1999 - val_accuracy: 0.9621
Epoch 93/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1492 - accuracy: 0.9773 - val_loss: 0.2199 - val_accuracy: 0.9588
Epoch 94/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1534 - accuracy: 0.9768 - val_loss: 0.2206 - val_accuracy: 0.9588
Epoch 95/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1509 - accuracy: 0.9771 - val_loss: 0.2080 - val_accuracy: 0.9590
Epoch 96/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1478 - accuracy: 0.9779 - val_loss: 0.2137 - val_accuracy: 0.9602
Epoch 97/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1502 - accuracy: 0.9766 - val_loss: 0.2033 - val_accuracy: 0.9625
Epoch 98/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1510 - accuracy: 0.9773 - val_loss: 0.2154 - val_accuracy: 0.9585
Epoch 99/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1487 - accuracy: 0.9777 - val_loss: 0.2229 - val_accuracy: 0.9546
Epoch 100/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1495 - accuracy: 0.9773 - val_loss: 0.2044 - val_accuracy: 0.9608
Epoch 101/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1514 - accuracy: 0.9773 - val_loss: 0.2297 - val_accuracy: 0.9527
Epoch 102/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1540 - accuracy: 0.9770 - val_loss: 0.1960 - val_accuracy: 0.9642
Epoch 103/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1515 - accuracy: 0.9769 - val_loss: 0.2105 - val_accuracy: 0.9600
Epoch 104/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1482 - accuracy: 0.9775 - val_loss: 0.1968 - val_accuracy: 0.9647
Epoch 105/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1508 - accuracy: 0.9769 - val_loss: 0.2294 - val_accuracy: 0.9564
Epoch 106/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1474 - accuracy: 0.9777 - val_loss: 0.2026 - val_accuracy: 0.9621
Epoch 107/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9778 - val_loss: 0.2028 - val_accuracy: 0.9614
Epoch 108/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1490 - accuracy: 0.9771 - val_loss: 0.2248 - val_accuracy: 0.9555
Epoch 109/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1502 - accuracy: 0.9768 - val_loss: 0.2030 - val_accuracy: 0.9640
Epoch 110/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1458 - accuracy: 0.9781 - val_loss: 0.2152 - val_accuracy: 0.9562
Epoch 111/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1472 - accuracy: 0.9779 - val_loss: 0.2072 - val_accuracy: 0.9607
Epoch 112/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1538 - accuracy: 0.9758 - val_loss: 0.2200 - val_accuracy: 0.9563
Epoch 113/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1467 - accuracy: 0.9786 - val_loss: 0.1828 - val_accuracy: 0.9676
Epoch 114/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1483 - accuracy: 0.9774 - val_loss: 0.1973 - val_accuracy: 0.9617
Epoch 115/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1508 - accuracy: 0.9766 - val_loss: 0.1906 - val_accuracy: 0.9655
Epoch 116/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9777 - val_loss: 0.1844 - val_accuracy: 0.9685
Epoch 117/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1491 - accuracy: 0.9770 - val_loss: 0.2164 - val_accuracy: 0.9576
Epoch 118/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1463 - accuracy: 0.9783 - val_loss: 0.2037 - val_accuracy: 0.9609
Epoch 119/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1514 - accuracy: 0.9766 - val_loss: 0.2229 - val_accuracy: 0.9558
Epoch 120/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1450 - accuracy: 0.9785 - val_loss: 0.2491 - val_accuracy: 0.9474
Epoch 121/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1490 - accuracy: 0.9768 - val_loss: 0.2173 - val_accuracy: 0.9577
Epoch 122/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1488 - accuracy: 0.9774 - val_loss: 0.2717 - val_accuracy: 0.9434
Epoch 123/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9774 - val_loss: 0.1853 - val_accuracy: 0.9661
Epoch 124/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1504 - accuracy: 0.9764 - val_loss: 0.2088 - val_accuracy: 0.9593
Epoch 125/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1461 - accuracy: 0.9778 - val_loss: 0.1827 - val_accuracy: 0.9686
Epoch 126/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1464 - accuracy: 0.9775 - val_loss: 0.2138 - val_accuracy: 0.9591
Epoch 127/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1451 - accuracy: 0.9776 - val_loss: 0.1953 - val_accuracy: 0.9636
Epoch 128/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1498 - accuracy: 0.9770 - val_loss: 0.2299 - val_accuracy: 0.9550
Epoch 129/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1503 - accuracy: 0.9767 - val_loss: 0.2189 - val_accuracy: 0.9567
Epoch 130/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9782 - val_loss: 0.2325 - val_accuracy: 0.9503
Epoch 131/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1428 - accuracy: 0.9785 - val_loss: 0.2311 - val_accuracy: 0.9528
Epoch 132/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1475 - accuracy: 0.9776 - val_loss: 0.1975 - val_accuracy: 0.9629
Epoch 133/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9780 - val_loss: 0.2030 - val_accuracy: 0.9609
Epoch 134/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1454 - accuracy: 0.9780 - val_loss: 0.1966 - val_accuracy: 0.9633
Epoch 135/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1455 - accuracy: 0.9785 - val_loss: 0.2031 - val_accuracy: 0.9619
Epoch 136/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1456 - accuracy: 0.9776 - val_loss: 0.2054 - val_accuracy: 0.9633
Epoch 137/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1462 - accuracy: 0.9781 - val_loss: 0.2271 - val_accuracy: 0.9547
Epoch 138/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1463 - accuracy: 0.9781 - val_loss: 0.2152 - val_accuracy: 0.9590
Epoch 139/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1443 - accuracy: 0.9785 - val_loss: 0.2238 - val_accuracy: 0.9544
Epoch 140/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1427 - accuracy: 0.9788 - val_loss: 0.2090 - val_accuracy: 0.9604
Epoch 141/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1491 - accuracy: 0.9768 - val_loss: 0.2307 - val_accuracy: 0.9530
Epoch 142/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1438 - accuracy: 0.9790 - val_loss: 0.2075 - val_accuracy: 0.9602
Epoch 143/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1439 - accuracy: 0.9781 - val_loss: 0.2400 - val_accuracy: 0.9511
Epoch 144/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1470 - accuracy: 0.9787 - val_loss: 0.1945 - val_accuracy: 0.9650
Epoch 145/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1413 - accuracy: 0.9792 - val_loss: 0.2201 - val_accuracy: 0.9544
Epoch 146/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1456 - accuracy: 0.9779 - val_loss: 0.2350 - val_accuracy: 0.9518
Epoch 147/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1468 - accuracy: 0.9776 - val_loss: 0.2078 - val_accuracy: 0.9599
Epoch 148/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1427 - accuracy: 0.9790 - val_loss: 0.1993 - val_accuracy: 0.9621
Epoch 149/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1466 - accuracy: 0.9779 - val_loss: 0.2055 - val_accuracy: 0.9611
Epoch 150/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1406 - accuracy: 0.9789 - val_loss: 0.2068 - val_accuracy: 0.9591
Epoch 151/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1466 - accuracy: 0.9764 - val_loss: 0.2143 - val_accuracy: 0.9590
Epoch 152/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1438 - accuracy: 0.9783 - val_loss: 0.1907 - val_accuracy: 0.9648
Epoch 153/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1444 - accuracy: 0.9782 - val_loss: 0.2316 - val_accuracy: 0.9544
Epoch 154/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1420 - accuracy: 0.9786 - val_loss: 0.1986 - val_accuracy: 0.9614
Epoch 155/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1430 - accuracy: 0.9785 - val_loss: 0.1909 - val_accuracy: 0.9662
Epoch 156/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1465 - accuracy: 0.9772 - val_loss: 0.2500 - val_accuracy: 0.9467
Epoch 157/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1416 - accuracy: 0.9782 - val_loss: 0.1955 - val_accuracy: 0.9638
Epoch 158/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1462 - accuracy: 0.9775 - val_loss: 0.2217 - val_accuracy: 0.9563
Epoch 159/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1418 - accuracy: 0.9788 - val_loss: 0.1908 - val_accuracy: 0.9654
Epoch 160/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1378 - accuracy: 0.9799 - val_loss: 0.2019 - val_accuracy: 0.9593
Epoch 161/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1464 - accuracy: 0.9776 - val_loss: 0.2128 - val_accuracy: 0.9586
Epoch 162/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1442 - accuracy: 0.9775 - val_loss: 0.2253 - val_accuracy: 0.9538
Epoch 163/200
235/235 [==============================] - 4s 16ms/step - loss: 0.1457 - accuracy: 0.9781 - val_loss: 0.2320 - val_accuracy: 0.9513
Epoch 164/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1430 - accuracy: 0.9783 - val_loss: 0.1913 - val_accuracy: 0.9646
Epoch 165/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1390 - accuracy: 0.9789 - val_loss: 0.1951 - val_accuracy: 0.9643
Epoch 166/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1440 - accuracy: 0.9783 - val_loss: 0.1924 - val_accuracy: 0.9632
Epoch 167/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1387 - accuracy: 0.9797 - val_loss: 0.2044 - val_accuracy: 0.9596
Epoch 168/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1393 - accuracy: 0.9791 - val_loss: 0.1966 - val_accuracy: 0.9627
Epoch 169/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1458 - accuracy: 0.9773 - val_loss: 0.1845 - val_accuracy: 0.9668
Epoch 170/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1391 - accuracy: 0.9789 - val_loss: 0.1934 - val_accuracy: 0.9645
Epoch 171/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1413 - accuracy: 0.9782 - val_loss: 0.2239 - val_accuracy: 0.9538
Epoch 172/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1429 - accuracy: 0.9779 - val_loss: 0.2463 - val_accuracy: 0.9495
Epoch 173/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1482 - accuracy: 0.9773 - val_loss: 0.2001 - val_accuracy: 0.9615
Epoch 174/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1394 - accuracy: 0.9793 - val_loss: 0.2169 - val_accuracy: 0.9569
Epoch 175/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1421 - accuracy: 0.9784 - val_loss: 0.1939 - val_accuracy: 0.9651
Epoch 176/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1417 - accuracy: 0.9789 - val_loss: 0.2107 - val_accuracy: 0.9580
Epoch 177/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1388 - accuracy: 0.9794 - val_loss: 0.2081 - val_accuracy: 0.9594
Epoch 178/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1379 - accuracy: 0.9796 - val_loss: 0.2102 - val_accuracy: 0.9585
Epoch 179/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1421 - accuracy: 0.9784 - val_loss: 0.2194 - val_accuracy: 0.9573
Epoch 180/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1412 - accuracy: 0.9790 - val_loss: 0.2069 - val_accuracy: 0.9586
Epoch 181/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1423 - accuracy: 0.9785 - val_loss: 0.2188 - val_accuracy: 0.9559
Epoch 182/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1426 - accuracy: 0.9787 - val_loss: 0.1921 - val_accuracy: 0.9652
Epoch 183/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1451 - accuracy: 0.9771 - val_loss: 0.1865 - val_accuracy: 0.9648
Epoch 184/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1410 - accuracy: 0.9786 - val_loss: 0.1943 - val_accuracy: 0.9638
Epoch 185/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1421 - accuracy: 0.9775 - val_loss: 0.2207 - val_accuracy: 0.9550
Epoch 186/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1399 - accuracy: 0.9789 - val_loss: 0.2148 - val_accuracy: 0.9576
Epoch 187/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1436 - accuracy: 0.9781 - val_loss: 0.2055 - val_accuracy: 0.9592
Epoch 188/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1379 - accuracy: 0.9790 - val_loss: 0.2217 - val_accuracy: 0.9554
Epoch 189/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1392 - accuracy: 0.9787 - val_loss: 0.2174 - val_accuracy: 0.9542
Epoch 190/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9786 - val_loss: 0.2188 - val_accuracy: 0.9563
Epoch 191/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1413 - accuracy: 0.9790 - val_loss: 0.1833 - val_accuracy: 0.9650
Epoch 192/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1422 - accuracy: 0.9783 - val_loss: 0.2730 - val_accuracy: 0.9364
Epoch 193/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1431 - accuracy: 0.9776 - val_loss: 0.1882 - val_accuracy: 0.9661
Epoch 194/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1436 - accuracy: 0.9790 - val_loss: 0.2011 - val_accuracy: 0.9600
Epoch 195/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1366 - accuracy: 0.9808 - val_loss: 0.1986 - val_accuracy: 0.9623
Epoch 196/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1402 - accuracy: 0.9794 - val_loss: 0.2006 - val_accuracy: 0.9609
Epoch 197/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1420 - accuracy: 0.9781 - val_loss: 0.1868 - val_accuracy: 0.9666
Epoch 198/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1408 - accuracy: 0.9789 - val_loss: 0.1953 - val_accuracy: 0.9645
Epoch 199/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1428 - accuracy: 0.9775 - val_loss: 0.1832 - val_accuracy: 0.9667
Epoch 200/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1368 - accuracy: 0.9798 - val_loss: 0.1996 - val_accuracy: 0.9619
Epoch 1/200
235/235 [==============================] - 4s 15ms/step - loss: 0.2417 - accuracy: 0.9285 - val_loss: 0.2194 - val_accuracy: 0.9534
Epoch 2/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0870 - accuracy: 0.9752 - val_loss: 0.1039 - val_accuracy: 0.9655
Epoch 3/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0505 - accuracy: 0.9862 - val_loss: 0.0899 - val_accuracy: 0.9719
Epoch 4/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0314 - accuracy: 0.9920 - val_loss: 0.0835 - val_accuracy: 0.9739
Epoch 5/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0200 - accuracy: 0.9951 - val_loss: 0.0901 - val_accuracy: 0.9731
Epoch 6/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0144 - accuracy: 0.9963 - val_loss: 0.0916 - val_accuracy: 0.9724
Epoch 7/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0132 - accuracy: 0.9969 - val_loss: 0.0872 - val_accuracy: 0.9739
Epoch 8/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0106 - accuracy: 0.9971 - val_loss: 0.0873 - val_accuracy: 0.9762
Epoch 9/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 0.0869 - val_accuracy: 0.9765
Epoch 10/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0094 - accuracy: 0.9974 - val_loss: 0.0957 - val_accuracy: 0.9776
Epoch 11/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0074 - accuracy: 0.9980 - val_loss: 0.1009 - val_accuracy: 0.9750
Epoch 12/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0081 - accuracy: 0.9974 - val_loss: 0.0934 - val_accuracy: 0.9763
Epoch 13/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9973 - val_loss: 0.0946 - val_accuracy: 0.9787
Epoch 14/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.0909 - val_accuracy: 0.9786
Epoch 15/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0063 - accuracy: 0.9980 - val_loss: 0.0923 - val_accuracy: 0.9780
Epoch 16/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9978 - val_loss: 0.0932 - val_accuracy: 0.9777
Epoch 17/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0066 - accuracy: 0.9980 - val_loss: 0.0963 - val_accuracy: 0.9780
Epoch 18/200
235/235 [==============================] - 4s 16ms/step - loss: 0.0065 - accuracy: 0.9980 - val_loss: 0.1022 - val_accuracy: 0.9765
Epoch 19/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0071 - accuracy: 0.9980 - val_loss: 0.0913 - val_accuracy: 0.9789
Epoch 20/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.1048 - val_accuracy: 0.9758
Epoch 21/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9983 - val_loss: 0.0845 - val_accuracy: 0.9808
Epoch 22/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0047 - accuracy: 0.9986 - val_loss: 0.1005 - val_accuracy: 0.9783
Epoch 23/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.0781 - val_accuracy: 0.9814
Epoch 24/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0840 - val_accuracy: 0.9803
Epoch 25/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 0.0849 - val_accuracy: 0.9818
Epoch 26/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.1142 - val_accuracy: 0.9751
Epoch 27/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0088 - accuracy: 0.9972 - val_loss: 0.1148 - val_accuracy: 0.9734
Epoch 28/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0127 - accuracy: 0.9953 - val_loss: 0.1084 - val_accuracy: 0.9765
Epoch 29/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0061 - accuracy: 0.9979 - val_loss: 0.0880 - val_accuracy: 0.9806
Epoch 30/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0809 - val_accuracy: 0.9825
Epoch 31/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 0.0876 - val_accuracy: 0.9829
Epoch 32/200
235/235 [==============================] - 3s 15ms/step - loss: 9.3206e-04 - accuracy: 0.9998 - val_loss: 0.0826 - val_accuracy: 0.9834
Epoch 33/200
235/235 [==============================] - 3s 15ms/step - loss: 3.5433e-04 - accuracy: 0.9999 - val_loss: 0.0838 - val_accuracy: 0.9832
Epoch 34/200
235/235 [==============================] - 3s 15ms/step - loss: 3.1421e-04 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9837
Epoch 35/200
235/235 [==============================] - 3s 15ms/step - loss: 1.1686e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9840
Epoch 36/200
235/235 [==============================] - 3s 15ms/step - loss: 8.4656e-05 - accuracy: 1.0000 - val_loss: 0.0777 - val_accuracy: 0.9843
Epoch 37/200
235/235 [==============================] - 3s 15ms/step - loss: 6.8309e-05 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9842
Epoch 38/200
235/235 [==============================] - 3s 15ms/step - loss: 6.3141e-05 - accuracy: 1.0000 - val_loss: 0.0783 - val_accuracy: 0.9846
Epoch 39/200
235/235 [==============================] - 3s 14ms/step - loss: 9.6400e-05 - accuracy: 1.0000 - val_loss: 0.0827 - val_accuracy: 0.9839
Epoch 40/200
235/235 [==============================] - 3s 13ms/step - loss: 6.9066e-04 - accuracy: 0.9998 - val_loss: 0.0954 - val_accuracy: 0.9823
Epoch 41/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0327 - accuracy: 0.9897 - val_loss: 0.1552 - val_accuracy: 0.9666
Epoch 42/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0150 - accuracy: 0.9949 - val_loss: 0.0836 - val_accuracy: 0.9812
Epoch 43/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.0782 - val_accuracy: 0.9821
Epoch 44/200
235/235 [==============================] - 3s 12ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0755 - val_accuracy: 0.9837
Epoch 45/200
235/235 [==============================] - 3s 15ms/step - loss: 4.7189e-04 - accuracy: 1.0000 - val_loss: 0.0732 - val_accuracy: 0.9837
Epoch 46/200
235/235 [==============================] - 3s 15ms/step - loss: 2.5655e-04 - accuracy: 1.0000 - val_loss: 0.0730 - val_accuracy: 0.9840
Epoch 47/200
235/235 [==============================] - 3s 14ms/step - loss: 2.4572e-04 - accuracy: 1.0000 - val_loss: 0.0724 - val_accuracy: 0.9839
Epoch 48/200
235/235 [==============================] - 3s 14ms/step - loss: 1.9183e-04 - accuracy: 1.0000 - val_loss: 0.0719 - val_accuracy: 0.9845
Epoch 49/200
235/235 [==============================] - 3s 14ms/step - loss: 1.2318e-04 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9843
Epoch 50/200
235/235 [==============================] - 3s 15ms/step - loss: 1.0600e-04 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9844
Epoch 51/200
235/235 [==============================] - 3s 15ms/step - loss: 8.4569e-05 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9846
Epoch 52/200
235/235 [==============================] - 3s 15ms/step - loss: 8.0213e-05 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9848
Epoch 53/200
235/235 [==============================] - 3s 14ms/step - loss: 6.7946e-05 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9848
Epoch 54/200
235/235 [==============================] - 3s 15ms/step - loss: 5.7373e-05 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9845
Epoch 55/200
235/235 [==============================] - 3s 15ms/step - loss: 5.4949e-05 - accuracy: 1.0000 - val_loss: 0.0743 - val_accuracy: 0.9846
Epoch 56/200
235/235 [==============================] - 3s 15ms/step - loss: 5.1014e-05 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9838
Epoch 57/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0140 - accuracy: 0.9958 - val_loss: 0.1949 - val_accuracy: 0.9594
Epoch 58/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0177 - accuracy: 0.9944 - val_loss: 0.0776 - val_accuracy: 0.9818
Epoch 59/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.0703 - val_accuracy: 0.9822
Epoch 60/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0691 - val_accuracy: 0.9841
Epoch 61/200
235/235 [==============================] - 3s 14ms/step - loss: 3.9402e-04 - accuracy: 1.0000 - val_loss: 0.0687 - val_accuracy: 0.9843
Epoch 62/200
235/235 [==============================] - 3s 14ms/step - loss: 2.3301e-04 - accuracy: 1.0000 - val_loss: 0.0683 - val_accuracy: 0.9841
Epoch 63/200
235/235 [==============================] - 3s 14ms/step - loss: 1.5572e-04 - accuracy: 1.0000 - val_loss: 0.0686 - val_accuracy: 0.9839
Epoch 64/200
235/235 [==============================] - 3s 14ms/step - loss: 1.2315e-04 - accuracy: 1.0000 - val_loss: 0.0696 - val_accuracy: 0.9842
Epoch 65/200
235/235 [==============================] - 3s 14ms/step - loss: 1.5173e-04 - accuracy: 1.0000 - val_loss: 0.0730 - val_accuracy: 0.9840
Epoch 66/200
235/235 [==============================] - 3s 14ms/step - loss: 7.6259e-04 - accuracy: 0.9998 - val_loss: 0.0924 - val_accuracy: 0.9793
Epoch 67/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.1030 - val_accuracy: 0.9786
Epoch 68/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9977 - val_loss: 0.0930 - val_accuracy: 0.9801
Epoch 69/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.0801 - val_accuracy: 0.9822
Epoch 70/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0832 - val_accuracy: 0.9819
Epoch 71/200
235/235 [==============================] - 3s 14ms/step - loss: 7.3600e-04 - accuracy: 0.9998 - val_loss: 0.0723 - val_accuracy: 0.9850
Epoch 72/200
235/235 [==============================] - 3s 14ms/step - loss: 4.1038e-04 - accuracy: 0.9999 - val_loss: 0.0739 - val_accuracy: 0.9849
Epoch 73/200
235/235 [==============================] - 3s 14ms/step - loss: 1.2531e-04 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9851
Epoch 74/200
235/235 [==============================] - 3s 14ms/step - loss: 1.1531e-04 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9853
Epoch 75/200
235/235 [==============================] - 3s 14ms/step - loss: 7.8246e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9848
Epoch 76/200
235/235 [==============================] - 3s 14ms/step - loss: 1.0678e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9842
Epoch 77/200
235/235 [==============================] - 4s 15ms/step - loss: 1.5456e-04 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 0.9848
Epoch 78/200
235/235 [==============================] - 3s 15ms/step - loss: 1.6944e-04 - accuracy: 0.9999 - val_loss: 0.0795 - val_accuracy: 0.9846
Epoch 79/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9989 - val_loss: 0.1358 - val_accuracy: 0.9715
Epoch 80/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0108 - accuracy: 0.9965 - val_loss: 0.0961 - val_accuracy: 0.9821
Epoch 81/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9987 - val_loss: 0.0927 - val_accuracy: 0.9824
Epoch 82/200
235/235 [==============================] - 3s 14ms/step - loss: 9.8481e-04 - accuracy: 0.9997 - val_loss: 0.0861 - val_accuracy: 0.9831
Epoch 83/200
235/235 [==============================] - 3s 14ms/step - loss: 5.7218e-04 - accuracy: 0.9998 - val_loss: 0.0806 - val_accuracy: 0.9851
Epoch 84/200
235/235 [==============================] - 3s 14ms/step - loss: 3.7279e-04 - accuracy: 1.0000 - val_loss: 0.0806 - val_accuracy: 0.9838
Epoch 85/200
235/235 [==============================] - 3s 14ms/step - loss: 6.1142e-04 - accuracy: 0.9998 - val_loss: 0.0798 - val_accuracy: 0.9845
Epoch 86/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0894 - val_accuracy: 0.9833
Epoch 87/200
235/235 [==============================] - 3s 15ms/step - loss: 9.4859e-04 - accuracy: 0.9998 - val_loss: 0.0846 - val_accuracy: 0.9845
Epoch 88/200
235/235 [==============================] - 3s 14ms/step - loss: 3.0982e-04 - accuracy: 0.9999 - val_loss: 0.0834 - val_accuracy: 0.9849
Epoch 89/200
235/235 [==============================] - 3s 15ms/step - loss: 2.8228e-04 - accuracy: 0.9999 - val_loss: 0.0851 - val_accuracy: 0.9844
Epoch 90/200
235/235 [==============================] - 3s 14ms/step - loss: 2.3257e-04 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9840
Epoch 91/200
235/235 [==============================] - 3s 15ms/step - loss: 1.7209e-04 - accuracy: 0.9999 - val_loss: 0.0867 - val_accuracy: 0.9845
Epoch 92/200
235/235 [==============================] - 3s 15ms/step - loss: 1.8568e-04 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9834
Epoch 93/200
235/235 [==============================] - 4s 15ms/step - loss: 3.7085e-04 - accuracy: 0.9999 - val_loss: 0.0895 - val_accuracy: 0.9831
Epoch 94/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.1246 - val_accuracy: 0.9768
Epoch 95/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.1030 - val_accuracy: 0.9826
Epoch 96/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.0932 - val_accuracy: 0.9835
Epoch 97/200
235/235 [==============================] - 3s 15ms/step - loss: 8.5810e-04 - accuracy: 0.9998 - val_loss: 0.0910 - val_accuracy: 0.9842
Epoch 98/200
235/235 [==============================] - 3s 15ms/step - loss: 3.7517e-04 - accuracy: 0.9999 - val_loss: 0.0883 - val_accuracy: 0.9853
Epoch 99/200
235/235 [==============================] - 3s 14ms/step - loss: 1.8331e-04 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9853
Epoch 100/200
235/235 [==============================] - 3s 15ms/step - loss: 7.3496e-05 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9852
Epoch 101/200
235/235 [==============================] - 3s 14ms/step - loss: 8.6417e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9855
Epoch 102/200
235/235 [==============================] - 3s 14ms/step - loss: 5.0333e-05 - accuracy: 1.0000 - val_loss: 0.0872 - val_accuracy: 0.9858
Epoch 103/200
235/235 [==============================] - 3s 14ms/step - loss: 3.7029e-05 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9857
Epoch 104/200
235/235 [==============================] - 3s 14ms/step - loss: 1.2491e-04 - accuracy: 0.9999 - val_loss: 0.0928 - val_accuracy: 0.9836
Epoch 105/200
235/235 [==============================] - 3s 14ms/step - loss: 1.8845e-04 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9846
Epoch 106/200
235/235 [==============================] - 3s 15ms/step - loss: 6.5816e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9844
Epoch 107/200
235/235 [==============================] - 3s 15ms/step - loss: 3.5948e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9852
Epoch 108/200
235/235 [==============================] - 3s 14ms/step - loss: 2.7824e-05 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9850
Epoch 109/200
235/235 [==============================] - 3s 14ms/step - loss: 3.5503e-04 - accuracy: 0.9999 - val_loss: 0.0983 - val_accuracy: 0.9842
Epoch 110/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0105 - accuracy: 0.9966 - val_loss: 0.1224 - val_accuracy: 0.9768
Epoch 111/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.0979 - val_accuracy: 0.9801
Epoch 112/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0892 - val_accuracy: 0.9821
Epoch 113/200
235/235 [==============================] - 3s 14ms/step - loss: 6.4947e-04 - accuracy: 0.9999 - val_loss: 0.0912 - val_accuracy: 0.9832
Epoch 114/200
235/235 [==============================] - 3s 15ms/step - loss: 1.7188e-04 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9832
Epoch 115/200
235/235 [==============================] - 4s 15ms/step - loss: 2.6432e-04 - accuracy: 0.9999 - val_loss: 0.0851 - val_accuracy: 0.9844
Epoch 116/200
235/235 [==============================] - 3s 13ms/step - loss: 2.2968e-04 - accuracy: 0.9999 - val_loss: 0.0854 - val_accuracy: 0.9848
Epoch 117/200
235/235 [==============================] - 3s 14ms/step - loss: 1.0776e-04 - accuracy: 1.0000 - val_loss: 0.0869 - val_accuracy: 0.9843
Epoch 118/200
235/235 [==============================] - 3s 14ms/step - loss: 7.5090e-05 - accuracy: 1.0000 - val_loss: 0.0866 - val_accuracy: 0.9847
Epoch 119/200
235/235 [==============================] - 3s 14ms/step - loss: 4.4949e-05 - accuracy: 1.0000 - val_loss: 0.0865 - val_accuracy: 0.9846
Epoch 120/200
235/235 [==============================] - 3s 14ms/step - loss: 4.9686e-05 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9844
Epoch 121/200
235/235 [==============================] - 3s 14ms/step - loss: 3.3715e-05 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9848
Epoch 122/200
235/235 [==============================] - 3s 14ms/step - loss: 3.2463e-05 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9847
Epoch 123/200
235/235 [==============================] - 3s 14ms/step - loss: 2.5402e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9842
Epoch 124/200
235/235 [==============================] - 3s 15ms/step - loss: 5.1521e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9845
Epoch 125/200
235/235 [==============================] - 3s 14ms/step - loss: 3.8432e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9845
Epoch 126/200
235/235 [==============================] - 3s 12ms/step - loss: 2.5508e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9845
Epoch 127/200
235/235 [==============================] - 3s 14ms/step - loss: 1.6994e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9847
Epoch 128/200
235/235 [==============================] - 3s 14ms/step - loss: 1.4846e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9845
Epoch 129/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0070 - accuracy: 0.9980 - val_loss: 0.1596 - val_accuracy: 0.9736
Epoch 130/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.1036 - val_accuracy: 0.9821
Epoch 131/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.0873 - val_accuracy: 0.9845
Epoch 132/200
235/235 [==============================] - 3s 15ms/step - loss: 6.7479e-04 - accuracy: 0.9998 - val_loss: 0.0856 - val_accuracy: 0.9852
Epoch 133/200
235/235 [==============================] - 3s 14ms/step - loss: 2.2862e-04 - accuracy: 1.0000 - val_loss: 0.0867 - val_accuracy: 0.9851
Epoch 134/200
235/235 [==============================] - 3s 14ms/step - loss: 1.6797e-04 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9851
Epoch 135/200
235/235 [==============================] - 3s 14ms/step - loss: 2.5734e-04 - accuracy: 0.9999 - val_loss: 0.0877 - val_accuracy: 0.9841
Epoch 136/200
235/235 [==============================] - 3s 14ms/step - loss: 1.1071e-04 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9849
Epoch 137/200
235/235 [==============================] - 3s 14ms/step - loss: 4.3735e-04 - accuracy: 0.9999 - val_loss: 0.0898 - val_accuracy: 0.9843
Epoch 138/200
235/235 [==============================] - 3s 14ms/step - loss: 1.7115e-04 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9846
Epoch 139/200
235/235 [==============================] - 3s 14ms/step - loss: 9.0449e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9849
Epoch 140/200
235/235 [==============================] - 3s 15ms/step - loss: 1.1265e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9847
Epoch 141/200
235/235 [==============================] - 3s 14ms/step - loss: 1.2293e-04 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9842
Epoch 142/200
235/235 [==============================] - 3s 14ms/step - loss: 6.1251e-05 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9841
Epoch 143/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1227 - val_accuracy: 0.9799
Epoch 144/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.1130 - val_accuracy: 0.9791
Epoch 145/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1031 - val_accuracy: 0.9816
Epoch 146/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1005 - val_accuracy: 0.9824
Epoch 147/200
235/235 [==============================] - 3s 15ms/step - loss: 3.6012e-04 - accuracy: 0.9999 - val_loss: 0.1015 - val_accuracy: 0.9823
Epoch 148/200
235/235 [==============================] - 3s 15ms/step - loss: 2.0742e-04 - accuracy: 0.9999 - val_loss: 0.1003 - val_accuracy: 0.9829
Epoch 149/200
235/235 [==============================] - 3s 15ms/step - loss: 9.0095e-05 - accuracy: 1.0000 - val_loss: 0.1017 - val_accuracy: 0.9828
Epoch 150/200
235/235 [==============================] - 3s 14ms/step - loss: 2.3657e-04 - accuracy: 0.9999 - val_loss: 0.0995 - val_accuracy: 0.9839
Epoch 151/200
235/235 [==============================] - 3s 14ms/step - loss: 5.0496e-04 - accuracy: 0.9999 - val_loss: 0.1076 - val_accuracy: 0.9829
Epoch 152/200
235/235 [==============================] - 3s 14ms/step - loss: 7.2158e-04 - accuracy: 0.9998 - val_loss: 0.1133 - val_accuracy: 0.9812
Epoch 153/200
235/235 [==============================] - 3s 15ms/step - loss: 3.7902e-04 - accuracy: 0.9999 - val_loss: 0.1091 - val_accuracy: 0.9809
Epoch 154/200
235/235 [==============================] - 3s 14ms/step - loss: 4.6656e-04 - accuracy: 0.9999 - val_loss: 0.1188 - val_accuracy: 0.9810
Epoch 155/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9996 - val_loss: 0.1129 - val_accuracy: 0.9820
Epoch 156/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1256 - val_accuracy: 0.9786
Epoch 157/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 0.1161 - val_accuracy: 0.9802
Epoch 158/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1126 - val_accuracy: 0.9806
Epoch 159/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1133 - val_accuracy: 0.9811
Epoch 160/200
235/235 [==============================] - 3s 14ms/step - loss: 2.5914e-04 - accuracy: 0.9999 - val_loss: 0.1073 - val_accuracy: 0.9822
Epoch 161/200
235/235 [==============================] - 3s 13ms/step - loss: 6.9392e-05 - accuracy: 1.0000 - val_loss: 0.1041 - val_accuracy: 0.9818
Epoch 162/200
235/235 [==============================] - 3s 15ms/step - loss: 3.7683e-05 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9821
Epoch 163/200
235/235 [==============================] - 3s 15ms/step - loss: 2.9555e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9821
Epoch 164/200
235/235 [==============================] - 3s 14ms/step - loss: 2.2291e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9822
Epoch 165/200
235/235 [==============================] - 3s 15ms/step - loss: 2.5781e-05 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9821
Epoch 166/200
235/235 [==============================] - 3s 15ms/step - loss: 1.7213e-05 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9823
Epoch 167/200
235/235 [==============================] - 3s 15ms/step - loss: 1.8023e-05 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9828
Epoch 168/200
235/235 [==============================] - 3s 15ms/step - loss: 1.1348e-05 - accuracy: 1.0000 - val_loss: 0.1027 - val_accuracy: 0.9826
Epoch 169/200
235/235 [==============================] - 3s 15ms/step - loss: 1.1937e-05 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9826
Epoch 170/200
235/235 [==============================] - 3s 15ms/step - loss: 1.0013e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9826
Epoch 171/200
235/235 [==============================] - 3s 15ms/step - loss: 1.1729e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9829
Epoch 172/200
235/235 [==============================] - 3s 14ms/step - loss: 9.6510e-06 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9828
Epoch 173/200
235/235 [==============================] - 3s 14ms/step - loss: 8.3136e-06 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9830
Epoch 174/200
235/235 [==============================] - 3s 14ms/step - loss: 6.7108e-06 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9830
Epoch 175/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.1845 - val_accuracy: 0.9705
Epoch 176/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0091 - accuracy: 0.9972 - val_loss: 0.1317 - val_accuracy: 0.9796
Epoch 177/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1098 - val_accuracy: 0.9813
Epoch 178/200
235/235 [==============================] - 3s 15ms/step - loss: 2.0586e-04 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9820
Epoch 179/200
235/235 [==============================] - 3s 15ms/step - loss: 9.7181e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9812
Epoch 180/200
235/235 [==============================] - 4s 15ms/step - loss: 1.0607e-04 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9819
Epoch 181/200
235/235 [==============================] - 4s 15ms/step - loss: 1.4671e-04 - accuracy: 0.9999 - val_loss: 0.1053 - val_accuracy: 0.9820
Epoch 182/200
235/235 [==============================] - 4s 15ms/step - loss: 4.1251e-05 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9818
Epoch 183/200
235/235 [==============================] - 4s 16ms/step - loss: 3.0490e-05 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9823
Epoch 184/200
235/235 [==============================] - 4s 15ms/step - loss: 2.5516e-05 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9826
Epoch 185/200
235/235 [==============================] - 4s 15ms/step - loss: 3.0899e-05 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9827
Epoch 186/200
235/235 [==============================] - 4s 15ms/step - loss: 2.9537e-05 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9827
Epoch 187/200
235/235 [==============================] - 4s 15ms/step - loss: 3.7856e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9825
Epoch 188/200
235/235 [==============================] - 4s 15ms/step - loss: 3.6901e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9830
Epoch 189/200
235/235 [==============================] - 4s 15ms/step - loss: 1.4884e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9831
Epoch 190/200
235/235 [==============================] - 4s 15ms/step - loss: 1.3319e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9832
Epoch 191/200
235/235 [==============================] - 4s 15ms/step - loss: 1.1079e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9831
Epoch 192/200
235/235 [==============================] - 4s 15ms/step - loss: 9.4640e-06 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9831
Epoch 193/200
235/235 [==============================] - 4s 15ms/step - loss: 1.0928e-05 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9834
Epoch 194/200
235/235 [==============================] - 4s 16ms/step - loss: 2.9295e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9828
Epoch 195/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0070 - accuracy: 0.9980 - val_loss: 0.1321 - val_accuracy: 0.9791
Epoch 196/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0050 - accuracy: 0.9985 - val_loss: 0.1003 - val_accuracy: 0.9829
Epoch 197/200
235/235 [==============================] - 4s 15ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1016 - val_accuracy: 0.9826
Epoch 198/200
235/235 [==============================] - 4s 15ms/step - loss: 2.4540e-04 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9829
Epoch 199/200
235/235 [==============================] - 4s 15ms/step - loss: 1.2677e-04 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9837
Epoch 200/200
235/235 [==============================] - 4s 15ms/step - loss: 7.2674e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9838
Epoch 1/200
235/235 [==============================] - 3s 10ms/step - loss: 1.5548 - accuracy: 0.8564 - val_loss: 0.9197 - val_accuracy: 0.9028
Epoch 2/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8712 - accuracy: 0.8970 - val_loss: 0.8255 - val_accuracy: 0.9004
Epoch 3/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8326 - accuracy: 0.8972 - val_loss: 0.8123 - val_accuracy: 0.9002
Epoch 4/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8235 - accuracy: 0.8970 - val_loss: 0.8064 - val_accuracy: 0.8986
Epoch 5/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8187 - accuracy: 0.8976 - val_loss: 0.8014 - val_accuracy: 0.8995
Epoch 6/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.8977 - val_loss: 0.7993 - val_accuracy: 0.8989
Epoch 7/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8130 - accuracy: 0.8982 - val_loss: 0.7971 - val_accuracy: 0.8983
Epoch 8/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8114 - accuracy: 0.8980 - val_loss: 0.7959 - val_accuracy: 0.8990
Epoch 9/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8099 - accuracy: 0.8982 - val_loss: 0.7963 - val_accuracy: 0.8990
Epoch 10/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8094 - accuracy: 0.8983 - val_loss: 0.7947 - val_accuracy: 0.8990
Epoch 11/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8084 - accuracy: 0.8985 - val_loss: 0.7948 - val_accuracy: 0.8990
Epoch 12/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8082 - accuracy: 0.8987 - val_loss: 0.7926 - val_accuracy: 0.9002
Epoch 13/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8072 - accuracy: 0.8987 - val_loss: 0.7932 - val_accuracy: 0.8995
Epoch 14/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8071 - accuracy: 0.8990 - val_loss: 0.7912 - val_accuracy: 0.9003
Epoch 15/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8065 - accuracy: 0.8991 - val_loss: 0.7900 - val_accuracy: 0.9016
Epoch 16/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8063 - accuracy: 0.8990 - val_loss: 0.7898 - val_accuracy: 0.9015
Epoch 17/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8060 - accuracy: 0.8992 - val_loss: 0.7907 - val_accuracy: 0.9013
Epoch 18/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8059 - accuracy: 0.8993 - val_loss: 0.7903 - val_accuracy: 0.9016
Epoch 19/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8996 - val_loss: 0.7902 - val_accuracy: 0.9022
Epoch 20/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8999 - val_loss: 0.7904 - val_accuracy: 0.9016
Epoch 21/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8053 - accuracy: 0.9000 - val_loss: 0.7890 - val_accuracy: 0.9018
Epoch 22/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8998 - val_loss: 0.7896 - val_accuracy: 0.9028
Epoch 23/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8999 - val_loss: 0.7894 - val_accuracy: 0.9023
Epoch 24/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.9000 - val_loss: 0.7895 - val_accuracy: 0.9023
Epoch 25/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8051 - accuracy: 0.9000 - val_loss: 0.7885 - val_accuracy: 0.9027
Epoch 26/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.9003 - val_loss: 0.7884 - val_accuracy: 0.9031
Epoch 27/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.9004 - val_loss: 0.7892 - val_accuracy: 0.9020
Epoch 28/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8049 - accuracy: 0.9004 - val_loss: 0.7889 - val_accuracy: 0.9030
Epoch 29/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.9005 - val_loss: 0.7887 - val_accuracy: 0.9032
Epoch 30/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.9005 - val_loss: 0.7894 - val_accuracy: 0.9024
Epoch 31/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.9005 - val_loss: 0.7888 - val_accuracy: 0.9034
Epoch 32/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.9004 - val_loss: 0.7886 - val_accuracy: 0.9028
Epoch 33/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9003 - val_loss: 0.7894 - val_accuracy: 0.9022
Epoch 34/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7892 - val_accuracy: 0.9027
Epoch 35/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9010 - val_loss: 0.7871 - val_accuracy: 0.9037
Epoch 36/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7886 - val_accuracy: 0.9025
Epoch 37/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9006 - val_loss: 0.7882 - val_accuracy: 0.9041
Epoch 38/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9008 - val_loss: 0.7887 - val_accuracy: 0.9031
Epoch 39/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9039
Epoch 40/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9036
Epoch 41/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9033
Epoch 42/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9029
Epoch 43/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9009 - val_loss: 0.7889 - val_accuracy: 0.9029
Epoch 44/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9032
Epoch 45/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9038
Epoch 46/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9010 - val_loss: 0.7884 - val_accuracy: 0.9030
Epoch 47/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9009 - val_loss: 0.7873 - val_accuracy: 0.9040
Epoch 48/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9037
Epoch 49/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9033
Epoch 50/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9033
Epoch 51/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7886 - val_accuracy: 0.9030
Epoch 52/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7886 - val_accuracy: 0.9028
Epoch 53/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9007 - val_loss: 0.7877 - val_accuracy: 0.9041
Epoch 54/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7880 - val_accuracy: 0.9034
Epoch 55/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9036
Epoch 56/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9043
Epoch 57/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7883 - val_accuracy: 0.9039
Epoch 58/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7878 - val_accuracy: 0.9034
Epoch 59/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9034
Epoch 60/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7880 - val_accuracy: 0.9034
Epoch 61/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9036
Epoch 62/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9036
Epoch 63/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9033
Epoch 64/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7876 - val_accuracy: 0.9035
Epoch 65/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9032
Epoch 66/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9015 - val_loss: 0.7872 - val_accuracy: 0.9037
Epoch 67/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9042
Epoch 68/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9042
Epoch 69/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7881 - val_accuracy: 0.9036
Epoch 70/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9032
Epoch 71/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7885 - val_accuracy: 0.9035
Epoch 72/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9024
Epoch 73/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7884 - val_accuracy: 0.9037
Epoch 74/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7875 - val_accuracy: 0.9038
Epoch 75/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9036
Epoch 76/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7892 - val_accuracy: 0.9023
Epoch 77/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9041
Epoch 78/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9033
Epoch 79/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9036
Epoch 80/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9035
Epoch 81/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9026
Epoch 82/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7881 - val_accuracy: 0.9036
Epoch 83/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9030
Epoch 84/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9032
Epoch 85/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7887 - val_accuracy: 0.9030
Epoch 86/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9044
Epoch 87/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9031
Epoch 88/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7888 - val_accuracy: 0.9033
Epoch 89/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9029
Epoch 90/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7875 - val_accuracy: 0.9032
Epoch 91/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9035
Epoch 92/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9036
Epoch 93/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7875 - val_accuracy: 0.9036
Epoch 94/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9027
Epoch 95/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9032
Epoch 96/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7885 - val_accuracy: 0.9027
Epoch 97/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9041
Epoch 98/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9040
Epoch 99/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9026
Epoch 100/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9040
Epoch 101/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7883 - val_accuracy: 0.9040
Epoch 102/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7871 - val_accuracy: 0.9038
Epoch 103/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9030
Epoch 104/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9039
Epoch 105/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9040
Epoch 106/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9028
Epoch 107/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9041
Epoch 108/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9030
Epoch 109/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7868 - val_accuracy: 0.9037
Epoch 110/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9039
Epoch 111/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9039
Epoch 112/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9036
Epoch 113/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7885 - val_accuracy: 0.9037
Epoch 114/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7893 - val_accuracy: 0.9023
Epoch 115/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7883 - val_accuracy: 0.9028
Epoch 116/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9033
Epoch 117/200
235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9031
Epoch 118/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9034
Epoch 119/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9033
Epoch 120/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9045
Epoch 121/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9039
Epoch 122/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7887 - val_accuracy: 0.9028
Epoch 123/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9039
Epoch 124/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9036
Epoch 125/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9038
Epoch 126/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9037
Epoch 127/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9040
Epoch 128/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7885 - val_accuracy: 0.9029
Epoch 129/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7874 - val_accuracy: 0.9045
Epoch 130/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7876 - val_accuracy: 0.9031
Epoch 131/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9029
Epoch 132/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9031
Epoch 133/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7880 - val_accuracy: 0.9036
Epoch 134/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7869 - val_accuracy: 0.9043
Epoch 135/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8029 - accuracy: 0.9015 - val_loss: 0.7869 - val_accuracy: 0.9045
Epoch 136/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7884 - val_accuracy: 0.9033
Epoch 137/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9039
Epoch 138/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7877 - val_accuracy: 0.9042
Epoch 139/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9031
Epoch 140/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9041
Epoch 141/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9025
Epoch 142/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7890 - val_accuracy: 0.9023
Epoch 143/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9033
Epoch 144/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7877 - val_accuracy: 0.9039
Epoch 145/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7885 - val_accuracy: 0.9027
Epoch 146/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9038
Epoch 147/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7889 - val_accuracy: 0.9037
Epoch 148/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9040
Epoch 149/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7870 - val_accuracy: 0.9045
Epoch 150/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9039
Epoch 151/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7885 - val_accuracy: 0.9033
Epoch 152/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7888 - val_accuracy: 0.9038
Epoch 153/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9040
Epoch 154/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9036
Epoch 155/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7867 - val_accuracy: 0.9039
Epoch 156/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9042
Epoch 157/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9033
Epoch 158/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9037
Epoch 159/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9041
Epoch 160/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9037
Epoch 161/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9042
Epoch 162/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9039
Epoch 163/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7888 - val_accuracy: 0.9036
Epoch 164/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9035
Epoch 165/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9015 - val_loss: 0.7874 - val_accuracy: 0.9037
Epoch 166/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9039
Epoch 167/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9030
Epoch 168/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9042
Epoch 169/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9039
Epoch 170/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9037
Epoch 171/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7887 - val_accuracy: 0.9028
Epoch 172/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9042
Epoch 173/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9035
Epoch 174/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7883 - val_accuracy: 0.9033
Epoch 175/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7878 - val_accuracy: 0.9032
Epoch 176/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7881 - val_accuracy: 0.9026
Epoch 177/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7887 - val_accuracy: 0.9030
Epoch 178/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9015 - val_loss: 0.7884 - val_accuracy: 0.9032
Epoch 179/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9037
Epoch 180/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9042
Epoch 181/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8029 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9032
Epoch 182/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9031
Epoch 183/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9008 - val_loss: 0.7890 - val_accuracy: 0.9034
Epoch 184/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7885 - val_accuracy: 0.9034
Epoch 185/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7883 - val_accuracy: 0.9029
Epoch 186/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9032
Epoch 187/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7881 - val_accuracy: 0.9039
Epoch 188/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9037
Epoch 189/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9035
Epoch 190/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9049
Epoch 191/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7885 - val_accuracy: 0.9029
Epoch 192/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9036
Epoch 193/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9034
Epoch 194/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9039
Epoch 195/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9034
Epoch 196/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9035
Epoch 197/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9032
Epoch 198/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7880 - val_accuracy: 0.9033
Epoch 199/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9018 - val_loss: 0.7872 - val_accuracy: 0.9033
Epoch 200/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9036
Epoch 1/200
235/235 [==============================] - 3s 9ms/step - loss: 0.4636 - accuracy: 0.8702 - val_loss: 0.2482 - val_accuracy: 0.9267
Epoch 2/200
235/235 [==============================] - 2s 9ms/step - loss: 0.2260 - accuracy: 0.9349 - val_loss: 0.1879 - val_accuracy: 0.9466
Epoch 3/200
235/235 [==============================] - 2s 9ms/step - loss: 0.1718 - accuracy: 0.9494 - val_loss: 0.1556 - val_accuracy: 0.9549
Epoch 4/200
235/235 [==============================] - 2s 9ms/step - loss: 0.1382 - accuracy: 0.9593 - val_loss: 0.1365 - val_accuracy: 0.9591
Epoch 5/200
235/235 [==============================] - 2s 9ms/step - loss: 0.1141 - accuracy: 0.9670 - val_loss: 0.1245 - val_accuracy: 0.9632
Epoch 6/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0958 - accuracy: 0.9725 - val_loss: 0.1159 - val_accuracy: 0.9642
Epoch 7/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0813 - accuracy: 0.9760 - val_loss: 0.1095 - val_accuracy: 0.9663
Epoch 8/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0695 - accuracy: 0.9799 - val_loss: 0.1057 - val_accuracy: 0.9680
Epoch 9/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0598 - accuracy: 0.9828 - val_loss: 0.1035 - val_accuracy: 0.9694
Epoch 10/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0517 - accuracy: 0.9853 - val_loss: 0.1009 - val_accuracy: 0.9704
Epoch 11/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0451 - accuracy: 0.9875 - val_loss: 0.1000 - val_accuracy: 0.9702
Epoch 12/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0389 - accuracy: 0.9895 - val_loss: 0.1002 - val_accuracy: 0.9702
Epoch 13/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0337 - accuracy: 0.9911 - val_loss: 0.1004 - val_accuracy: 0.9708
Epoch 14/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0291 - accuracy: 0.9926 - val_loss: 0.1032 - val_accuracy: 0.9711
Epoch 15/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0251 - accuracy: 0.9941 - val_loss: 0.1059 - val_accuracy: 0.9705
Epoch 16/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0213 - accuracy: 0.9955 - val_loss: 0.1078 - val_accuracy: 0.9707
Epoch 17/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0184 - accuracy: 0.9963 - val_loss: 0.1124 - val_accuracy: 0.9704
Epoch 18/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0158 - accuracy: 0.9972 - val_loss: 0.1128 - val_accuracy: 0.9711
Epoch 19/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0137 - accuracy: 0.9978 - val_loss: 0.1135 - val_accuracy: 0.9714
Epoch 20/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0118 - accuracy: 0.9982 - val_loss: 0.1175 - val_accuracy: 0.9712
Epoch 21/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0107 - accuracy: 0.9985 - val_loss: 0.1152 - val_accuracy: 0.9715
Epoch 22/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0101 - accuracy: 0.9982 - val_loss: 0.1166 - val_accuracy: 0.9721
Epoch 23/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0100 - accuracy: 0.9980 - val_loss: 0.1213 - val_accuracy: 0.9706
Epoch 24/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0104 - accuracy: 0.9973 - val_loss: 0.1252 - val_accuracy: 0.9722
Epoch 25/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0107 - accuracy: 0.9970 - val_loss: 0.1230 - val_accuracy: 0.9721
Epoch 26/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 0.1223 - val_accuracy: 0.9732
Epoch 27/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0067 - accuracy: 0.9984 - val_loss: 0.1164 - val_accuracy: 0.9743
Epoch 28/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0060 - accuracy: 0.9987 - val_loss: 0.1174 - val_accuracy: 0.9751
Epoch 29/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0058 - accuracy: 0.9986 - val_loss: 0.1249 - val_accuracy: 0.9736
Epoch 30/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 0.1256 - val_accuracy: 0.9740
Epoch 31/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0038 - accuracy: 0.9993 - val_loss: 0.1313 - val_accuracy: 0.9718
Epoch 32/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0024 - accuracy: 0.9998 - val_loss: 0.1371 - val_accuracy: 0.9721
Epoch 33/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0019 - accuracy: 0.9999 - val_loss: 0.1249 - val_accuracy: 0.9742
Epoch 34/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 0.9998 - val_loss: 0.1356 - val_accuracy: 0.9737
Epoch 35/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 0.9999 - val_loss: 0.1321 - val_accuracy: 0.9745
Epoch 36/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 0.9999 - val_loss: 0.1400 - val_accuracy: 0.9733
Epoch 37/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.1554 - val_accuracy: 0.9717
Epoch 38/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0130 - accuracy: 0.9956 - val_loss: 0.1454 - val_accuracy: 0.9717
Epoch 39/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0052 - accuracy: 0.9985 - val_loss: 0.1359 - val_accuracy: 0.9743
Epoch 40/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.1424 - val_accuracy: 0.9746
Epoch 41/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.1444 - val_accuracy: 0.9739
Epoch 42/200
235/235 [==============================] - 2s 9ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.1372 - val_accuracy: 0.9760
Epoch 43/200
235/235 [==============================] - 2s 9ms/step - loss: 6.9030e-04 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9760
Epoch 44/200
235/235 [==============================] - 2s 9ms/step - loss: 5.2515e-04 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 0.9755
Epoch 45/200
235/235 [==============================] - 2s 9ms/step - loss: 3.9104e-04 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9763
Epoch 46/200
235/235 [==============================] - 2s 9ms/step - loss: 3.0156e-04 - accuracy: 1.0000 - val_loss: 0.1396 - val_accuracy: 0.9762
Epoch 47/200
235/235 [==============================] - 2s 9ms/step - loss: 2.5831e-04 - accuracy: 1.0000 - val_loss: 0.1401 - val_accuracy: 0.9764
Epoch 48/200
235/235 [==============================] - 2s 10ms/step - loss: 2.2528e-04 - accuracy: 1.0000 - val_loss: 0.1408 - val_accuracy: 0.9765
Epoch 49/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0069e-04 - accuracy: 1.0000 - val_loss: 0.1416 - val_accuracy: 0.9765
Epoch 50/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8075e-04 - accuracy: 1.0000 - val_loss: 0.1424 - val_accuracy: 0.9765
Epoch 51/200
235/235 [==============================] - 2s 9ms/step - loss: 1.6289e-04 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9766
Epoch 52/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4745e-04 - accuracy: 1.0000 - val_loss: 0.1442 - val_accuracy: 0.9765
Epoch 53/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3378e-04 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9765
Epoch 54/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2113e-04 - accuracy: 1.0000 - val_loss: 0.1462 - val_accuracy: 0.9766
Epoch 55/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0982e-04 - accuracy: 1.0000 - val_loss: 0.1473 - val_accuracy: 0.9768
Epoch 56/200
235/235 [==============================] - 2s 9ms/step - loss: 9.9565e-05 - accuracy: 1.0000 - val_loss: 0.1484 - val_accuracy: 0.9769
Epoch 57/200
235/235 [==============================] - 2s 8ms/step - loss: 8.9980e-05 - accuracy: 1.0000 - val_loss: 0.1497 - val_accuracy: 0.9770
Epoch 58/200
235/235 [==============================] - 2s 9ms/step - loss: 8.1538e-05 - accuracy: 1.0000 - val_loss: 0.1509 - val_accuracy: 0.9770
Epoch 59/200
235/235 [==============================] - 2s 9ms/step - loss: 7.3624e-05 - accuracy: 1.0000 - val_loss: 0.1521 - val_accuracy: 0.9768
Epoch 60/200
235/235 [==============================] - 2s 9ms/step - loss: 6.6442e-05 - accuracy: 1.0000 - val_loss: 0.1533 - val_accuracy: 0.9769
Epoch 61/200
235/235 [==============================] - 2s 9ms/step - loss: 5.9909e-05 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 0.9769
Epoch 62/200
235/235 [==============================] - 2s 9ms/step - loss: 5.3916e-05 - accuracy: 1.0000 - val_loss: 0.1561 - val_accuracy: 0.9766
Epoch 63/200
235/235 [==============================] - 2s 9ms/step - loss: 4.8425e-05 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9765
Epoch 64/200
235/235 [==============================] - 2s 9ms/step - loss: 4.3482e-05 - accuracy: 1.0000 - val_loss: 0.1587 - val_accuracy: 0.9763
Epoch 65/200
235/235 [==============================] - 2s 9ms/step - loss: 3.8939e-05 - accuracy: 1.0000 - val_loss: 0.1601 - val_accuracy: 0.9763
Epoch 66/200
235/235 [==============================] - 2s 9ms/step - loss: 3.4831e-05 - accuracy: 1.0000 - val_loss: 0.1617 - val_accuracy: 0.9762
Epoch 67/200
235/235 [==============================] - 2s 9ms/step - loss: 3.1149e-05 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9761
Epoch 68/200
235/235 [==============================] - 2s 9ms/step - loss: 2.7816e-05 - accuracy: 1.0000 - val_loss: 0.1646 - val_accuracy: 0.9760
Epoch 69/200
235/235 [==============================] - 2s 9ms/step - loss: 2.4778e-05 - accuracy: 1.0000 - val_loss: 0.1660 - val_accuracy: 0.9759
Epoch 70/200
235/235 [==============================] - 2s 9ms/step - loss: 2.2055e-05 - accuracy: 1.0000 - val_loss: 0.1675 - val_accuracy: 0.9759
Epoch 71/200
235/235 [==============================] - 2s 9ms/step - loss: 1.9603e-05 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9759
Epoch 72/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7442e-05 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9758
Epoch 73/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5484e-05 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9762
Epoch 74/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3738e-05 - accuracy: 1.0000 - val_loss: 0.1736 - val_accuracy: 0.9761
Epoch 75/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2169e-05 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9762
Epoch 76/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0803e-05 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9760
Epoch 77/200
235/235 [==============================] - 2s 9ms/step - loss: 9.5778e-06 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9762
Epoch 78/200
235/235 [==============================] - 2s 9ms/step - loss: 8.4757e-06 - accuracy: 1.0000 - val_loss: 0.1800 - val_accuracy: 0.9763
Epoch 79/200
235/235 [==============================] - 2s 9ms/step - loss: 7.4902e-06 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9763
Epoch 80/200
235/235 [==============================] - 2s 9ms/step - loss: 6.6270e-06 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9763
Epoch 81/200
235/235 [==============================] - 2s 9ms/step - loss: 5.8574e-06 - accuracy: 1.0000 - val_loss: 0.1847 - val_accuracy: 0.9763
Epoch 82/200
235/235 [==============================] - 2s 9ms/step - loss: 5.1781e-06 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9762
Epoch 83/200
235/235 [==============================] - 2s 9ms/step - loss: 4.5803e-06 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9762
Epoch 84/200
235/235 [==============================] - 2s 9ms/step - loss: 4.0495e-06 - accuracy: 1.0000 - val_loss: 0.1895 - val_accuracy: 0.9762
Epoch 85/200
235/235 [==============================] - 2s 9ms/step - loss: 3.5778e-06 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 0.9762
Epoch 86/200
235/235 [==============================] - 2s 9ms/step - loss: 3.1672e-06 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9762
Epoch 87/200
235/235 [==============================] - 2s 9ms/step - loss: 2.7985e-06 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9761
Epoch 88/200
235/235 [==============================] - 2s 9ms/step - loss: 2.4729e-06 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9762
Epoch 89/200
235/235 [==============================] - 2s 9ms/step - loss: 2.1841e-06 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9763
Epoch 90/200
235/235 [==============================] - 2s 9ms/step - loss: 1.9319e-06 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9763
Epoch 91/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7084e-06 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9763
Epoch 92/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5128e-06 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9764
Epoch 93/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3339e-06 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9764
Epoch 94/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1823e-06 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9763
Epoch 95/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0465e-06 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9765
Epoch 96/200
235/235 [==============================] - 2s 9ms/step - loss: 9.2750e-07 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9764
Epoch 97/200
235/235 [==============================] - 2s 9ms/step - loss: 8.2416e-07 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9764
Epoch 98/200
235/235 [==============================] - 2s 9ms/step - loss: 7.2945e-07 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9764
Epoch 99/200
235/235 [==============================] - 2s 9ms/step - loss: 6.4697e-07 - accuracy: 1.0000 - val_loss: 0.2129 - val_accuracy: 0.9762
Epoch 100/200
235/235 [==============================] - 2s 9ms/step - loss: 5.7459e-07 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9763
Epoch 101/200
235/235 [==============================] - 2s 9ms/step - loss: 5.1078e-07 - accuracy: 1.0000 - val_loss: 0.2159 - val_accuracy: 0.9762
Epoch 102/200
235/235 [==============================] - 2s 9ms/step - loss: 4.5550e-07 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9760
Epoch 103/200
235/235 [==============================] - 2s 9ms/step - loss: 4.0522e-07 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.9760
Epoch 104/200
235/235 [==============================] - 2s 9ms/step - loss: 3.6144e-07 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9759
Epoch 105/200
235/235 [==============================] - 2s 9ms/step - loss: 3.2283e-07 - accuracy: 1.0000 - val_loss: 0.2217 - val_accuracy: 0.9759
Epoch 106/200
235/235 [==============================] - 2s 9ms/step - loss: 2.8862e-07 - accuracy: 1.0000 - val_loss: 0.2231 - val_accuracy: 0.9759
Epoch 107/200
235/235 [==============================] - 2s 9ms/step - loss: 2.5798e-07 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9759
Epoch 108/200
235/235 [==============================] - 2s 9ms/step - loss: 2.3138e-07 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9760
Epoch 109/200
235/235 [==============================] - 2s 9ms/step - loss: 2.0806e-07 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9759
Epoch 110/200
235/235 [==============================] - 2s 9ms/step - loss: 1.8673e-07 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9758
Epoch 111/200
235/235 [==============================] - 2s 9ms/step - loss: 1.6829e-07 - accuracy: 1.0000 - val_loss: 0.2297 - val_accuracy: 0.9758
Epoch 112/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5177e-07 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9758
Epoch 113/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3728e-07 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9758
Epoch 114/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2430e-07 - accuracy: 1.0000 - val_loss: 0.2333 - val_accuracy: 0.9759
Epoch 115/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1303e-07 - accuracy: 1.0000 - val_loss: 0.2344 - val_accuracy: 0.9759
Epoch 116/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0274e-07 - accuracy: 1.0000 - val_loss: 0.2355 - val_accuracy: 0.9758
Epoch 117/200
235/235 [==============================] - 2s 9ms/step - loss: 9.3704e-08 - accuracy: 1.0000 - val_loss: 0.2367 - val_accuracy: 0.9758
Epoch 118/200
235/235 [==============================] - 2s 9ms/step - loss: 8.5614e-08 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9758
Epoch 119/200
235/235 [==============================] - 2s 9ms/step - loss: 7.8291e-08 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9758
Epoch 120/200
235/235 [==============================] - 2s 9ms/step - loss: 7.1754e-08 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9757
Epoch 121/200
235/235 [==============================] - 2s 9ms/step - loss: 6.6157e-08 - accuracy: 1.0000 - val_loss: 0.2406 - val_accuracy: 0.9758
Epoch 122/200
235/235 [==============================] - 2s 9ms/step - loss: 6.1007e-08 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9759
Epoch 123/200
235/235 [==============================] - 2s 9ms/step - loss: 5.6366e-08 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9759
Epoch 124/200
235/235 [==============================] - 2s 9ms/step - loss: 5.2265e-08 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9759
Epoch 125/200
235/235 [==============================] - 2s 9ms/step - loss: 4.8502e-08 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9761
Epoch 126/200
235/235 [==============================] - 2s 9ms/step - loss: 4.5258e-08 - accuracy: 1.0000 - val_loss: 0.2448 - val_accuracy: 0.9760
Epoch 127/200
235/235 [==============================] - 2s 9ms/step - loss: 4.2105e-08 - accuracy: 1.0000 - val_loss: 0.2457 - val_accuracy: 0.9760
Epoch 128/200
235/235 [==============================] - 2s 9ms/step - loss: 3.9486e-08 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9759
Epoch 129/200
235/235 [==============================] - 2s 9ms/step - loss: 3.6925e-08 - accuracy: 1.0000 - val_loss: 0.2470 - val_accuracy: 0.9759
Epoch 130/200
235/235 [==============================] - 2s 9ms/step - loss: 3.4688e-08 - accuracy: 1.0000 - val_loss: 0.2477 - val_accuracy: 0.9760
Epoch 131/200
235/235 [==============================] - 2s 9ms/step - loss: 3.2661e-08 - accuracy: 1.0000 - val_loss: 0.2482 - val_accuracy: 0.9759
Epoch 132/200
235/235 [==============================] - 2s 9ms/step - loss: 3.0784e-08 - accuracy: 1.0000 - val_loss: 0.2489 - val_accuracy: 0.9759
Epoch 133/200
235/235 [==============================] - 2s 9ms/step - loss: 2.8998e-08 - accuracy: 1.0000 - val_loss: 0.2495 - val_accuracy: 0.9761
Epoch 134/200
235/235 [==============================] - 2s 9ms/step - loss: 2.7517e-08 - accuracy: 1.0000 - val_loss: 0.2500 - val_accuracy: 0.9761
Epoch 135/200
235/235 [==============================] - 2s 9ms/step - loss: 2.6041e-08 - accuracy: 1.0000 - val_loss: 0.2505 - val_accuracy: 0.9760
Epoch 136/200
235/235 [==============================] - 2s 9ms/step - loss: 2.4718e-08 - accuracy: 1.0000 - val_loss: 0.2511 - val_accuracy: 0.9758
Epoch 137/200
235/235 [==============================] - 2s 9ms/step - loss: 2.3550e-08 - accuracy: 1.0000 - val_loss: 0.2516 - val_accuracy: 0.9758
Epoch 138/200
235/235 [==============================] - 2s 9ms/step - loss: 2.2391e-08 - accuracy: 1.0000 - val_loss: 0.2521 - val_accuracy: 0.9758
Epoch 139/200
235/235 [==============================] - 2s 9ms/step - loss: 2.1414e-08 - accuracy: 1.0000 - val_loss: 0.2527 - val_accuracy: 0.9757
Epoch 140/200
235/235 [==============================] - 2s 9ms/step - loss: 2.0478e-08 - accuracy: 1.0000 - val_loss: 0.2531 - val_accuracy: 0.9758
Epoch 141/200
235/235 [==============================] - 2s 9ms/step - loss: 1.9616e-08 - accuracy: 1.0000 - val_loss: 0.2535 - val_accuracy: 0.9758
Epoch 142/200
235/235 [==============================] - 2s 9ms/step - loss: 1.8748e-08 - accuracy: 1.0000 - val_loss: 0.2539 - val_accuracy: 0.9759
Epoch 143/200
235/235 [==============================] - 2s 9ms/step - loss: 1.8088e-08 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9759
Epoch 144/200
235/235 [==============================] - 2s 9ms/step - loss: 1.7323e-08 - accuracy: 1.0000 - val_loss: 0.2547 - val_accuracy: 0.9759
Epoch 145/200
235/235 [==============================] - 2s 9ms/step - loss: 1.6610e-08 - accuracy: 1.0000 - val_loss: 0.2551 - val_accuracy: 0.9759
Epoch 146/200
235/235 [==============================] - 2s 9ms/step - loss: 1.6050e-08 - accuracy: 1.0000 - val_loss: 0.2555 - val_accuracy: 0.9759
Epoch 147/200
235/235 [==============================] - 2s 9ms/step - loss: 1.5507e-08 - accuracy: 1.0000 - val_loss: 0.2558 - val_accuracy: 0.9759
Epoch 148/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4987e-08 - accuracy: 1.0000 - val_loss: 0.2563 - val_accuracy: 0.9758
Epoch 149/200
235/235 [==============================] - 2s 9ms/step - loss: 1.4462e-08 - accuracy: 1.0000 - val_loss: 0.2565 - val_accuracy: 0.9758
Epoch 150/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3989e-08 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9758
Epoch 151/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3522e-08 - accuracy: 1.0000 - val_loss: 0.2571 - val_accuracy: 0.9757
Epoch 152/200
235/235 [==============================] - 2s 9ms/step - loss: 1.3117e-08 - accuracy: 1.0000 - val_loss: 0.2574 - val_accuracy: 0.9758
Epoch 153/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2749e-08 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9759
Epoch 154/200
235/235 [==============================] - 2s 9ms/step - loss: 1.2378e-08 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9759
Epoch 155/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1998e-08 - accuracy: 1.0000 - val_loss: 0.2583 - val_accuracy: 0.9760
Epoch 156/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1702e-08 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9760
Epoch 157/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1406e-08 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 0.9760
Epoch 158/200
235/235 [==============================] - 2s 9ms/step - loss: 1.1116e-08 - accuracy: 1.0000 - val_loss: 0.2592 - val_accuracy: 0.9760
Epoch 159/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0777e-08 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9761
Epoch 160/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0562e-08 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9761
Epoch 161/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0278e-08 - accuracy: 1.0000 - val_loss: 0.2600 - val_accuracy: 0.9760
Epoch 162/200
235/235 [==============================] - 2s 9ms/step - loss: 1.0008e-08 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9759
Epoch 163/200
235/235 [==============================] - 2s 9ms/step - loss: 9.7652e-09 - accuracy: 1.0000 - val_loss: 0.2604 - val_accuracy: 0.9759
Epoch 164/200
235/235 [==============================] - 2s 9ms/step - loss: 9.5765e-09 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.9759
Epoch 165/200
235/235 [==============================] - 2s 9ms/step - loss: 9.3182e-09 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9759
Epoch 166/200
235/235 [==============================] - 2s 9ms/step - loss: 9.1016e-09 - accuracy: 1.0000 - val_loss: 0.2610 - val_accuracy: 0.9759
Epoch 167/200
235/235 [==============================] - 2s 9ms/step - loss: 8.8712e-09 - accuracy: 1.0000 - val_loss: 0.2611 - val_accuracy: 0.9759
Epoch 168/200
235/235 [==============================] - 2s 8ms/step - loss: 8.7142e-09 - accuracy: 1.0000 - val_loss: 0.2614 - val_accuracy: 0.9760
Epoch 169/200
235/235 [==============================] - 2s 9ms/step - loss: 8.5394e-09 - accuracy: 1.0000 - val_loss: 0.2616 - val_accuracy: 0.9760
Epoch 170/200
235/235 [==============================] - 2s 9ms/step - loss: 8.3407e-09 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9760
Epoch 171/200
235/235 [==============================] - 2s 9ms/step - loss: 8.1658e-09 - accuracy: 1.0000 - val_loss: 0.2620 - val_accuracy: 0.9760
Epoch 172/200
235/235 [==============================] - 2s 9ms/step - loss: 7.9592e-09 - accuracy: 1.0000 - val_loss: 0.2622 - val_accuracy: 0.9759
Epoch 173/200
235/235 [==============================] - 2s 9ms/step - loss: 7.8539e-09 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9759
Epoch 174/200
235/235 [==============================] - 2s 9ms/step - loss: 7.6791e-09 - accuracy: 1.0000 - val_loss: 0.2626 - val_accuracy: 0.9760
Epoch 175/200
235/235 [==============================] - 2s 9ms/step - loss: 7.4983e-09 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9760
Epoch 176/200
235/235 [==============================] - 2s 9ms/step - loss: 7.4347e-09 - accuracy: 1.0000 - val_loss: 0.2629 - val_accuracy: 0.9760
Epoch 177/200
235/235 [==============================] - 2s 9ms/step - loss: 7.2340e-09 - accuracy: 1.0000 - val_loss: 0.2631 - val_accuracy: 0.9760
Epoch 178/200
235/235 [==============================] - 2s 8ms/step - loss: 7.1069e-09 - accuracy: 1.0000 - val_loss: 0.2632 - val_accuracy: 0.9760
Epoch 179/200
235/235 [==============================] - 2s 9ms/step - loss: 6.9936e-09 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9760
Epoch 180/200
235/235 [==============================] - 2s 9ms/step - loss: 6.8525e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9760
Epoch 181/200
235/235 [==============================] - 2s 9ms/step - loss: 6.7512e-09 - accuracy: 1.0000 - val_loss: 0.2636 - val_accuracy: 0.9759
Epoch 182/200
235/235 [==============================] - 2s 9ms/step - loss: 6.6419e-09 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9759
Epoch 183/200
235/235 [==============================] - 2s 9ms/step - loss: 6.5466e-09 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9759
Epoch 184/200
235/235 [==============================] - 2s 10ms/step - loss: 6.4274e-09 - accuracy: 1.0000 - val_loss: 0.2640 - val_accuracy: 0.9759
Epoch 185/200
235/235 [==============================] - 2s 9ms/step - loss: 6.3340e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9759
Epoch 186/200
235/235 [==============================] - 2s 9ms/step - loss: 6.2366e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9759
Epoch 187/200
235/235 [==============================] - 2s 9ms/step - loss: 6.1095e-09 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9759
Epoch 188/200
235/235 [==============================] - 2s 9ms/step - loss: 5.9764e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9759
Epoch 189/200
235/235 [==============================] - 2s 9ms/step - loss: 5.9227e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9759
Epoch 190/200
235/235 [==============================] - 2s 9ms/step - loss: 5.7697e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9759
Epoch 191/200
235/235 [==============================] - 2s 9ms/step - loss: 5.7141e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9759
Epoch 192/200
235/235 [==============================] - 2s 9ms/step - loss: 5.6307e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9759
Epoch 193/200
235/235 [==============================] - 2s 9ms/step - loss: 5.5452e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9759
Epoch 194/200
235/235 [==============================] - 2s 9ms/step - loss: 5.4022e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9759
Epoch 195/200
235/235 [==============================] - 2s 9ms/step - loss: 5.3684e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9759
Epoch 196/200
235/235 [==============================] - 2s 9ms/step - loss: 5.2730e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9760
Epoch 197/200
235/235 [==============================] - 2s 9ms/step - loss: 5.2253e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9760
Epoch 198/200
235/235 [==============================] - 2s 9ms/step - loss: 5.1598e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9760
Epoch 199/200
235/235 [==============================] - 2s 9ms/step - loss: 5.0684e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9760
Epoch 200/200
235/235 [==============================] - 2s 9ms/step - loss: 4.9571e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9760
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.03726593032479286
Thresholhold 0.020722776651382446
Using suggest threshold.
Applying new mask
Percentage zeros 0.2775
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 ...
 [1. 0. 0. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.0608268603682518
Thresholhold -0.09495096653699875
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.11545035243034363
Thresholhold 0.16133734583854675
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
  5/235 [..............................] - ETA: 3s - loss: 7.6018 - accuracy: 0.4187     WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0138s vs `on_train_batch_begin` time: 11.1363s). Check your callbacks.
235/235 [==============================] - 71s 14ms/step - loss: 2.2008 - accuracy: 0.9227 - val_loss: 1.6339 - val_accuracy: 0.9025
[-3.4934537e-08 -1.1419273e-07  2.6109504e-07 ... -0.0000000e+00
 -1.3341206e-01  0.0000000e+00]
Sparsity at: 0.24706235912847482
Epoch 2/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4417 - accuracy: 0.9603 - val_loss: 0.5500 - val_accuracy: 0.9432
[-3.9394123e-13 -1.3250265e-13  1.2746615e-12 ... -0.0000000e+00
 -1.0383350e-01  0.0000000e+00]
Sparsity at: 0.24706235912847482
Epoch 3/500
235/235 [==============================] - 4s 16ms/step - loss: 0.3054 - accuracy: 0.9657 - val_loss: 0.3533 - val_accuracy: 0.9449
[ 6.0108646e-19 -7.2390489e-19  3.5585930e-18 ... -0.0000000e+00
 -9.5932662e-02  0.0000000e+00]
Sparsity at: 0.24706235912847482
Epoch 4/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2715 - accuracy: 0.9678 - val_loss: 0.3059 - val_accuracy: 0.9507
[ 8.5233336e-24  1.0058221e-24 -2.3831149e-23 ... -0.0000000e+00
 -8.7623201e-02  0.0000000e+00]
Sparsity at: 0.24706235912847482
Epoch 5/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2559 - accuracy: 0.9681 - val_loss: 0.2840 - val_accuracy: 0.9584
[-2.1679155e-29  5.2759388e-29 -5.7082409e-29 ...  0.0000000e+00
 -8.0072187e-02  0.0000000e+00]
Sparsity at: 0.24706235912847482
Epoch 6/500
235/235 [==============================] - 4s 15ms/step - loss: 0.2433 - accuracy: 0.9699 - val_loss: 0.2883 - val_accuracy: 0.9538
[ 1.3988824e-34  4.8135736e-34  7.1390720e-34 ... -0.0000000e+00
 -7.5122505e-02  0.0000000e+00]
Sparsity at: 0.24706235912847482
Epoch 7/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2339 - accuracy: 0.9707 - val_loss: 0.3604 - val_accuracy: 0.9263
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.9691218e-02  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 8/500
235/235 [==============================] - 4s 15ms/step - loss: 0.2222 - accuracy: 0.9725 - val_loss: 0.2502 - val_accuracy: 0.9607
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.4845793e-02  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 9/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2128 - accuracy: 0.9730 - val_loss: 0.2431 - val_accuracy: 0.9612
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.8598582e-02  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 10/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2090 - accuracy: 0.9730 - val_loss: 0.2701 - val_accuracy: 0.9544
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.5561662e-02  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 11/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2048 - accuracy: 0.9736 - val_loss: 0.2654 - val_accuracy: 0.9530
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.6279004e-02  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 12/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2000 - accuracy: 0.9739 - val_loss: 0.2505 - val_accuracy: 0.9553
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.0788168e-02  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 13/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1936 - accuracy: 0.9743 - val_loss: 0.2363 - val_accuracy: 0.9585
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.6563486e-02  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 14/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1924 - accuracy: 0.9735 - val_loss: 0.2413 - val_accuracy: 0.9554
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.0205315e-02  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 15/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1859 - accuracy: 0.9748 - val_loss: 0.2137 - val_accuracy: 0.9652
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.1632353e-03  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 16/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1850 - accuracy: 0.9747 - val_loss: 0.2520 - val_accuracy: 0.9524
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.3819027e-03  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 17/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1819 - accuracy: 0.9759 - val_loss: 0.2593 - val_accuracy: 0.9491: 0.1829 
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -9.3547907e-03  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 18/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1795 - accuracy: 0.9753 - val_loss: 0.2383 - val_accuracy: 0.9568
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.9037499e-03  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 19/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1777 - accuracy: 0.9752 - val_loss: 0.2311 - val_accuracy: 0.9565
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  9.1937027e-04  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 20/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1754 - accuracy: 0.9749 - val_loss: 0.2151 - val_accuracy: 0.9621
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.0056236e-03  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 21/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1711 - accuracy: 0.9760 - val_loss: 0.2075 - val_accuracy: 0.9646
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.4182271e-03  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 22/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1726 - accuracy: 0.9749 - val_loss: 0.2215 - val_accuracy: 0.9625
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.3562939e-04  0.0000000e+00]
Sparsity at: 0.24706611570247933
Epoch 23/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1692 - accuracy: 0.9758 - val_loss: 0.2202 - val_accuracy: 0.9609
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  5.3756922e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 24/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1713 - accuracy: 0.9754 - val_loss: 0.2036 - val_accuracy: 0.9644
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.0112582e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 25/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1671 - accuracy: 0.9765 - val_loss: 0.2502 - val_accuracy: 0.9491
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.1671721e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 26/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1648 - accuracy: 0.9768 - val_loss: 0.2102 - val_accuracy: 0.9620
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.7522077e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 27/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1686 - accuracy: 0.9757 - val_loss: 0.2282 - val_accuracy: 0.9597
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.8454020e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 28/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1633 - accuracy: 0.9765 - val_loss: 0.2141 - val_accuracy: 0.9589
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.1984252e-03 -0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 29/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1639 - accuracy: 0.9761 - val_loss: 0.2382 - val_accuracy: 0.9549
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.7747652e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 30/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1632 - accuracy: 0.9766 - val_loss: 0.2238 - val_accuracy: 0.9585
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -8.5075060e-04  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 31/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1615 - accuracy: 0.9763 - val_loss: 0.1989 - val_accuracy: 0.9641
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.2501156e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 32/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1638 - accuracy: 0.9757 - val_loss: 0.2184 - val_accuracy: 0.9607
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.9345551e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 33/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1607 - accuracy: 0.9771 - val_loss: 0.1967 - val_accuracy: 0.9655
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.5447971e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 34/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1593 - accuracy: 0.9769 - val_loss: 0.2130 - val_accuracy: 0.9623
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.3834254e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 35/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1606 - accuracy: 0.9765 - val_loss: 0.2156 - val_accuracy: 0.9602
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.0499612e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 36/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1577 - accuracy: 0.9763 - val_loss: 0.2184 - val_accuracy: 0.9574
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.2691919e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 37/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1587 - accuracy: 0.9762 - val_loss: 0.1998 - val_accuracy: 0.9646
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.4836230e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 38/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1593 - accuracy: 0.9768 - val_loss: 0.2038 - val_accuracy: 0.9632
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.5866872e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 39/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1545 - accuracy: 0.9771 - val_loss: 0.2162 - val_accuracy: 0.9605
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.7717341e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 40/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1570 - accuracy: 0.9771 - val_loss: 0.2628 - val_accuracy: 0.9454
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.3572663e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 41/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1571 - accuracy: 0.9769 - val_loss: 0.2181 - val_accuracy: 0.9595
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.5019919e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 42/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1535 - accuracy: 0.9777 - val_loss: 0.2283 - val_accuracy: 0.9524
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.1530629e-02  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 43/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1532 - accuracy: 0.9779 - val_loss: 0.2165 - val_accuracy: 0.9581
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.2825965e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 44/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1538 - accuracy: 0.9773 - val_loss: 0.2055 - val_accuracy: 0.9626
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.0874640e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 45/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1531 - accuracy: 0.9781 - val_loss: 0.2030 - val_accuracy: 0.9626
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.6134688e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 46/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1546 - accuracy: 0.9768 - val_loss: 0.1999 - val_accuracy: 0.9636
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.2520741e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 47/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1510 - accuracy: 0.9778 - val_loss: 0.1979 - val_accuracy: 0.9630
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.4756119e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 48/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1512 - accuracy: 0.9768 - val_loss: 0.2025 - val_accuracy: 0.9616
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.1243339e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 49/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1548 - accuracy: 0.9765 - val_loss: 0.2188 - val_accuracy: 0.9601
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.4084835e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 50/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1517 - accuracy: 0.9782 - val_loss: 0.2193 - val_accuracy: 0.9560
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.2754159e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 3.11443741857911e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.2775
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 ...
 [1. 0. 0. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.00010634585146621078
Thresholhold -5.833261820953339e-05
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.01770162130969144
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 209s 13ms/step - loss: 0.1514 - accuracy: 0.9771 - val_loss: 0.2123 - val_accuracy: 0.9579
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.3187897e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 52/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1494 - accuracy: 0.9786 - val_loss: 0.2294 - val_accuracy: 0.9575
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.2013354e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 53/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1464 - accuracy: 0.9791 - val_loss: 0.2306 - val_accuracy: 0.9560
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -8.1838742e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 54/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1548 - accuracy: 0.9764 - val_loss: 0.2206 - val_accuracy: 0.9585
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.3704723e-03  0.0000000e+00]
Sparsity at: 0.24706987227648386
Epoch 55/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9784 - val_loss: 0.2049 - val_accuracy: 0.9607
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.0981226e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 56/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1524 - accuracy: 0.9776 - val_loss: 0.2009 - val_accuracy: 0.9638
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -9.9036321e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 57/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1496 - accuracy: 0.9781 - val_loss: 0.2472 - val_accuracy: 0.9480
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.4124332e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 58/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1495 - accuracy: 0.9780 - val_loss: 0.1942 - val_accuracy: 0.9638
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.9812107e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 59/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1456 - accuracy: 0.9789 - val_loss: 0.1957 - val_accuracy: 0.9669
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.6305955e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 60/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1491 - accuracy: 0.9779 - val_loss: 0.1935 - val_accuracy: 0.9659
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.7458041e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 61/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1489 - accuracy: 0.9781 - val_loss: 0.1985 - val_accuracy: 0.9634
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.7901105e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 62/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1460 - accuracy: 0.9791 - val_loss: 0.1874 - val_accuracy: 0.9681
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.8666169e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 63/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9781 - val_loss: 0.2019 - val_accuracy: 0.9610
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -9.6983503e-04  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 64/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1479 - accuracy: 0.9783 - val_loss: 0.2158 - val_accuracy: 0.9575
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.6440653e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 65/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1455 - accuracy: 0.9789 - val_loss: 0.2065 - val_accuracy: 0.9623
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.6598024e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 66/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1463 - accuracy: 0.9788 - val_loss: 0.2110 - val_accuracy: 0.9608
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.5956312e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 67/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1451 - accuracy: 0.9786 - val_loss: 0.2618 - val_accuracy: 0.9483
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.5059080e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 68/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1461 - accuracy: 0.9791 - val_loss: 0.2124 - val_accuracy: 0.9613
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.6270677e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 69/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9777 - val_loss: 0.1955 - val_accuracy: 0.9657
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.4302301e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 70/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1464 - accuracy: 0.9790 - val_loss: 0.2023 - val_accuracy: 0.9608
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  8.4237037e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 71/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1459 - accuracy: 0.9786 - val_loss: 0.1983 - val_accuracy: 0.9624
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 5.8640400e-04
 0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 72/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1439 - accuracy: 0.9789 - val_loss: 0.2219 - val_accuracy: 0.9564
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.7833997e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 73/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1488 - accuracy: 0.9768 - val_loss: 0.2202 - val_accuracy: 0.9573
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.1084672e-04 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 74/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1454 - accuracy: 0.9790 - val_loss: 0.2078 - val_accuracy: 0.9591
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.7799969e-03
 0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 75/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1454 - accuracy: 0.9785 - val_loss: 0.2152 - val_accuracy: 0.9592
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.0641828e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 76/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1478 - accuracy: 0.9776 - val_loss: 0.2364 - val_accuracy: 0.9536
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.3382218e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 77/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1470 - accuracy: 0.9780 - val_loss: 0.2323 - val_accuracy: 0.9529
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.7808198e-04
 0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 78/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1436 - accuracy: 0.9789 - val_loss: 0.2440 - val_accuracy: 0.9491
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.1133333e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 79/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1448 - accuracy: 0.9786 - val_loss: 0.2023 - val_accuracy: 0.9631
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.4986507e-05  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 80/500
235/235 [==============================] - 4s 18ms/step - loss: 0.1467 - accuracy: 0.9779 - val_loss: 0.1921 - val_accuracy: 0.9662
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.2541380e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 81/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1432 - accuracy: 0.9794 - val_loss: 0.1963 - val_accuracy: 0.9642
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.2242961e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 82/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1456 - accuracy: 0.9784 - val_loss: 0.2080 - val_accuracy: 0.9592
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.2049425e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 83/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9790 - val_loss: 0.2129 - val_accuracy: 0.9575
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.7972395e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 84/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1459 - accuracy: 0.9780 - val_loss: 0.2259 - val_accuracy: 0.9565
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.9458019e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 85/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1465 - accuracy: 0.9784 - val_loss: 0.2253 - val_accuracy: 0.9577
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.7476991e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 86/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1431 - accuracy: 0.9790 - val_loss: 0.2116 - val_accuracy: 0.9590
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.3705303e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 87/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1447 - accuracy: 0.9785 - val_loss: 0.2305 - val_accuracy: 0.9555
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.4280575e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 88/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1429 - accuracy: 0.9793 - val_loss: 0.2169 - val_accuracy: 0.9593
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.8564592e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 89/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1432 - accuracy: 0.9786 - val_loss: 0.1941 - val_accuracy: 0.9649
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.1159312e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 90/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1430 - accuracy: 0.9786 - val_loss: 0.1982 - val_accuracy: 0.9625
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.0037460e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 91/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9789 - val_loss: 0.2042 - val_accuracy: 0.9595
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.5703730e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 92/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1433 - accuracy: 0.9782 - val_loss: 0.2173 - val_accuracy: 0.9562
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.9945925e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 93/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1407 - accuracy: 0.9790 - val_loss: 0.2060 - val_accuracy: 0.9608
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.2079871e-04  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 94/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9790 - val_loss: 0.2057 - val_accuracy: 0.9593
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -8.6928289e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 95/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1432 - accuracy: 0.9789 - val_loss: 0.1992 - val_accuracy: 0.9627
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.5615275e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 96/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1403 - accuracy: 0.9789 - val_loss: 0.2480 - val_accuracy: 0.9504
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.2404095e-03 -0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 97/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1414 - accuracy: 0.9793 - val_loss: 0.2210 - val_accuracy: 0.9571
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.0043127e-03  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 98/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1422 - accuracy: 0.9787 - val_loss: 0.2025 - val_accuracy: 0.9615
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.0183817e-04  0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 99/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1414 - accuracy: 0.9795 - val_loss: 0.1967 - val_accuracy: 0.9631
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.1805954e-03
 0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 100/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1410 - accuracy: 0.9793 - val_loss: 0.1988 - val_accuracy: 0.9630
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.6555770e-03
 0.0000000e+00]
Sparsity at: 0.24707362885048836
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 4.122906951942523e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.2775
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 1. 1. 1.]
 ...
 [1. 0. 0. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 3.661272726674736e-05
Thresholhold 1.856839162428514e-07
Using suggest threshold.
Applying new mask
Percentage zeros 0.42693335
tf.Tensor(
[[1. 0. 1. ... 0. 0. 1.]
 [0. 0. 1. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.02442430927259509
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 259s 15ms/step - loss: 0.1424 - accuracy: 0.9787 - val_loss: 0.2137 - val_accuracy: 0.9588
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.6190293e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 102/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1399 - accuracy: 0.9792 - val_loss: 0.2070 - val_accuracy: 0.9612
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.2047195e-02  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 103/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1433 - accuracy: 0.9788 - val_loss: 0.2258 - val_accuracy: 0.9557
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.5016566e-04 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 104/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1404 - accuracy: 0.9790 - val_loss: 0.2019 - val_accuracy: 0.9629
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.2308168e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 105/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1419 - accuracy: 0.9786 - val_loss: 0.2208 - val_accuracy: 0.9553
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.5044698e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 106/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1406 - accuracy: 0.9795 - val_loss: 0.1921 - val_accuracy: 0.9628
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.2140858e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 107/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1414 - accuracy: 0.9790 - val_loss: 0.2213 - val_accuracy: 0.9536
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.0683170e-06  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 108/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1410 - accuracy: 0.9789 - val_loss: 0.2113 - val_accuracy: 0.9587
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.3053456e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 109/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1385 - accuracy: 0.9790 - val_loss: 0.2082 - val_accuracy: 0.9619
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.7220025e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 110/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1409 - accuracy: 0.9796 - val_loss: 0.2547 - val_accuracy: 0.9486
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.8190849e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 111/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1439 - accuracy: 0.9781 - val_loss: 0.1925 - val_accuracy: 0.9628
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.4573188e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 112/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1390 - accuracy: 0.9799 - val_loss: 0.1865 - val_accuracy: 0.9644
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.4481458e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 113/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1400 - accuracy: 0.9791 - val_loss: 0.1870 - val_accuracy: 0.9656
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.0912537e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 114/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1404 - accuracy: 0.9786 - val_loss: 0.2309 - val_accuracy: 0.9541
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.4038267e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 115/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1394 - accuracy: 0.9794 - val_loss: 0.2134 - val_accuracy: 0.9577
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.1599835e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 116/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.1906 - val_accuracy: 0.9649
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.4207626e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 117/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1446 - accuracy: 0.9779 - val_loss: 0.2384 - val_accuracy: 0.9507
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.1002485e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 118/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1397 - accuracy: 0.9790 - val_loss: 0.2140 - val_accuracy: 0.9584
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.6717114e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 119/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1385 - accuracy: 0.9794 - val_loss: 0.1945 - val_accuracy: 0.9611
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.0613670e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 120/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1380 - accuracy: 0.9798 - val_loss: 0.2029 - val_accuracy: 0.9616
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.1049351e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 121/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1372 - accuracy: 0.9790 - val_loss: 0.2113 - val_accuracy: 0.9591
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.6375917e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 122/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1367 - accuracy: 0.9795 - val_loss: 0.1939 - val_accuracy: 0.9652
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.3219380e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 123/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1385 - accuracy: 0.9799 - val_loss: 0.1998 - val_accuracy: 0.9623
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.7361452e-04  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 124/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1382 - accuracy: 0.9801 - val_loss: 0.1978 - val_accuracy: 0.9611
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  7.8910717e-04  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 125/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1422 - accuracy: 0.9786 - val_loss: 0.1801 - val_accuracy: 0.9658
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.9103604e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 126/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1393 - accuracy: 0.9789 - val_loss: 0.1917 - val_accuracy: 0.9653
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.1409272e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 127/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1374 - accuracy: 0.9804 - val_loss: 0.1930 - val_accuracy: 0.9630
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.5913878e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 128/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1352 - accuracy: 0.9801 - val_loss: 0.1994 - val_accuracy: 0.9606
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.1284190e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 129/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9804 - val_loss: 0.2092 - val_accuracy: 0.9584
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.4070538e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 130/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1409 - accuracy: 0.9793 - val_loss: 0.2033 - val_accuracy: 0.9615
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.1309329e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 131/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1363 - accuracy: 0.9798 - val_loss: 0.1907 - val_accuracy: 0.9631
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.9956185e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 132/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1343 - accuracy: 0.9805 - val_loss: 0.1961 - val_accuracy: 0.9637
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.6847834e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 133/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1365 - accuracy: 0.9796 - val_loss: 0.1894 - val_accuracy: 0.9658
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.6326773e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 134/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1382 - accuracy: 0.9797 - val_loss: 0.2115 - val_accuracy: 0.9587
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.2767093e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 135/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9801 - val_loss: 0.2047 - val_accuracy: 0.9609
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.3010508e-04  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 136/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1377 - accuracy: 0.9792 - val_loss: 0.1945 - val_accuracy: 0.9630
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.6367331e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 137/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1372 - accuracy: 0.9801 - val_loss: 0.2004 - val_accuracy: 0.9605
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.3808409e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 138/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1367 - accuracy: 0.9801 - val_loss: 0.2018 - val_accuracy: 0.9611
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.3965506e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 139/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1367 - accuracy: 0.9797 - val_loss: 0.2066 - val_accuracy: 0.9597
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.6131141e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 140/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1372 - accuracy: 0.9795 - val_loss: 0.1909 - val_accuracy: 0.9633
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.6020334e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 141/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9795 - val_loss: 0.2272 - val_accuracy: 0.9544
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.0293133e-02 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 142/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1398 - accuracy: 0.9786 - val_loss: 0.2467 - val_accuracy: 0.9501
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.4314309e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 143/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1392 - accuracy: 0.9791 - val_loss: 0.1846 - val_accuracy: 0.9670
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.8391220e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 144/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9810 - val_loss: 0.1963 - val_accuracy: 0.9613
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.7051181e-04 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 145/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1354 - accuracy: 0.9799 - val_loss: 0.2164 - val_accuracy: 0.9571
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.2889595e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 146/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1364 - accuracy: 0.9794 - val_loss: 0.2013 - val_accuracy: 0.9620
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.2592855e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 147/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9799 - val_loss: 0.1905 - val_accuracy: 0.9646
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.4929804e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 148/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1370 - accuracy: 0.9793 - val_loss: 0.2195 - val_accuracy: 0.9578
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.4266124e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 149/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1385 - accuracy: 0.9794 - val_loss: 0.1930 - val_accuracy: 0.9639
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.5238187e-03 -0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 150/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9799 - val_loss: 0.2034 - val_accuracy: 0.9606
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.7608323e-03  0.0000000e+00]
Sparsity at: 0.2951878287002254
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 5.1083703888624145e-34
Thresholhold 1.398882358516678e-34
Using suggest threshold.
Applying new mask
Percentage zeros 0.43816325
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [1. 0. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.00043749415078420464
Thresholhold -0.00010349297372158617
Using suggest threshold.
Applying new mask
Percentage zeros 0.42693335
tf.Tensor(
[[1. 0. 1. ... 0. 0. 1.]
 [0. 0. 1. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 0. 1. 0.]
 [1. 0. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.04167799063760702
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 240s 13ms/step - loss: 0.1339 - accuracy: 0.9799 - val_loss: 0.2133 - val_accuracy: 0.9573
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.5538598e-04
 0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 152/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1350 - accuracy: 0.9800 - val_loss: 0.1801 - val_accuracy: 0.9665
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.1424405e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 153/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9800 - val_loss: 0.2063 - val_accuracy: 0.9606
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.6918487e-03  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 154/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9805 - val_loss: 0.2045 - val_accuracy: 0.9600
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  7.9647638e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 155/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9802 - val_loss: 0.1785 - val_accuracy: 0.9677
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.3168419e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 156/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1348 - accuracy: 0.9801 - val_loss: 0.1980 - val_accuracy: 0.9638
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.5486054e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 157/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1342 - accuracy: 0.9805 - val_loss: 0.2258 - val_accuracy: 0.9555
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.2501264e-04  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 158/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1390 - accuracy: 0.9789 - val_loss: 0.2050 - val_accuracy: 0.9586
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  4.7099762e-05 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 159/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9805 - val_loss: 0.1899 - val_accuracy: 0.9645
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.8772400e-06 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 160/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1359 - accuracy: 0.9801 - val_loss: 0.2060 - val_accuracy: 0.9611
[ 1.39888236e-34  4.81357360e-34  4.61340023e-34 ... -0.00000000e+00
 -1.01768135e-04  0.00000000e+00]
Sparsity at: 0.437129977460556
Epoch 161/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1338 - accuracy: 0.9803 - val_loss: 0.1966 - val_accuracy: 0.9632
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -7.5908692e-04  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 162/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9801 - val_loss: 0.2166 - val_accuracy: 0.9561
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.2618256e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 163/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9794 - val_loss: 0.1957 - val_accuracy: 0.9652
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  4.5243805e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 164/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9808 - val_loss: 0.2250 - val_accuracy: 0.9539
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 8.5082080e-05
 0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 165/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1375 - accuracy: 0.9794 - val_loss: 0.2121 - val_accuracy: 0.9599
[ 1.39888236e-34  4.81357360e-34  4.61340023e-34 ...  0.00000000e+00
 -1.19433935e-05 -0.00000000e+00]
Sparsity at: 0.437129977460556
Epoch 166/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1370 - accuracy: 0.9795 - val_loss: 0.2540 - val_accuracy: 0.9475
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.7680637e-06  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 167/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9803 - val_loss: 0.2080 - val_accuracy: 0.9602
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.5087634e-07  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 168/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1374 - accuracy: 0.9797 - val_loss: 0.2182 - val_accuracy: 0.9564
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.8471492e-07  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 169/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9795 - val_loss: 0.2165 - val_accuracy: 0.9575
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.5552089e-07 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 170/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9800 - val_loss: 0.1757 - val_accuracy: 0.9681
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.3312129e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 171/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1332 - accuracy: 0.9803 - val_loss: 0.2058 - val_accuracy: 0.9595
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.0961806e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 172/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9807 - val_loss: 0.2070 - val_accuracy: 0.9591
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.8697976e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 173/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1362 - accuracy: 0.9800 - val_loss: 0.1895 - val_accuracy: 0.9643
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.6966951e-03
 0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 174/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9799 - val_loss: 0.2090 - val_accuracy: 0.9594
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.2618344e-03  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 175/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9801 - val_loss: 0.2152 - val_accuracy: 0.9592
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.0913042e-04  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 176/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9803 - val_loss: 0.2298 - val_accuracy: 0.9562
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  8.2144630e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 177/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1332 - accuracy: 0.9806 - val_loss: 0.2449 - val_accuracy: 0.9476
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.7857473e-03
 0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 178/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1336 - accuracy: 0.9804 - val_loss: 0.2123 - val_accuracy: 0.9584
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.8535372e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 179/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9799 - val_loss: 0.2214 - val_accuracy: 0.9570
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.7287687e-04  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 180/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9806 - val_loss: 0.2264 - val_accuracy: 0.9551
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.5616220e-03  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 181/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9804 - val_loss: 0.1963 - val_accuracy: 0.9625
[ 1.39888236e-34  4.81357360e-34  4.61340023e-34 ...  0.00000000e+00
 -1.03262115e-04 -0.00000000e+00]
Sparsity at: 0.437129977460556
Epoch 182/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1326 - accuracy: 0.9806 - val_loss: 0.2075 - val_accuracy: 0.9623
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.3878462e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 183/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9805 - val_loss: 0.2103 - val_accuracy: 0.9574
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.2966636e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 184/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9802 - val_loss: 0.2104 - val_accuracy: 0.9601
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.0827970e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 185/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1338 - accuracy: 0.9806 - val_loss: 0.2045 - val_accuracy: 0.9604
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.7618227e-03  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 186/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9795 - val_loss: 0.2080 - val_accuracy: 0.9581
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.4654805e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 187/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9805 - val_loss: 0.2290 - val_accuracy: 0.9548
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.1959257e-04  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 188/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1383 - accuracy: 0.9792 - val_loss: 0.2375 - val_accuracy: 0.9517
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.1622006e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 189/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1371 - accuracy: 0.9794 - val_loss: 0.2000 - val_accuracy: 0.9620
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.0593296e-03  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 190/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1333 - accuracy: 0.9808 - val_loss: 0.2139 - val_accuracy: 0.9596
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.4119891e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 191/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9803 - val_loss: 0.1924 - val_accuracy: 0.9642
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.5651505e-05  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 192/500
235/235 [==============================] - 4s 19ms/step - loss: 0.1362 - accuracy: 0.9791 - val_loss: 0.2293 - val_accuracy: 0.9551
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.6354783e-03
 0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 193/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9802 - val_loss: 0.2152 - val_accuracy: 0.9574
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  9.7854389e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 194/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9811 - val_loss: 0.2007 - val_accuracy: 0.9606
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.6802651e-04  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 195/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9807 - val_loss: 0.1978 - val_accuracy: 0.9604
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.0464070e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 196/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1358 - accuracy: 0.9799 - val_loss: 0.2315 - val_accuracy: 0.9523
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.9961939e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 197/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1336 - accuracy: 0.9811 - val_loss: 0.2001 - val_accuracy: 0.9622
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.0716910e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 198/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9807 - val_loss: 0.1952 - val_accuracy: 0.9626
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.2502997e-04  0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 199/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9795 - val_loss: 0.2141 - val_accuracy: 0.9580
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.6066333e-03 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 200/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9809 - val_loss: 0.2069 - val_accuracy: 0.9592
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.3081696e-04 -0.0000000e+00]
Sparsity at: 0.437129977460556
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 1.7790788624055115e-05
Thresholhold 1.398882358516678e-34
Using suggest threshold.
Applying new mask
Percentage zeros 0.43911564
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [1. 0. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.0032467699331542033
Thresholhold 6.794191904191393e-06
Using suggest threshold.
Applying new mask
Percentage zeros 0.7631
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 0.]
 [0. 0. 1. ... 0. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.05935055366467168
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 172s 12ms/step - loss: 0.1339 - accuracy: 0.9799 - val_loss: 0.2166 - val_accuracy: 0.9563
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.0841874e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 202/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1355 - accuracy: 0.9794 - val_loss: 0.1834 - val_accuracy: 0.9646
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.5259359e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9794 - val_loss: 0.2174 - val_accuracy: 0.9555
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.0823895e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9801 - val_loss: 0.2080 - val_accuracy: 0.9582
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.3064937e-04  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9800 - val_loss: 0.1991 - val_accuracy: 0.9615
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.7059729e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9805 - val_loss: 0.1786 - val_accuracy: 0.9676
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.9548881e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9797 - val_loss: 0.2110 - val_accuracy: 0.9586
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.7415751e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9806 - val_loss: 0.1881 - val_accuracy: 0.9637
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  5.4346700e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9802 - val_loss: 0.2092 - val_accuracy: 0.9589
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.8529620e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9808 - val_loss: 0.1746 - val_accuracy: 0.9690
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.1761585e-05  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9798 - val_loss: 0.1863 - val_accuracy: 0.9658
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.3021492e-05  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9800 - val_loss: 0.2157 - val_accuracy: 0.9561
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.9737682e-05  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9801 - val_loss: 0.2052 - val_accuracy: 0.9604
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.3344270e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9802 - val_loss: 0.2737 - val_accuracy: 0.9426
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.3173285e-04  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9796 - val_loss: 0.2023 - val_accuracy: 0.9618
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.5292045e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9792 - val_loss: 0.2392 - val_accuracy: 0.9513
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  5.1748141e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9801 - val_loss: 0.2030 - val_accuracy: 0.9602
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.5715660e-05  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9795 - val_loss: 0.1892 - val_accuracy: 0.9637
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.3138834e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9806 - val_loss: 0.1886 - val_accuracy: 0.9644
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.0561539e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9802 - val_loss: 0.2424 - val_accuracy: 0.9461
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.0211793e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9797 - val_loss: 0.2055 - val_accuracy: 0.9599
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.6267926e-03  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9800 - val_loss: 0.1950 - val_accuracy: 0.9635
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.1458444e-03
 0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9794 - val_loss: 0.1932 - val_accuracy: 0.9616
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.2837814e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9799 - val_loss: 0.2111 - val_accuracy: 0.9589
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.2420901e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 225/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1362 - accuracy: 0.9789 - val_loss: 0.1898 - val_accuracy: 0.9639
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.1019445e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 226/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1335 - accuracy: 0.9797 - val_loss: 0.2075 - val_accuracy: 0.9591
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 7.2819757e-04
 0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 227/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1320 - accuracy: 0.9805 - val_loss: 0.2158 - val_accuracy: 0.9572
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  7.4344169e-04  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9797 - val_loss: 0.1888 - val_accuracy: 0.9629
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.0737314e-03  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9793 - val_loss: 0.1932 - val_accuracy: 0.9649
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.1472593e-03  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9804 - val_loss: 0.1932 - val_accuracy: 0.9634
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.9556246e-04  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9793 - val_loss: 0.1940 - val_accuracy: 0.9648
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.8840354e-04
 0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9808 - val_loss: 0.2125 - val_accuracy: 0.9577
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.3333550e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.1955 - val_accuracy: 0.9636
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.1824080e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9795 - val_loss: 0.2074 - val_accuracy: 0.9611
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.2734474e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9804 - val_loss: 0.1925 - val_accuracy: 0.9635
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.1261896e-03  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 236/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9789 - val_loss: 0.2007 - val_accuracy: 0.9632
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 5.3408754e-04
 0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9811 - val_loss: 0.2048 - val_accuracy: 0.9597
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.1480955e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9787 - val_loss: 0.1980 - val_accuracy: 0.9612
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.6401295e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9792 - val_loss: 0.1823 - val_accuracy: 0.9650
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.6325749e-03
 0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9801 - val_loss: 0.1774 - val_accuracy: 0.9675
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.7101139e-04  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9798 - val_loss: 0.1876 - val_accuracy: 0.9637
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.8466971e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9803 - val_loss: 0.1962 - val_accuracy: 0.9605
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.5662089e-04  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9797 - val_loss: 0.2053 - val_accuracy: 0.9579
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.6425705e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9795 - val_loss: 0.1863 - val_accuracy: 0.9656
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.6272781e-05 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 245/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1308 - accuracy: 0.9803 - val_loss: 0.1848 - val_accuracy: 0.9646
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.5862150e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9796 - val_loss: 0.1713 - val_accuracy: 0.9668
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.0402581e-03 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9798 - val_loss: 0.1954 - val_accuracy: 0.9598
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.0120177e-03  0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 248/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1324 - accuracy: 0.9797 - val_loss: 0.1850 - val_accuracy: 0.9660
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.3139959e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9808 - val_loss: 0.1752 - val_accuracy: 0.9678
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  4.7803303e-04 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9797 - val_loss: 0.2028 - val_accuracy: 0.9606
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.1050567e-05 -0.0000000e+00]
Sparsity at: 0.4758564988730278
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.003530736334766582
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.43911564
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [1. 0. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.0119226070366939
Thresholhold 6.476342150563141e-06
Using suggest threshold.
Applying new mask
Percentage zeros 0.87166667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.07383174323319519
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 208s 12ms/step - loss: 0.1342 - accuracy: 0.9794 - val_loss: 0.1843 - val_accuracy: 0.9650
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.6426386e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 252/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1302 - accuracy: 0.9799 - val_loss: 0.2053 - val_accuracy: 0.9598
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.3525995e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 253/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1320 - accuracy: 0.9802 - val_loss: 0.2091 - val_accuracy: 0.9597
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.4704706e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9802 - val_loss: 0.2004 - val_accuracy: 0.9609
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.4491310e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9813 - val_loss: 0.1802 - val_accuracy: 0.9659
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -9.2557384e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9798 - val_loss: 0.2079 - val_accuracy: 0.9598
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.4840075e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9787 - val_loss: 0.2022 - val_accuracy: 0.9605
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.3169934e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9811 - val_loss: 0.2116 - val_accuracy: 0.9576
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.4805001e-03
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9796 - val_loss: 0.1963 - val_accuracy: 0.9644
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.9985343e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9807 - val_loss: 0.1760 - val_accuracy: 0.9669
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.0512780e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9811 - val_loss: 0.1855 - val_accuracy: 0.9648
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.6194466e-07
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9798 - val_loss: 0.1899 - val_accuracy: 0.9655
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.8897397e-06 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9806 - val_loss: 0.2073 - val_accuracy: 0.9579
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.2807987e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9793 - val_loss: 0.2038 - val_accuracy: 0.9619
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.1697434e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9802 - val_loss: 0.1916 - val_accuracy: 0.9616
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  6.7746972e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9803 - val_loss: 0.1766 - val_accuracy: 0.9683
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.3367013e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9810 - val_loss: 0.1918 - val_accuracy: 0.9645
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.9967096e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1300 - accuracy: 0.9805 - val_loss: 0.1791 - val_accuracy: 0.9665
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.6408228e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9802 - val_loss: 0.1946 - val_accuracy: 0.9628
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.0519116e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9803 - val_loss: 0.1772 - val_accuracy: 0.9663
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.4505130e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1300 - accuracy: 0.9803 - val_loss: 0.1834 - val_accuracy: 0.9665
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.9567480e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9807 - val_loss: 0.1796 - val_accuracy: 0.9678
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.6778932e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1304 - accuracy: 0.9804 - val_loss: 0.1763 - val_accuracy: 0.9665
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.2897531e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9794 - val_loss: 0.2109 - val_accuracy: 0.9573
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  4.2628719e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9789 - val_loss: 0.2013 - val_accuracy: 0.9582
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.1662475e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9803 - val_loss: 0.1947 - val_accuracy: 0.9616
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.1107063e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9801 - val_loss: 0.1976 - val_accuracy: 0.9613
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -8.8958346e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9790 - val_loss: 0.1847 - val_accuracy: 0.9656
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.4006325e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9800 - val_loss: 0.1996 - val_accuracy: 0.9619
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.9275221e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9811 - val_loss: 0.1878 - val_accuracy: 0.9644
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.7257169e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9803 - val_loss: 0.2015 - val_accuracy: 0.9601
[ 1.39888236e-34  4.81357360e-34  4.61340023e-34 ... -0.00000000e+00
  1.20821096e-04  0.00000000e+00]
Sparsity at: 0.48809166040571
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9808 - val_loss: 0.2022 - val_accuracy: 0.9576
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.8836462e-06 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9793 - val_loss: 0.2103 - val_accuracy: 0.9610
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.9455225e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9797 - val_loss: 0.2077 - val_accuracy: 0.9593
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.2685582e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9804 - val_loss: 0.2023 - val_accuracy: 0.9589
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.1617626e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9794 - val_loss: 0.1923 - val_accuracy: 0.9635
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.7860759e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9804 - val_loss: 0.1938 - val_accuracy: 0.9628
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.1265378e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9797 - val_loss: 0.2086 - val_accuracy: 0.9584
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.9538085e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9806 - val_loss: 0.2098 - val_accuracy: 0.9607
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.5548644e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 290/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1327 - accuracy: 0.9800 - val_loss: 0.1736 - val_accuracy: 0.9680
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.5330020e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9803 - val_loss: 0.1889 - val_accuracy: 0.9653
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.3386445e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9816 - val_loss: 0.1904 - val_accuracy: 0.9649
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.6362326e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9784 - val_loss: 0.1946 - val_accuracy: 0.9638
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  8.6859782e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9803 - val_loss: 0.2159 - val_accuracy: 0.9579
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.9776520e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9811 - val_loss: 0.1759 - val_accuracy: 0.9661
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.2655028e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9812 - val_loss: 0.2086 - val_accuracy: 0.9602
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.7434205e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9808 - val_loss: 0.1944 - val_accuracy: 0.9620
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.1435795e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9808 - val_loss: 0.2189 - val_accuracy: 0.9545
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.3751520e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9798 - val_loss: 0.2024 - val_accuracy: 0.9610
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  6.1064452e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9812 - val_loss: 0.2119 - val_accuracy: 0.9585
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.0458655e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.010878022520181663
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.43911564
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [1. 0. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.022533535368353563
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.87166667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.08570467849509633
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 245s 12ms/step - loss: 0.1320 - accuracy: 0.9798 - val_loss: 0.2068 - val_accuracy: 0.9589
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.0287430e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 302/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1300 - accuracy: 0.9804 - val_loss: 0.2070 - val_accuracy: 0.9577
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  8.2219206e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9810 - val_loss: 0.2098 - val_accuracy: 0.9601
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.8300686e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 304/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1316 - accuracy: 0.9796 - val_loss: 0.2109 - val_accuracy: 0.9585
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.3620086e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9799 - val_loss: 0.1815 - val_accuracy: 0.9671
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.1677590e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9805 - val_loss: 0.1864 - val_accuracy: 0.9642
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.5538680e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9800 - val_loss: 0.2154 - val_accuracy: 0.9572
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.8722789e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9802 - val_loss: 0.2025 - val_accuracy: 0.9612
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.2759237e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9801 - val_loss: 0.1985 - val_accuracy: 0.9631
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.0076985e-03
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9801 - val_loss: 0.1962 - val_accuracy: 0.9639
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.1087283e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9815 - val_loss: 0.2109 - val_accuracy: 0.9581
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.2178175e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9806 - val_loss: 0.2034 - val_accuracy: 0.9604
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.4352013e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9800 - val_loss: 0.1925 - val_accuracy: 0.9620
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -8.4792264e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9811 - val_loss: 0.1966 - val_accuracy: 0.9621
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.2862558e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9795 - val_loss: 0.2004 - val_accuracy: 0.9608
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.6395365e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9808 - val_loss: 0.2006 - val_accuracy: 0.9607
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  9.3596835e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9801 - val_loss: 0.2293 - val_accuracy: 0.9532
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.3580963e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9806 - val_loss: 0.1987 - val_accuracy: 0.9603
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.8772028e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1299 - accuracy: 0.9805 - val_loss: 0.1858 - val_accuracy: 0.9654
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.3259499e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1262 - accuracy: 0.9812 - val_loss: 0.2389 - val_accuracy: 0.9523
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.0428694e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9796 - val_loss: 0.2031 - val_accuracy: 0.9600
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.8978580e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9787 - val_loss: 0.1871 - val_accuracy: 0.9656
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.0846011e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1304 - accuracy: 0.9803 - val_loss: 0.2017 - val_accuracy: 0.9618
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.3994758e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9810 - val_loss: 0.2027 - val_accuracy: 0.9613
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.1443392e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9794 - val_loss: 0.1805 - val_accuracy: 0.9650
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.5654352e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9807 - val_loss: 0.1783 - val_accuracy: 0.9682
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.3012943e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9798 - val_loss: 0.1998 - val_accuracy: 0.9608
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.5866906e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9805 - val_loss: 0.1827 - val_accuracy: 0.9650
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.4256855e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9812 - val_loss: 0.1876 - val_accuracy: 0.9646
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.4135535e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9804 - val_loss: 0.1790 - val_accuracy: 0.9669
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.1074486e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 331/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1261 - accuracy: 0.9808 - val_loss: 0.2042 - val_accuracy: 0.9609
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.8034944e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9804 - val_loss: 0.1871 - val_accuracy: 0.9636
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  9.6679345e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9810 - val_loss: 0.1991 - val_accuracy: 0.9624
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.8885365e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9808 - val_loss: 0.1957 - val_accuracy: 0.9607
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.3094368e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9797 - val_loss: 0.1938 - val_accuracy: 0.9624
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.1122239e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9792 - val_loss: 0.2322 - val_accuracy: 0.9548
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.9193843e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9806 - val_loss: 0.1825 - val_accuracy: 0.9664
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.0637764e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9804 - val_loss: 0.1996 - val_accuracy: 0.9616
[ 1.39888236e-34  4.81357360e-34  4.61340023e-34 ... -0.00000000e+00
 -1.10249166e-04 -0.00000000e+00]
Sparsity at: 0.48809166040571
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9807 - val_loss: 0.1942 - val_accuracy: 0.9639
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.5398652e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9801 - val_loss: 0.1996 - val_accuracy: 0.9616
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.2029074e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1263 - accuracy: 0.9810 - val_loss: 0.2385 - val_accuracy: 0.9517
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 6.9186452e-04
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9811 - val_loss: 0.2367 - val_accuracy: 0.9535
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.5352000e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9796 - val_loss: 0.1966 - val_accuracy: 0.9628
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.6529242e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9802 - val_loss: 0.1841 - val_accuracy: 0.9653
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.2776029e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9804 - val_loss: 0.2268 - val_accuracy: 0.9551
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.3630217e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9798 - val_loss: 0.1876 - val_accuracy: 0.9642
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.4473923e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9799 - val_loss: 0.1808 - val_accuracy: 0.9649
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -8.5819709e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9797 - val_loss: 0.1893 - val_accuracy: 0.9659
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.0475174e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9804 - val_loss: 0.1844 - val_accuracy: 0.9663
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.0007545e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9803 - val_loss: 0.1860 - val_accuracy: 0.9655
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.3572280e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.017323238427237486
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.43911564
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [1. 0. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.03224054172972535
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.87166667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.09469104772470516
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
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 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
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 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
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 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 244s 12ms/step - loss: 0.1272 - accuracy: 0.9809 - val_loss: 0.1909 - val_accuracy: 0.9640
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.4577816e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 352/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1274 - accuracy: 0.9807 - val_loss: 0.2014 - val_accuracy: 0.9627
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.1046820e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9795 - val_loss: 0.2078 - val_accuracy: 0.9590
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.4049340e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 354/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1274 - accuracy: 0.9804 - val_loss: 0.1845 - val_accuracy: 0.9654
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -9.9451099e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9792 - val_loss: 0.2041 - val_accuracy: 0.9589
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.5704495e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9811 - val_loss: 0.2001 - val_accuracy: 0.9601
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.9935438e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9800 - val_loss: 0.1957 - val_accuracy: 0.9627
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.0952558e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9800 - val_loss: 0.1820 - val_accuracy: 0.9653
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.8357998e-06 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1262 - accuracy: 0.9812 - val_loss: 0.1940 - val_accuracy: 0.9624
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.4250965e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9808 - val_loss: 0.2013 - val_accuracy: 0.9615
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  7.0493860e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9810 - val_loss: 0.1892 - val_accuracy: 0.9632
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  5.5408187e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9810 - val_loss: 0.1806 - val_accuracy: 0.9648
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  4.0703555e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9805 - val_loss: 0.1850 - val_accuracy: 0.9650
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.0017204e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9805 - val_loss: 0.1853 - val_accuracy: 0.9655
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.4355180e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9804 - val_loss: 0.2095 - val_accuracy: 0.9571
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  6.7988788e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9794 - val_loss: 0.2021 - val_accuracy: 0.9612
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.3307489e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9812 - val_loss: 0.2014 - val_accuracy: 0.9595
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -8.3155447e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9798 - val_loss: 0.2023 - val_accuracy: 0.9618
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.0727261e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9800 - val_loss: 0.1870 - val_accuracy: 0.9658
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 7.6732412e-04
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9801 - val_loss: 0.2046 - val_accuracy: 0.9586
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.7912083e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9800 - val_loss: 0.1822 - val_accuracy: 0.9656
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  7.3126762e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9803 - val_loss: 0.1806 - val_accuracy: 0.9658
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.6330113e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9797 - val_loss: 0.2035 - val_accuracy: 0.9615
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.5580232e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9796 - val_loss: 0.1988 - val_accuracy: 0.9637
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.4837763e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9814 - val_loss: 0.2069 - val_accuracy: 0.9584
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.0624164e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9799 - val_loss: 0.1917 - val_accuracy: 0.9631
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  8.2077266e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 377/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1262 - accuracy: 0.9809 - val_loss: 0.2139 - val_accuracy: 0.9576
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.8564976e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9797 - val_loss: 0.1912 - val_accuracy: 0.9644
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.2543010e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 379/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1275 - accuracy: 0.9804 - val_loss: 0.1978 - val_accuracy: 0.9626
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.8632263e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 380/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1276 - accuracy: 0.9805 - val_loss: 0.1987 - val_accuracy: 0.9626
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 7.6061452e-04
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1299 - accuracy: 0.9805 - val_loss: 0.2044 - val_accuracy: 0.9600
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.4569253e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9801 - val_loss: 0.1980 - val_accuracy: 0.9614
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -7.1604373e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9805 - val_loss: 0.1920 - val_accuracy: 0.9642
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.7270191e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9802 - val_loss: 0.1773 - val_accuracy: 0.9640
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.2098746e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 385/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1279 - accuracy: 0.9798 - val_loss: 0.2012 - val_accuracy: 0.9621
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.6086413e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9805 - val_loss: 0.1725 - val_accuracy: 0.9677
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.5462115e-03
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9806 - val_loss: 0.2034 - val_accuracy: 0.9627
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.6892169e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9815 - val_loss: 0.1990 - val_accuracy: 0.9605
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.5410886e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9807 - val_loss: 0.1827 - val_accuracy: 0.9653
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.7342695e-06  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 390/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1302 - accuracy: 0.9807 - val_loss: 0.2097 - val_accuracy: 0.9585
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.1302849e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9814 - val_loss: 0.2099 - val_accuracy: 0.9617
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.1500490e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9803 - val_loss: 0.2135 - val_accuracy: 0.9603
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.0443031e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9802 - val_loss: 0.1866 - val_accuracy: 0.9651
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  8.9640802e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9807 - val_loss: 0.1828 - val_accuracy: 0.9677
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.9065368e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9791 - val_loss: 0.1889 - val_accuracy: 0.9631
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -7.9919130e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1304 - accuracy: 0.9805 - val_loss: 0.2029 - val_accuracy: 0.9600
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.8484246e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9797 - val_loss: 0.1911 - val_accuracy: 0.9611
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.9648864e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9799 - val_loss: 0.1993 - val_accuracy: 0.9612
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.5801664e-04
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9800 - val_loss: 0.2161 - val_accuracy: 0.9573
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.6813051e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9799 - val_loss: 0.2013 - val_accuracy: 0.9607
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -8.0838875e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.021713767837582942
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.43911564
tf.Tensor(
[[1. 1. 1. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 0. 1.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [1. 0. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.03694960108197343
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.87166667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 1. 1.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.09687688139530692
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.]
 [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.]
 [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 242s 12ms/step - loss: 0.1299 - accuracy: 0.9802 - val_loss: 0.1964 - val_accuracy: 0.9634
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.2531057e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 402/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1253 - accuracy: 0.9815 - val_loss: 0.2007 - val_accuracy: 0.9621
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 6.7649336e-05
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9798 - val_loss: 0.1866 - val_accuracy: 0.9653
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.0797048e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9808 - val_loss: 0.1980 - val_accuracy: 0.9624
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.7734433e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9798 - val_loss: 0.1863 - val_accuracy: 0.9644
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.1009780e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9805 - val_loss: 0.1841 - val_accuracy: 0.9655
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  8.9803252e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9802 - val_loss: 0.1900 - val_accuracy: 0.9660
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.9773498e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9804 - val_loss: 0.1908 - val_accuracy: 0.9643
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  2.2134158e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9809 - val_loss: 0.1982 - val_accuracy: 0.9598
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.5278238e-07 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9802 - val_loss: 0.2018 - val_accuracy: 0.9611
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.3455713e-09 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9799 - val_loss: 0.2158 - val_accuracy: 0.9559
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -8.5652246e-10  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9799 - val_loss: 0.1915 - val_accuracy: 0.9635
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.1703126e-15
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9804 - val_loss: 0.1885 - val_accuracy: 0.9644
[1.39888236e-34 4.81357360e-34 4.61340023e-34 ... 0.00000000e+00
 1.18233166e-20 0.00000000e+00]
Sparsity at: 0.48809166040571
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9797 - val_loss: 0.2303 - val_accuracy: 0.9543
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.3822716e-26  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 415/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1305 - accuracy: 0.9802 - val_loss: 0.1886 - val_accuracy: 0.9642
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.5642930e-31  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9797 - val_loss: 0.2094 - val_accuracy: 0.9578
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9816 - val_loss: 0.1962 - val_accuracy: 0.9644
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9805 - val_loss: 0.1914 - val_accuracy: 0.9657
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9801 - val_loss: 0.2127 - val_accuracy: 0.9571
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9804 - val_loss: 0.1926 - val_accuracy: 0.9641
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9795 - val_loss: 0.1979 - val_accuracy: 0.9605
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9798 - val_loss: 0.1921 - val_accuracy: 0.9648
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9805 - val_loss: 0.2039 - val_accuracy: 0.9632
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9804 - val_loss: 0.1896 - val_accuracy: 0.9637
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9793 - val_loss: 0.1925 - val_accuracy: 0.9662
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 426/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1271 - accuracy: 0.9808 - val_loss: 0.1940 - val_accuracy: 0.9630
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.5643091e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9810 - val_loss: 0.2024 - val_accuracy: 0.9609
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -3.4917784e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9805 - val_loss: 0.2085 - val_accuracy: 0.9605
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  5.1602966e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9796 - val_loss: 0.2068 - val_accuracy: 0.9578
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.8680256e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9814 - val_loss: 0.2488 - val_accuracy: 0.9480
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -8.7814115e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9810 - val_loss: 0.1997 - val_accuracy: 0.9613
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.9954630e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9796 - val_loss: 0.1953 - val_accuracy: 0.9632
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.1197824e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9803 - val_loss: 0.1766 - val_accuracy: 0.9673
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.8281140e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9804 - val_loss: 0.2000 - val_accuracy: 0.9603
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.9694909e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9798 - val_loss: 0.2039 - val_accuracy: 0.9606
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.8631099e-03
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9810 - val_loss: 0.1859 - val_accuracy: 0.9663
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.4609578e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9808 - val_loss: 0.1835 - val_accuracy: 0.9637
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  7.8266556e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9809 - val_loss: 0.1860 - val_accuracy: 0.9649
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.8776259e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9806 - val_loss: 0.1838 - val_accuracy: 0.9653
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.8344529e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9796 - val_loss: 0.1848 - val_accuracy: 0.9634
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.6816432e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9803 - val_loss: 0.1787 - val_accuracy: 0.9655
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.7559162e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9803 - val_loss: 0.1860 - val_accuracy: 0.9663
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.3259020e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 443/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1330 - accuracy: 0.9797 - val_loss: 0.2151 - val_accuracy: 0.9569
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -9.9821005e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9801 - val_loss: 0.1902 - val_accuracy: 0.9630
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.0405128e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1300 - accuracy: 0.9802 - val_loss: 0.1981 - val_accuracy: 0.9603
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.5771826e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9803 - val_loss: 0.1823 - val_accuracy: 0.9659
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.1225374e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9796 - val_loss: 0.1965 - val_accuracy: 0.9621
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.2475654e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9787 - val_loss: 0.1974 - val_accuracy: 0.9625
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  6.2016112e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9806 - val_loss: 0.1860 - val_accuracy: 0.9621
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.0099871e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9805 - val_loss: 0.2184 - val_accuracy: 0.9559
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  5.1190867e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9802 - val_loss: 0.1710 - val_accuracy: 0.9682
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -9.7378834e-06  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1241 - accuracy: 0.9815 - val_loss: 0.2050 - val_accuracy: 0.9587
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.4975001e-05  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9801 - val_loss: 0.1883 - val_accuracy: 0.9621
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -6.3573339e-06  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9790 - val_loss: 0.1882 - val_accuracy: 0.9638
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  8.0610218e-05 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9802 - val_loss: 0.2036 - val_accuracy: 0.9625
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.2415971e-04
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9809 - val_loss: 0.1841 - val_accuracy: 0.9636
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.5177488e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9798 - val_loss: 0.1762 - val_accuracy: 0.9674
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -6.9175183e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9809 - val_loss: 0.2069 - val_accuracy: 0.9597
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  3.0604794e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9806 - val_loss: 0.1796 - val_accuracy: 0.9668
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.2529758e-03
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9804 - val_loss: 0.1876 - val_accuracy: 0.9623
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  6.2467298e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9805 - val_loss: 0.2196 - val_accuracy: 0.9544
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.3983153e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9801 - val_loss: 0.1787 - val_accuracy: 0.9649
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.6073945e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9800 - val_loss: 0.1722 - val_accuracy: 0.9699
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.8695957e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9805 - val_loss: 0.1854 - val_accuracy: 0.9652
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.1063369e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9804 - val_loss: 0.1736 - val_accuracy: 0.9697
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.3175045e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9801 - val_loss: 0.2059 - val_accuracy: 0.9591
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  7.6385541e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9807 - val_loss: 0.1787 - val_accuracy: 0.9662
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  5.5458688e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9804 - val_loss: 0.2259 - val_accuracy: 0.9563
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.2886905e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9801 - val_loss: 0.1997 - val_accuracy: 0.9614
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.9550936e-04
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9807 - val_loss: 0.1918 - val_accuracy: 0.9624
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.8418157e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9801 - val_loss: 0.1697 - val_accuracy: 0.9699
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.0449234e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9797 - val_loss: 0.2031 - val_accuracy: 0.9598
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.4533716e-03
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9801 - val_loss: 0.1825 - val_accuracy: 0.9669
[1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.0492996e-02
 0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1285 - accuracy: 0.9801 - val_loss: 0.1865 - val_accuracy: 0.9632
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.8096509e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9798 - val_loss: 0.1826 - val_accuracy: 0.9674
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.7263809e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1259 - accuracy: 0.9808 - val_loss: 0.1898 - val_accuracy: 0.9656
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.1228092e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9800 - val_loss: 0.2005 - val_accuracy: 0.9611
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.5121604e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9803 - val_loss: 0.2214 - val_accuracy: 0.9537
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -7.2913917e-07 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9794 - val_loss: 0.1817 - val_accuracy: 0.9656
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.7112724e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1304 - accuracy: 0.9796 - val_loss: 0.1961 - val_accuracy: 0.9635
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  2.7726165e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9801 - val_loss: 0.1840 - val_accuracy: 0.9670
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -2.0365163e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9802 - val_loss: 0.2100 - val_accuracy: 0.9601
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  3.1877335e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9798 - val_loss: 0.1947 - val_accuracy: 0.9605
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.5924698e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9808 - val_loss: 0.1892 - val_accuracy: 0.9652
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  8.6475280e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9800 - val_loss: 0.1978 - val_accuracy: 0.9599
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
  1.7149337e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9808 - val_loss: 0.1727 - val_accuracy: 0.9686
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.7060778e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 487/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1269 - accuracy: 0.9800 - val_loss: 0.1944 - val_accuracy: 0.9619
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -5.6231697e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9802 - val_loss: 0.2001 - val_accuracy: 0.9626
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -5.9556961e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9810 - val_loss: 0.1789 - val_accuracy: 0.9671
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  8.1373140e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9808 - val_loss: 0.1741 - val_accuracy: 0.9690
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.4102547e-04 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9803 - val_loss: 0.1901 - val_accuracy: 0.9652
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -1.2546194e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9806 - val_loss: 0.2010 - val_accuracy: 0.9623
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -2.1622840e-03 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9805 - val_loss: 0.1883 - val_accuracy: 0.9652
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  4.6879746e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1243 - accuracy: 0.9811 - val_loss: 0.2153 - val_accuracy: 0.9577
[ 1.39888236e-34  4.81357360e-34  4.61340023e-34 ...  0.00000000e+00
  1.21089986e-04 -0.00000000e+00]
Sparsity at: 0.48809166040571
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9797 - val_loss: 0.2007 - val_accuracy: 0.9639
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
  1.3613561e-03  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9807 - val_loss: 0.1929 - val_accuracy: 0.9622
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -7.2531204e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 497/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1285 - accuracy: 0.9806 - val_loss: 0.1829 - val_accuracy: 0.9663
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -1.0037678e-04  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 498/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9796 - val_loss: 0.1980 - val_accuracy: 0.9617
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ... -0.0000000e+00
 -4.2148949e-06  0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9797 - val_loss: 0.1764 - val_accuracy: 0.9677
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -3.7922968e-07 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9803 - val_loss: 0.2003 - val_accuracy: 0.9602
[ 1.3988824e-34  4.8135736e-34  4.6134002e-34 ...  0.0000000e+00
 -4.3595131e-07 -0.0000000e+00]
Sparsity at: 0.48809166040571
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.037129007279872894
Thresholhold -0.05450941622257233
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.06132306158542633
Thresholhold -0.07481430470943451
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.1178489625453949
Thresholhold -0.06855818629264832
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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  1/235 [..............................] - ETA: 4:21:57 - loss: 2.7905 - accuracy: 0.1445WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0094s vs `on_train_batch_begin` time: 11.0806s). Check your callbacks.
235/235 [==============================] - 70s 12ms/step - loss: 0.2415 - accuracy: 0.9289 - val_loss: 0.2132 - val_accuracy: 0.9550
[-0.05450942  0.01009626 -0.00054583 ...  0.20011221 -0.21515071
 -0.14325681]
Sparsity at: 0.0
Epoch 2/500
235/235 [==============================] - 3s 12ms/step - loss: 0.0876 - accuracy: 0.9750 - val_loss: 0.0966 - val_accuracy: 0.9700
[-0.05450942  0.01009626 -0.00054583 ...  0.23081218 -0.23711504
 -0.1560789 ]
Sparsity at: 0.0
Epoch 3/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0505 - accuracy: 0.9862 - val_loss: 0.0833 - val_accuracy: 0.9744
[-0.05450942  0.01009626 -0.00054583 ...  0.25628632 -0.26043
 -0.16595905]
Sparsity at: 0.0
Epoch 4/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0308 - accuracy: 0.9922 - val_loss: 0.0861 - val_accuracy: 0.9733
[-0.05450942  0.01009626 -0.00054583 ...  0.27751678 -0.27961394
 -0.17310259]
Sparsity at: 0.0
Epoch 5/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0194 - accuracy: 0.9955 - val_loss: 0.0822 - val_accuracy: 0.9735
[-0.05450942  0.01009626 -0.00054583 ...  0.300424   -0.29422602
 -0.17671813]
Sparsity at: 0.0
Epoch 6/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0138 - accuracy: 0.9966 - val_loss: 0.0823 - val_accuracy: 0.9760
[-0.05450942  0.01009626 -0.00054583 ...  0.31929427 -0.2952289
 -0.18168859]
Sparsity at: 0.0
Epoch 7/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0138 - accuracy: 0.9964 - val_loss: 0.0894 - val_accuracy: 0.9729
[-0.05450942  0.01009626 -0.00054583 ...  0.3338897  -0.3126545
 -0.18281323]
Sparsity at: 0.0
Epoch 8/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0122 - accuracy: 0.9969 - val_loss: 0.0847 - val_accuracy: 0.9764
[-0.05450942  0.01009626 -0.00054583 ...  0.34222835 -0.3229267
 -0.19074386]
Sparsity at: 0.0
Epoch 9/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0106 - accuracy: 0.9969 - val_loss: 0.0832 - val_accuracy: 0.9768
[-0.05450942  0.01009626 -0.00054583 ...  0.35670444 -0.32905862
 -0.19204962]
Sparsity at: 0.0
Epoch 10/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0101 - accuracy: 0.9972 - val_loss: 0.0837 - val_accuracy: 0.9764
[-0.05450942  0.01009626 -0.00054583 ...  0.372736   -0.32508072
 -0.19110572]
Sparsity at: 0.0
Epoch 11/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0083 - accuracy: 0.9978 - val_loss: 0.0839 - val_accuracy: 0.9776
[-0.05450942  0.01009626 -0.00054583 ...  0.38408643 -0.33403382
 -0.19220103]
Sparsity at: 0.0
Epoch 12/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0065 - accuracy: 0.9980 - val_loss: 0.0832 - val_accuracy: 0.9794
[-0.05450942  0.01009626 -0.00054583 ...  0.3908822  -0.33916074
 -0.19416702]
Sparsity at: 0.0
Epoch 13/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0064 - accuracy: 0.9982 - val_loss: 0.0784 - val_accuracy: 0.9787
[-0.05450942  0.01009626 -0.00054583 ...  0.38757458 -0.3382323
 -0.19588149]
Sparsity at: 0.0
Epoch 14/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9990 - val_loss: 0.0800 - val_accuracy: 0.9803
[-0.05450942  0.01009626 -0.00054583 ...  0.40115485 -0.34620863
 -0.20064116]
Sparsity at: 0.0
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9994 - val_loss: 0.0718 - val_accuracy: 0.9809
[-0.05450942  0.01009626 -0.00054583 ...  0.4026549  -0.3460693
 -0.20430736]
Sparsity at: 0.0
Epoch 16/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9996 - val_loss: 0.0914 - val_accuracy: 0.9779
[-0.05450942  0.01009626 -0.00054583 ...  0.41177487 -0.3490294
 -0.208024  ]
Sparsity at: 0.0
Epoch 17/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.1279 - val_accuracy: 0.9700
[-0.05450942  0.01009626 -0.00054583 ...  0.4224475  -0.35416237
 -0.21556702]
Sparsity at: 0.0
Epoch 18/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0184 - accuracy: 0.9936 - val_loss: 0.1095 - val_accuracy: 0.9725
[-0.05450942  0.01009626 -0.00054583 ...  0.42155692 -0.34116215
 -0.20000446]
Sparsity at: 0.0
Epoch 19/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0115 - accuracy: 0.9960 - val_loss: 0.0867 - val_accuracy: 0.9785
[-0.05450942  0.01009626 -0.00054583 ...  0.414257   -0.35070923
 -0.20100692]
Sparsity at: 0.0
Epoch 20/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0075 - accuracy: 0.9975 - val_loss: 0.0868 - val_accuracy: 0.9810
[-0.05450942  0.01009626 -0.00054583 ...  0.4127119  -0.36144352
 -0.1839759 ]
Sparsity at: 0.0
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0035 - accuracy: 0.9989 - val_loss: 0.0832 - val_accuracy: 0.9806
[-0.05450942  0.01009626 -0.00054583 ...  0.4171977  -0.36277395
 -0.1878449 ]
Sparsity at: 0.0
Epoch 22/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0728 - val_accuracy: 0.9830
[-0.05450942  0.01009626 -0.00054583 ...  0.42150843 -0.3692222
 -0.19237576]
Sparsity at: 0.0
Epoch 23/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.0795 - val_accuracy: 0.9818
[-0.05450942  0.01009626 -0.00054583 ...  0.422935   -0.3767858
 -0.18638399]
Sparsity at: 0.0
Epoch 24/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0770 - val_accuracy: 0.9824
[-0.05450942  0.01009626 -0.00054583 ...  0.43026227 -0.37381005
 -0.19352132]
Sparsity at: 0.0
Epoch 25/500
235/235 [==============================] - 3s 13ms/step - loss: 8.1831e-04 - accuracy: 0.9998 - val_loss: 0.0745 - val_accuracy: 0.9829
[-0.05450942  0.01009626 -0.00054583 ...  0.43124744 -0.3765246
 -0.19485505]
Sparsity at: 0.0
Epoch 26/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0923 - val_accuracy: 0.9805
[-0.05450942  0.01009626 -0.00054583 ...  0.43784767 -0.3794938
 -0.2001888 ]
Sparsity at: 0.0
Epoch 27/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0086 - accuracy: 0.9971 - val_loss: 0.1498 - val_accuracy: 0.9694
[-0.05450942  0.01009626 -0.00054583 ...  0.43473956 -0.38066217
 -0.20399846]
Sparsity at: 0.0
Epoch 28/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0154 - accuracy: 0.9945 - val_loss: 0.0926 - val_accuracy: 0.9786
[-0.05450942  0.01009626 -0.00054583 ...  0.43695235 -0.4059898
 -0.20897159]
Sparsity at: 0.0
Epoch 29/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0066 - accuracy: 0.9977 - val_loss: 0.0774 - val_accuracy: 0.9819
[-0.05450942  0.01009626 -0.00054583 ...  0.4457631  -0.4184451
 -0.22712761]
Sparsity at: 0.0
Epoch 30/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.0730 - val_accuracy: 0.9831
[-0.05450942  0.01009626 -0.00054583 ...  0.44658524 -0.42340267
 -0.22533636]
Sparsity at: 0.0
Epoch 31/500
235/235 [==============================] - 3s 13ms/step - loss: 8.3199e-04 - accuracy: 0.9999 - val_loss: 0.0740 - val_accuracy: 0.9832
[-0.05450942  0.01009626 -0.00054583 ...  0.44621664 -0.42699742
 -0.23091614]
Sparsity at: 0.0
Epoch 32/500
235/235 [==============================] - 3s 13ms/step - loss: 6.1709e-04 - accuracy: 0.9999 - val_loss: 0.0735 - val_accuracy: 0.9832
[-0.05450942  0.01009626 -0.00054583 ...  0.4481061  -0.42815065
 -0.22955613]
Sparsity at: 0.0
Epoch 33/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3586e-04 - accuracy: 1.0000 - val_loss: 0.0698 - val_accuracy: 0.9841
[-0.05450942  0.01009626 -0.00054583 ...  0.4505352  -0.428865
 -0.22655794]
Sparsity at: 0.0
Epoch 34/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4834e-04 - accuracy: 1.0000 - val_loss: 0.0710 - val_accuracy: 0.9837
[-0.05450942  0.01009626 -0.00054583 ...  0.45070252 -0.42981657
 -0.2270369 ]
Sparsity at: 0.0
Epoch 35/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1477e-04 - accuracy: 1.0000 - val_loss: 0.0697 - val_accuracy: 0.9840
[-0.05450942  0.01009626 -0.00054583 ...  0.45224392 -0.43037412
 -0.22719708]
Sparsity at: 0.0
Epoch 36/500
235/235 [==============================] - 3s 13ms/step - loss: 8.7302e-05 - accuracy: 1.0000 - val_loss: 0.0694 - val_accuracy: 0.9844
[-0.05450942  0.01009626 -0.00054583 ...  0.4533458  -0.43150717
 -0.2279303 ]
Sparsity at: 0.0
Epoch 37/500
235/235 [==============================] - 3s 13ms/step - loss: 7.1450e-05 - accuracy: 1.0000 - val_loss: 0.0697 - val_accuracy: 0.9844
[-0.05450942  0.01009626 -0.00054583 ...  0.4548255  -0.4321433
 -0.22809035]
Sparsity at: 0.0
Epoch 38/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8799e-05 - accuracy: 1.0000 - val_loss: 0.0702 - val_accuracy: 0.9841
[-0.05450942  0.01009626 -0.00054583 ...  0.45590773 -0.43305388
 -0.22838162]
Sparsity at: 0.0
Epoch 39/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3525e-05 - accuracy: 1.0000 - val_loss: 0.0704 - val_accuracy: 0.9844
[-0.05450942  0.01009626 -0.00054583 ...  0.4573869  -0.4330939
 -0.22914332]
Sparsity at: 0.0
Epoch 40/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9402e-05 - accuracy: 1.0000 - val_loss: 0.0709 - val_accuracy: 0.9845
[-0.05450942  0.01009626 -0.00054583 ...  0.45915425 -0.43429145
 -0.22970274]
Sparsity at: 0.0
Epoch 41/500
235/235 [==============================] - 3s 13ms/step - loss: 4.1711e-05 - accuracy: 1.0000 - val_loss: 0.0712 - val_accuracy: 0.9848
[-0.05450942  0.01009626 -0.00054583 ...  0.46076664 -0.43507972
 -0.23040633]
Sparsity at: 0.0
Epoch 42/500
235/235 [==============================] - 3s 13ms/step - loss: 4.0267e-05 - accuracy: 1.0000 - val_loss: 0.0717 - val_accuracy: 0.9845
[-0.05450942  0.01009626 -0.00054583 ...  0.46173775 -0.43692407
 -0.23004994]
Sparsity at: 0.0
Epoch 43/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3067e-05 - accuracy: 1.0000 - val_loss: 0.0725 - val_accuracy: 0.9843
[-0.05450942  0.01009626 -0.00054583 ...  0.46294153 -0.43767175
 -0.23116162]
Sparsity at: 0.0
Epoch 44/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0726e-05 - accuracy: 1.0000 - val_loss: 0.0729 - val_accuracy: 0.9845
[-0.05450942  0.01009626 -0.00054583 ...  0.46450704 -0.43877536
 -0.23205411]
Sparsity at: 0.0
Epoch 45/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6020e-05 - accuracy: 1.0000 - val_loss: 0.0727 - val_accuracy: 0.9846
[-0.05450942  0.01009626 -0.00054583 ...  0.46586427 -0.43968672
 -0.2325571 ]
Sparsity at: 0.0
Epoch 46/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5947e-05 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9850
[-0.05450942  0.01009626 -0.00054583 ...  0.46685046 -0.44053036
 -0.23343515]
Sparsity at: 0.0
Epoch 47/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2879e-05 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9847
[-0.05450942  0.01009626 -0.00054583 ...  0.46866867 -0.44179937
 -0.2332024 ]
Sparsity at: 0.0
Epoch 48/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9453e-05 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9850
[-0.05450942  0.01009626 -0.00054583 ...  0.4702337  -0.44306484
 -0.23376909]
Sparsity at: 0.0
Epoch 49/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7743e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9852
[-0.05450942  0.01009626 -0.00054583 ...  0.47160578 -0.44475976
 -0.23459777]
Sparsity at: 0.0
Epoch 50/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5825e-05 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9853
[-0.05450942  0.01009626 -0.00054583 ...  0.4742282  -0.44578722
 -0.23552185]
Sparsity at: 0.0
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.06909565646716764
Thresholhold -0.05450941622257233
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.0964097025008499
Thresholhold -0.11463846266269684
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.31271971456216363
Thresholhold -0.25922611355781555
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 118s 11ms/step - loss: 1.3968e-05 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9853
[-0.05450942  0.01009626 -0.00054583 ...  0.47402266 -0.44713575
 -0.23630936]
Sparsity at: 0.0
Epoch 52/500
235/235 [==============================] - 3s 12ms/step - loss: 0.0266 - accuracy: 0.9933 - val_loss: 0.3552 - val_accuracy: 0.9409
[-0.05450942  0.01009626 -0.00054583 ...  0.4500926  -0.42372838
 -0.22058588]
Sparsity at: 0.0
Epoch 53/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0344 - accuracy: 0.9893 - val_loss: 0.0794 - val_accuracy: 0.9800
[-0.05450942  0.01009626 -0.00054583 ...  0.43176877 -0.39148265
 -0.23400038]
Sparsity at: 0.0
Epoch 54/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0064 - accuracy: 0.9982 - val_loss: 0.0738 - val_accuracy: 0.9819
[-0.05450942  0.01009626 -0.00054583 ...  0.43429634 -0.38510275
 -0.22832192]
Sparsity at: 0.0
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 0.0726 - val_accuracy: 0.9834
[-0.05450942  0.01009626 -0.00054583 ...  0.4404288  -0.38835803
 -0.23018515]
Sparsity at: 0.0
Epoch 56/500
235/235 [==============================] - 3s 13ms/step - loss: 8.4611e-04 - accuracy: 0.9999 - val_loss: 0.0731 - val_accuracy: 0.9832
[-0.05450942  0.01009626 -0.00054583 ...  0.44761673 -0.39999166
 -0.2327564 ]
Sparsity at: 0.0
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9249e-04 - accuracy: 1.0000 - val_loss: 0.0739 - val_accuracy: 0.9837
[-0.05450942  0.01009626 -0.00054583 ...  0.45130435 -0.40536836
 -0.23289822]
Sparsity at: 0.0
Epoch 58/500
235/235 [==============================] - 4s 16ms/step - loss: 5.5136e-04 - accuracy: 1.0000 - val_loss: 0.0720 - val_accuracy: 0.9845
[-0.05450942  0.01009626 -0.00054583 ...  0.4533759  -0.40681177
 -0.23341732]
Sparsity at: 0.0
Epoch 59/500
235/235 [==============================] - 3s 12ms/step - loss: 3.5280e-04 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9839
[-0.05450942  0.01009626 -0.00054583 ...  0.4551178  -0.4085616
 -0.23288314]
Sparsity at: 0.0
Epoch 60/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0987e-04 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9839
[-0.05450942  0.01009626 -0.00054583 ...  0.45634022 -0.41029918
 -0.2335798 ]
Sparsity at: 0.0
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0848 - val_accuracy: 0.9815
[-0.05450942  0.01009626 -0.00054583 ...  0.45866343 -0.41304365
 -0.22501664]
Sparsity at: 0.0
Epoch 62/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.0908 - val_accuracy: 0.9810
[-0.05450942  0.01009626 -0.00054583 ...  0.46451667 -0.41809878
 -0.22718015]
Sparsity at: 0.0
Epoch 63/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1005 - val_accuracy: 0.9777
[-0.05450942  0.01009626 -0.00054583 ...  0.47567096 -0.4167286
 -0.22029394]
Sparsity at: 0.0
Epoch 64/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0074 - accuracy: 0.9977 - val_loss: 0.1017 - val_accuracy: 0.9784
[-0.05450942  0.01009626 -0.00054583 ...  0.48086745 -0.39974797
 -0.22285235]
Sparsity at: 0.0
Epoch 65/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0049 - accuracy: 0.9986 - val_loss: 0.0925 - val_accuracy: 0.9801
[-0.05450942  0.01009626 -0.00054583 ...  0.48978716 -0.39499664
 -0.21975265]
Sparsity at: 0.0
Epoch 66/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9991 - val_loss: 0.1008 - val_accuracy: 0.9777
[-0.05450942  0.01009626 -0.00054583 ...  0.5009468  -0.40092227
 -0.22066136]
Sparsity at: 0.0
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 0.0830 - val_accuracy: 0.9822
[-0.05450942  0.01009626 -0.00054583 ...  0.50589806 -0.40682143
 -0.22615469]
Sparsity at: 0.0
Epoch 68/500
235/235 [==============================] - 3s 13ms/step - loss: 7.3658e-04 - accuracy: 0.9999 - val_loss: 0.0778 - val_accuracy: 0.9841
[-0.05450942  0.01009626 -0.00054583 ...  0.5028206  -0.408657
 -0.21802443]
Sparsity at: 0.0
Epoch 69/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7994e-04 - accuracy: 1.0000 - val_loss: 0.0788 - val_accuracy: 0.9842
[-0.05450942  0.01009626 -0.00054583 ...  0.50290906 -0.4097446
 -0.21905568]
Sparsity at: 0.0
Epoch 70/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4084e-04 - accuracy: 1.0000 - val_loss: 0.0792 - val_accuracy: 0.9843
[-0.05450942  0.01009626 -0.00054583 ...  0.50359344 -0.4114527
 -0.21965334]
Sparsity at: 0.0
Epoch 71/500
235/235 [==============================] - 3s 13ms/step - loss: 8.3888e-05 - accuracy: 1.0000 - val_loss: 0.0794 - val_accuracy: 0.9843
[-0.05450942  0.01009626 -0.00054583 ...  0.50358266 -0.41242063
 -0.21970175]
Sparsity at: 0.0
Epoch 72/500
235/235 [==============================] - 3s 13ms/step - loss: 7.9682e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9837
[-0.05450942  0.01009626 -0.00054583 ...  0.50401646 -0.41674367
 -0.21965604]
Sparsity at: 0.0
Epoch 73/500
235/235 [==============================] - 3s 13ms/step - loss: 6.5596e-05 - accuracy: 1.0000 - val_loss: 0.0800 - val_accuracy: 0.9846
[-0.05450942  0.01009626 -0.00054583 ...  0.5049147  -0.4157207
 -0.22052668]
Sparsity at: 0.0
Epoch 74/500
235/235 [==============================] - 3s 13ms/step - loss: 6.0833e-05 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9843
[-0.05450942  0.01009626 -0.00054583 ...  0.505586   -0.4154534
 -0.22047366]
Sparsity at: 0.0
Epoch 75/500
235/235 [==============================] - 3s 13ms/step - loss: 4.1604e-05 - accuracy: 1.0000 - val_loss: 0.0813 - val_accuracy: 0.9839
[-0.05450942  0.01009626 -0.00054583 ...  0.5061006  -0.4164136
 -0.22065751]
Sparsity at: 0.0
Epoch 76/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7609e-05 - accuracy: 1.0000 - val_loss: 0.0816 - val_accuracy: 0.9836
[-0.05450942  0.01009626 -0.00054583 ...  0.5066256  -0.41811085
 -0.22083157]
Sparsity at: 0.0
Epoch 77/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2355e-05 - accuracy: 1.0000 - val_loss: 0.0821 - val_accuracy: 0.9835
[-0.05450942  0.01009626 -0.00054583 ...  0.50753474 -0.41904444
 -0.22056343]
Sparsity at: 0.0
Epoch 78/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9969e-05 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9836
[-0.05450942  0.01009626 -0.00054583 ...  0.5082601  -0.4198181
 -0.22080778]
Sparsity at: 0.0
Epoch 79/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6588e-05 - accuracy: 1.0000 - val_loss: 0.0829 - val_accuracy: 0.9836
[-0.05450942  0.01009626 -0.00054583 ...  0.5090798  -0.42144704
 -0.22115612]
Sparsity at: 0.0
Epoch 80/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2502e-05 - accuracy: 1.0000 - val_loss: 0.0832 - val_accuracy: 0.9838
[-0.05450942  0.01009626 -0.00054583 ...  0.5092405  -0.4224372
 -0.22154526]
Sparsity at: 0.0
Epoch 81/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2821e-04 - accuracy: 0.9999 - val_loss: 0.1025 - val_accuracy: 0.9819
[-0.05450942  0.01009626 -0.00054583 ...  0.51181537 -0.43092996
 -0.22237253]
Sparsity at: 0.0
Epoch 82/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0259 - accuracy: 0.9923 - val_loss: 0.1156 - val_accuracy: 0.9766
[-0.05450942  0.01009626 -0.00054583 ...  0.5088173  -0.39345688
 -0.2363766 ]
Sparsity at: 0.0
Epoch 83/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0078 - accuracy: 0.9976 - val_loss: 0.0888 - val_accuracy: 0.9806
[-0.05450942  0.01009626 -0.00054583 ...  0.5148693  -0.39103022
 -0.25137112]
Sparsity at: 0.0
Epoch 84/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0787 - val_accuracy: 0.9835
[-0.05450942  0.01009626 -0.00054583 ...  0.5178211  -0.39362714
 -0.2505413 ]
Sparsity at: 0.0
Epoch 85/500
235/235 [==============================] - 3s 13ms/step - loss: 6.3642e-04 - accuracy: 0.9999 - val_loss: 0.0786 - val_accuracy: 0.9837
[-0.05450942  0.01009626 -0.00054583 ...  0.5180767  -0.3996931
 -0.25266585]
Sparsity at: 0.0
Epoch 86/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0374e-04 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9836
[-0.05450942  0.01009626 -0.00054583 ...  0.51702195 -0.40092906
 -0.25554967]
Sparsity at: 0.0
Epoch 87/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6259e-04 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9837
[-0.05450942  0.01009626 -0.00054583 ...  0.5179631  -0.40158147
 -0.25568837]
Sparsity at: 0.0
Epoch 88/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2847e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9840
[-0.05450942  0.01009626 -0.00054583 ...  0.5187971  -0.40289387
 -0.25568503]
Sparsity at: 0.0
Epoch 89/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0333e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9842
[-0.05450942  0.01009626 -0.00054583 ...  0.5197888  -0.40443364
 -0.25692943]
Sparsity at: 0.0
Epoch 90/500
235/235 [==============================] - 3s 13ms/step - loss: 8.8469e-05 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9843
[-0.05450942  0.01009626 -0.00054583 ...  0.52025795 -0.4067398
 -0.2564937 ]
Sparsity at: 0.0
Epoch 91/500
235/235 [==============================] - 3s 13ms/step - loss: 7.6731e-05 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9848
[-0.05450942  0.01009626 -0.00054583 ...  0.5193875  -0.4079405
 -0.25507882]
Sparsity at: 0.0
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3650e-05 - accuracy: 1.0000 - val_loss: 0.0775 - val_accuracy: 0.9845
[-0.05450942  0.01009626 -0.00054583 ...  0.5210929  -0.40864143
 -0.2555256 ]
Sparsity at: 0.0
Epoch 93/500
235/235 [==============================] - 3s 13ms/step - loss: 5.6063e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9845
[-0.05450942  0.01009626 -0.00054583 ...  0.52308494 -0.41075328
 -0.2559555 ]
Sparsity at: 0.0
Epoch 94/500
235/235 [==============================] - 3s 13ms/step - loss: 5.1882e-05 - accuracy: 1.0000 - val_loss: 0.0784 - val_accuracy: 0.9846
[-0.05450942  0.01009626 -0.00054583 ...  0.52526563 -0.41190317
 -0.25715274]
Sparsity at: 0.0
Epoch 95/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1107 - val_accuracy: 0.9794
[-0.05450942  0.01009626 -0.00054583 ...  0.52682143 -0.41498017
 -0.26308203]
Sparsity at: 0.0
Epoch 96/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.1271 - val_accuracy: 0.9741
[-0.05450942  0.01009626 -0.00054583 ...  0.5111164  -0.4350201
 -0.20471412]
Sparsity at: 0.0
Epoch 97/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0065 - accuracy: 0.9977 - val_loss: 0.0982 - val_accuracy: 0.9795
[-0.05450942  0.01009626 -0.00054583 ...  0.49661228 -0.44186455
 -0.20365007]
Sparsity at: 0.0
Epoch 98/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0838 - val_accuracy: 0.9831
[-0.05450942  0.01009626 -0.00054583 ...  0.50466925 -0.4558257
 -0.21113397]
Sparsity at: 0.0
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1573e-04 - accuracy: 0.9999 - val_loss: 0.0883 - val_accuracy: 0.9829
[-0.05450942  0.01009626 -0.00054583 ...  0.5060526  -0.4552143
 -0.20996371]
Sparsity at: 0.0
Epoch 100/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5341e-04 - accuracy: 1.0000 - val_loss: 0.0844 - val_accuracy: 0.9829
[-0.05450942  0.01009626 -0.00054583 ...  0.5073188  -0.45698458
 -0.21132706]
Sparsity at: 0.0
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.1352814660744759
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.16507129524108777
Thresholhold -0.2088223397731781
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.3850435044503051
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 218s 11ms/step - loss: 2.4730e-04 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9831
[-0.05450942  0.01009626 -0.00054583 ...  0.5098376  -0.46093944
 -0.21183197]
Sparsity at: 0.0
Epoch 102/500
235/235 [==============================] - 3s 12ms/step - loss: 1.2795e-04 - accuracy: 1.0000 - val_loss: 0.0843 - val_accuracy: 0.9826
[-0.05450942  0.01009626 -0.00054583 ...  0.5109204  -0.46400246
 -0.21282776]
Sparsity at: 0.0
Epoch 103/500
235/235 [==============================] - 3s 13ms/step - loss: 9.9090e-05 - accuracy: 1.0000 - val_loss: 0.0852 - val_accuracy: 0.9827
[-0.05450942  0.01009626 -0.00054583 ...  0.51286036 -0.4644988
 -0.21343563]
Sparsity at: 0.0
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7650e-05 - accuracy: 1.0000 - val_loss: 0.0860 - val_accuracy: 0.9829
[-0.05450942  0.01009626 -0.00054583 ...  0.51378995 -0.46560282
 -0.21390921]
Sparsity at: 0.0
Epoch 105/500
235/235 [==============================] - 3s 13ms/step - loss: 9.3512e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9829
[-0.05450942  0.01009626 -0.00054583 ...  0.5154258  -0.4659413
 -0.21420777]
Sparsity at: 0.0
Epoch 106/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9325e-05 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9829
[-0.05450942  0.01009626 -0.00054583 ...  0.51690406 -0.4678133
 -0.21485157]
Sparsity at: 0.0
Epoch 107/500
235/235 [==============================] - 3s 13ms/step - loss: 4.1052e-05 - accuracy: 1.0000 - val_loss: 0.0846 - val_accuracy: 0.9835
[-0.05450942  0.01009626 -0.00054583 ...  0.51829904 -0.46924478
 -0.21567523]
Sparsity at: 0.0
Epoch 108/500
235/235 [==============================] - 3s 13ms/step - loss: 4.3297e-05 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9830
[-0.05450942  0.01009626 -0.00054583 ...  0.51961863 -0.4711746
 -0.2160236 ]
Sparsity at: 0.0
Epoch 109/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4525e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9834
[-0.05450942  0.01009626 -0.00054583 ...  0.52031744 -0.47234893
 -0.21591881]
Sparsity at: 0.0
Epoch 110/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0436e-05 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9835
[-0.05450942  0.01009626 -0.00054583 ...  0.5223311  -0.47317252
 -0.21725197]
Sparsity at: 0.0
Epoch 111/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0068 - accuracy: 0.9980 - val_loss: 0.1675 - val_accuracy: 0.9700
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.4756826e-01
 -4.7751001e-01 -2.2416425e-01]
Sparsity at: 0.0
Epoch 112/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0110 - accuracy: 0.9962 - val_loss: 0.1104 - val_accuracy: 0.9798
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.4852962e-01
 -5.0338262e-01 -2.1961421e-01]
Sparsity at: 0.0
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.0953 - val_accuracy: 0.9826
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5486488e-01
 -4.8553309e-01 -2.2373247e-01]
Sparsity at: 0.0
Epoch 114/500
235/235 [==============================] - 3s 13ms/step - loss: 7.1221e-04 - accuracy: 0.9999 - val_loss: 0.0925 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.4833508e-01
 -4.9209702e-01 -2.2393090e-01]
Sparsity at: 0.0
Epoch 115/500
235/235 [==============================] - 3s 13ms/step - loss: 3.5735e-04 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.4848009e-01
 -4.9562880e-01 -2.2257921e-01]
Sparsity at: 0.0
Epoch 116/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3363e-04 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.4881269e-01
 -4.9610874e-01 -2.2035985e-01]
Sparsity at: 0.0
Epoch 117/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3055e-04 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5028546e-01
 -4.9697098e-01 -2.2120795e-01]
Sparsity at: 0.0
Epoch 118/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4360e-04 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9837
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5199885e-01
 -4.9912673e-01 -2.2096348e-01]
Sparsity at: 0.0
Epoch 119/500
235/235 [==============================] - 3s 13ms/step - loss: 6.5715e-05 - accuracy: 1.0000 - val_loss: 0.0908 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5230999e-01
 -5.0001198e-01 -2.2137100e-01]
Sparsity at: 0.0
Epoch 120/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8175e-05 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5315089e-01
 -4.9991241e-01 -2.2161718e-01]
Sparsity at: 0.0
Epoch 121/500
235/235 [==============================] - 3s 13ms/step - loss: 5.0649e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5410177e-01
 -5.0104022e-01 -2.2274962e-01]
Sparsity at: 0.0
Epoch 122/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7714e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5518633e-01
 -5.0169367e-01 -2.2346123e-01]
Sparsity at: 0.0
Epoch 123/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9011e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5656302e-01
 -5.0333875e-01 -2.2264168e-01]
Sparsity at: 0.0
Epoch 124/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6941e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5755949e-01
 -5.0290197e-01 -2.1934767e-01]
Sparsity at: 0.0
Epoch 125/500
235/235 [==============================] - 3s 13ms/step - loss: 3.5726e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5766273e-01
 -5.0362474e-01 -2.2434416e-01]
Sparsity at: 0.0
Epoch 126/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9229e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5093408e-01
 -5.0399685e-01 -2.2418515e-01]
Sparsity at: 0.0
Epoch 127/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6980e-05 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5330592e-01
 -5.0491387e-01 -2.2462662e-01]
Sparsity at: 0.0
Epoch 128/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0056 - accuracy: 0.9986 - val_loss: 0.1414 - val_accuracy: 0.9741
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6162858e-01
 -5.0079077e-01 -1.7489073e-01]
Sparsity at: 0.0
Epoch 129/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0131 - accuracy: 0.9958 - val_loss: 0.1042 - val_accuracy: 0.9808
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.4967439e-01
 -4.7797152e-01 -1.8903105e-01]
Sparsity at: 0.0
Epoch 130/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 0.0983 - val_accuracy: 0.9833
[-0.05450942  0.01009626 -0.00054583 ...  0.5431443  -0.47834787
 -0.18943347]
Sparsity at: 0.0
Epoch 131/500
235/235 [==============================] - 3s 13ms/step - loss: 6.5468e-04 - accuracy: 0.9998 - val_loss: 0.0937 - val_accuracy: 0.9834
[-0.05450942  0.01009626 -0.00054583 ...  0.54512405 -0.48063335
 -0.19428793]
Sparsity at: 0.0
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1556e-04 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9838
[-0.05450942  0.01009626 -0.00054583 ...  0.5445734  -0.4832988
 -0.1957009 ]
Sparsity at: 0.0
Epoch 133/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2573e-04 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9840
[-0.05450942  0.01009626 -0.00054583 ...  0.5454544  -0.48659348
 -0.19400413]
Sparsity at: 0.0
Epoch 134/500
235/235 [==============================] - 3s 13ms/step - loss: 8.5070e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.4615158e-01
 -4.8916301e-01 -1.9545834e-01]
Sparsity at: 0.0
Epoch 135/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9182e-04 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9833
[-0.05450942  0.01009626 -0.00054583 ...  0.54100174 -0.47908148
 -0.19805025]
Sparsity at: 0.0
Epoch 136/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3282e-04 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9834
[-0.05450942  0.01009626 -0.00054583 ...  0.5374653  -0.47435302
 -0.19837436]
Sparsity at: 0.0
Epoch 137/500
235/235 [==============================] - 3s 13ms/step - loss: 8.2159e-05 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9841
[-0.05450942  0.01009626 -0.00054583 ...  0.53856033 -0.47618106
 -0.20012961]
Sparsity at: 0.0
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1428e-05 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9839
[-0.05450942  0.01009626 -0.00054583 ...  0.5382029  -0.47669077
 -0.19918498]
Sparsity at: 0.0
Epoch 139/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9968e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9841
[-0.05450942  0.01009626 -0.00054583 ...  0.5396785  -0.4784272
 -0.20000376]
Sparsity at: 0.0
Epoch 140/500
235/235 [==============================] - 3s 13ms/step - loss: 5.0261e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9839
[-0.05450942  0.01009626 -0.00054583 ...  0.54026437 -0.47754407
 -0.2004942 ]
Sparsity at: 0.0
Epoch 141/500
235/235 [==============================] - 3s 13ms/step - loss: 3.8617e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9841
[-0.05450942  0.01009626 -0.00054583 ...  0.5418829  -0.47902533
 -0.20157439]
Sparsity at: 0.0
Epoch 142/500
235/235 [==============================] - 3s 13ms/step - loss: 3.5384e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9843
[-0.05450942  0.01009626 -0.00054583 ...  0.54275215 -0.48037454
 -0.20097703]
Sparsity at: 0.0
Epoch 143/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0945e-05 - accuracy: 1.0000 - val_loss: 0.0959 - val_accuracy: 0.9844
[-0.05450942  0.01009626 -0.00054583 ...  0.54448193 -0.48220098
 -0.20153861]
Sparsity at: 0.0
Epoch 144/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6439e-04 - accuracy: 0.9999 - val_loss: 0.1091 - val_accuracy: 0.9826
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5036187e-01
 -4.8188365e-01 -2.0731044e-01]
Sparsity at: 0.0
Epoch 145/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0094 - accuracy: 0.9967 - val_loss: 0.1226 - val_accuracy: 0.9793
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5921155e-01
 -4.6094036e-01 -2.4732493e-01]
Sparsity at: 0.0
Epoch 146/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9987 - val_loss: 0.0954 - val_accuracy: 0.9829
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6782877e-01
 -4.8143187e-01 -2.4280065e-01]
Sparsity at: 0.0
Epoch 147/500
235/235 [==============================] - 3s 13ms/step - loss: 7.4757e-04 - accuracy: 0.9999 - val_loss: 0.0910 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6883681e-01
 -4.7698998e-01 -2.4918419e-01]
Sparsity at: 0.0
Epoch 148/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3281e-04 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9843
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6755543e-01
 -4.7616354e-01 -2.5090572e-01]
Sparsity at: 0.0
Epoch 149/500
235/235 [==============================] - 3s 13ms/step - loss: 9.6320e-05 - accuracy: 1.0000 - val_loss: 0.0879 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6882417e-01
 -4.7710124e-01 -2.5124630e-01]
Sparsity at: 0.0
Epoch 150/500
235/235 [==============================] - 3s 13ms/step - loss: 7.2292e-05 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6861424e-01
 -4.7872934e-01 -2.5126636e-01]
Sparsity at: 0.0
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.2041903307203352
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.23595483062840916
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.46597818951360637
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 211s 11ms/step - loss: 5.9790e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9848
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6960404e-01
 -4.7787893e-01 -2.5150684e-01]
Sparsity at: 0.0
Epoch 152/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9070e-05 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.7106405e-01
 -4.7951719e-01 -2.5316265e-01]
Sparsity at: 0.0
Epoch 153/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8152e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.7169241e-01
 -4.6882790e-01 -2.5356436e-01]
Sparsity at: 0.0
Epoch 154/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6960e-04 - accuracy: 0.9999 - val_loss: 0.1001 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.7180673e-01
 -4.7230881e-01 -2.5347248e-01]
Sparsity at: 0.0
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0206e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.7356030e-01
 -4.7869748e-01 -2.5663173e-01]
Sparsity at: 0.0
Epoch 156/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1034 - val_accuracy: 0.9804
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.7376856e-01
 -4.7249600e-01 -2.6266131e-01]
Sparsity at: 0.0
Epoch 157/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1130 - val_accuracy: 0.9809
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5414444e-01
 -4.5605209e-01 -2.7101049e-01]
Sparsity at: 0.0
Epoch 158/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.1182 - val_accuracy: 0.9804
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6084085e-01
 -4.7327054e-01 -2.6655492e-01]
Sparsity at: 0.0
Epoch 159/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1045 - val_accuracy: 0.9824
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5163813e-01
 -4.7459912e-01 -2.6064277e-01]
Sparsity at: 0.0
Epoch 160/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.0972 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5893713e-01
 -4.6699986e-01 -2.6785979e-01]
Sparsity at: 0.0
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2640e-04 - accuracy: 0.9998 - val_loss: 0.0917 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6246644e-01
 -4.6958256e-01 -2.8054103e-01]
Sparsity at: 0.0
Epoch 162/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7224e-04 - accuracy: 0.9999 - val_loss: 0.0942 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5952770e-01
 -4.6740809e-01 -2.7980986e-01]
Sparsity at: 0.0
Epoch 163/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1082e-04 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9837
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5863732e-01
 -4.6855721e-01 -2.7815163e-01]
Sparsity at: 0.0
Epoch 164/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5684e-04 - accuracy: 0.9999 - val_loss: 0.0921 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5910909e-01
 -4.7185543e-01 -2.7828011e-01]
Sparsity at: 0.0
Epoch 165/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1238e-04 - accuracy: 0.9999 - val_loss: 0.0895 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6067818e-01
 -4.7410694e-01 -2.7886909e-01]
Sparsity at: 0.0
Epoch 166/500
235/235 [==============================] - 4s 15ms/step - loss: 4.8391e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6087750e-01
 -4.7298512e-01 -2.8149799e-01]
Sparsity at: 0.0
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4517e-05 - accuracy: 1.0000 - val_loss: 0.0879 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6156349e-01
 -4.7440800e-01 -2.8115082e-01]
Sparsity at: 0.0
Epoch 168/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9775e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6080776e-01
 -4.7517166e-01 -2.8209046e-01]
Sparsity at: 0.0
Epoch 169/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1540e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6094074e-01
 -4.7561094e-01 -2.8281242e-01]
Sparsity at: 0.0
Epoch 170/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1134e-04 - accuracy: 0.9999 - val_loss: 0.0906 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.7047063e-01
 -4.7941777e-01 -2.9215595e-01]
Sparsity at: 0.0
Epoch 171/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.1317 - val_accuracy: 0.9794
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6219161e-01
 -4.7771823e-01 -2.7193606e-01]
Sparsity at: 0.0
Epoch 172/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.1229 - val_accuracy: 0.9788
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5606359e-01
 -5.0218654e-01 -2.8006905e-01]
Sparsity at: 0.0
Epoch 173/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.1074 - val_accuracy: 0.9806
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5460852e-01
 -4.8575169e-01 -2.8051063e-01]
Sparsity at: 0.0
Epoch 174/500
235/235 [==============================] - 4s 15ms/step - loss: 6.5333e-04 - accuracy: 0.9998 - val_loss: 0.0927 - val_accuracy: 0.9826
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5542815e-01
 -4.8218656e-01 -2.8372705e-01]
Sparsity at: 0.0
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4194e-04 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9828
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5674964e-01
 -4.8481402e-01 -2.8598803e-01]
Sparsity at: 0.0
Epoch 176/500
235/235 [==============================] - 3s 13ms/step - loss: 6.1571e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9833
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5749750e-01
 -4.8585925e-01 -2.8692228e-01]
Sparsity at: 0.0
Epoch 177/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7130e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5812055e-01
 -4.8745656e-01 -2.8622872e-01]
Sparsity at: 0.0
Epoch 178/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7702e-05 - accuracy: 1.0000 - val_loss: 0.0925 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.5978703e-01
 -4.8864526e-01 -2.8615063e-01]
Sparsity at: 0.0
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6382e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9826
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6168419e-01
 -4.9550152e-01 -2.8663486e-01]
Sparsity at: 0.0
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9173e-05 - accuracy: 1.0000 - val_loss: 0.0922 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6232744e-01
 -4.9583161e-01 -2.8687659e-01]
Sparsity at: 0.0
Epoch 181/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4341e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6259316e-01
 -4.9520400e-01 -2.8571296e-01]
Sparsity at: 0.0
Epoch 182/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1186e-05 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6331700e-01
 -4.9533704e-01 -2.8492793e-01]
Sparsity at: 0.0
Epoch 183/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7427e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6451696e-01
 -4.9562234e-01 -2.8519151e-01]
Sparsity at: 0.0
Epoch 184/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5304e-05 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9829
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6537735e-01
 -4.9554607e-01 -2.8567448e-01]
Sparsity at: 0.0
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2432e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6590921e-01
 -4.9582335e-01 -2.8581676e-01]
Sparsity at: 0.0
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2009e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6678361e-01
 -4.9645853e-01 -2.8545409e-01]
Sparsity at: 0.0
Epoch 187/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1461e-05 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6724823e-01
 -4.9656019e-01 -2.8577724e-01]
Sparsity at: 0.0
Epoch 188/500
235/235 [==============================] - 3s 13ms/step - loss: 9.2388e-06 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6856245e-01
 -4.9697095e-01 -2.8609407e-01]
Sparsity at: 0.0
Epoch 189/500
235/235 [==============================] - 3s 13ms/step - loss: 9.0506e-06 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.6950045e-01
 -4.9824056e-01 -2.8709444e-01]
Sparsity at: 0.0
Epoch 190/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0925e-05 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9837
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.7010698e-01
 -5.0036812e-01 -2.8691503e-01]
Sparsity at: 0.0
Epoch 191/500
235/235 [==============================] - 3s 13ms/step - loss: 7.2677e-04 - accuracy: 0.9999 - val_loss: 0.1199 - val_accuracy: 0.9806
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.7042623e-01
 -4.8601174e-01 -2.9815650e-01]
Sparsity at: 0.0
Epoch 192/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0097 - accuracy: 0.9970 - val_loss: 0.1167 - val_accuracy: 0.9804
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.8843267e-01
 -5.1557732e-01 -3.0202088e-01]
Sparsity at: 0.0
Epoch 193/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9989 - val_loss: 0.1044 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0070002e-01
 -5.1226181e-01 -3.0256060e-01]
Sparsity at: 0.0
Epoch 194/500
235/235 [==============================] - 3s 13ms/step - loss: 6.7385e-04 - accuracy: 0.9998 - val_loss: 0.0976 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  5.9982592e-01
 -5.2158481e-01 -2.9850927e-01]
Sparsity at: 0.0
Epoch 195/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4735e-04 - accuracy: 0.9999 - val_loss: 0.1038 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0121626e-01
 -5.2410632e-01 -2.9390094e-01]
Sparsity at: 0.0
Epoch 196/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2273e-04 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9843
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0332149e-01
 -5.1959753e-01 -2.9815087e-01]
Sparsity at: 0.0
Epoch 197/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7156e-05 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0324466e-01
 -5.2098483e-01 -2.9800597e-01]
Sparsity at: 0.0
Epoch 198/500
235/235 [==============================] - 3s 13ms/step - loss: 4.8372e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0329521e-01
 -5.2132809e-01 -2.9784495e-01]
Sparsity at: 0.0
Epoch 199/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2301e-05 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0370916e-01
 -5.2256691e-01 -2.9715100e-01]
Sparsity at: 0.0
Epoch 200/500
235/235 [==============================] - 3s 13ms/step - loss: 8.1394e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0448521e-01
 -5.2294427e-01 -2.9898939e-01]
Sparsity at: 0.0
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.2874537197822491
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.3186411199774071
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.5636029235072613
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 204s 11ms/step - loss: 8.4325e-04 - accuracy: 0.9998 - val_loss: 0.1146 - val_accuracy: 0.9826
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0950238e-01
 -4.9925497e-01 -3.3555302e-01]
Sparsity at: 0.0
Epoch 202/500
235/235 [==============================] - 3s 12ms/step - loss: 4.8157e-04 - accuracy: 0.9998 - val_loss: 0.1110 - val_accuracy: 0.9833
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2005216e-01
 -5.0502318e-01 -3.3103040e-01]
Sparsity at: 0.0
Epoch 203/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7465e-04 - accuracy: 0.9999 - val_loss: 0.1037 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1956078e-01
 -5.0318015e-01 -3.3429950e-01]
Sparsity at: 0.0
Epoch 204/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0407e-04 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1864352e-01
 -5.0518948e-01 -3.3523580e-01]
Sparsity at: 0.0
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1573e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2882578e-01
 -5.0963444e-01 -3.3437878e-01]
Sparsity at: 0.0
Epoch 206/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4746e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2864375e-01
 -5.0653112e-01 -3.3664536e-01]
Sparsity at: 0.0
Epoch 207/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.1144 - val_accuracy: 0.9819
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1742079e-01
 -5.0197595e-01 -3.3811587e-01]
Sparsity at: 0.0
Epoch 208/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1166 - val_accuracy: 0.9819
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3147867e-01
 -5.0792593e-01 -3.3347246e-01]
Sparsity at: 0.0
Epoch 209/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1295 - val_accuracy: 0.9800
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1288697e-01
 -5.3304881e-01 -3.4310421e-01]
Sparsity at: 0.0
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1074 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1201382e-01
 -5.4386103e-01 -3.5203356e-01]
Sparsity at: 0.0
Epoch 211/500
235/235 [==============================] - 4s 15ms/step - loss: 4.4846e-04 - accuracy: 0.9999 - val_loss: 0.1057 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1244524e-01
 -5.5831331e-01 -3.5050705e-01]
Sparsity at: 0.0
Epoch 212/500
235/235 [==============================] - 4s 15ms/step - loss: 6.1539e-04 - accuracy: 0.9998 - val_loss: 0.1031 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1057192e-01
 -5.4338759e-01 -3.5213125e-01]
Sparsity at: 0.0
Epoch 213/500
235/235 [==============================] - 4s 16ms/step - loss: 1.4683e-04 - accuracy: 1.0000 - val_loss: 0.1002 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1560261e-01
 -5.4469484e-01 -3.5494003e-01]
Sparsity at: 0.0
Epoch 214/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7782e-05 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9843
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1470604e-01
 -5.4480261e-01 -3.5545653e-01]
Sparsity at: 0.0
Epoch 215/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9864e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1483705e-01
 -5.4412687e-01 -3.5600471e-01]
Sparsity at: 0.0
Epoch 216/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9205e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9851
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1407834e-01
 -5.4772061e-01 -3.5647425e-01]
Sparsity at: 0.0
Epoch 217/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1620e-05 - accuracy: 1.0000 - val_loss: 0.1001 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1434227e-01
 -5.4673374e-01 -3.5614845e-01]
Sparsity at: 0.0
Epoch 218/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3827e-05 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1519468e-01
 -5.4605353e-01 -3.5597736e-01]
Sparsity at: 0.0
Epoch 219/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4964e-05 - accuracy: 1.0000 - val_loss: 0.0983 - val_accuracy: 0.9848
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1545092e-01
 -5.4578584e-01 -3.5559243e-01]
Sparsity at: 0.0
Epoch 220/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5088e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1640811e-01
 -5.4461604e-01 -3.5773984e-01]
Sparsity at: 0.0
Epoch 221/500
235/235 [==============================] - 3s 13ms/step - loss: 5.2197e-04 - accuracy: 0.9998 - val_loss: 0.1117 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2371159e-01
 -5.4464835e-01 -3.4565946e-01]
Sparsity at: 0.0
Epoch 222/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1337 - val_accuracy: 0.9816
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2378913e-01
 -5.5754745e-01 -3.4456840e-01]
Sparsity at: 0.0
Epoch 223/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0042 - accuracy: 0.9989 - val_loss: 0.1125 - val_accuracy: 0.9810
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1221707e-01
 -5.3908062e-01 -3.2429215e-01]
Sparsity at: 0.0
Epoch 224/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.1025 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0205883e-01
 -5.1781064e-01 -3.2461664e-01]
Sparsity at: 0.0
Epoch 225/500
235/235 [==============================] - 3s 13ms/step - loss: 5.0683e-04 - accuracy: 0.9998 - val_loss: 0.1032 - val_accuracy: 0.9833
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0636050e-01
 -5.3595763e-01 -3.3571878e-01]
Sparsity at: 0.0
Epoch 226/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5532e-04 - accuracy: 0.9999 - val_loss: 0.1008 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0159755e-01
 -5.3635305e-01 -3.3443895e-01]
Sparsity at: 0.0
Epoch 227/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4194e-05 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0271400e-01
 -5.3576726e-01 -3.3441862e-01]
Sparsity at: 0.0
Epoch 228/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1829e-05 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0365713e-01
 -5.3585714e-01 -3.3521560e-01]
Sparsity at: 0.0
Epoch 229/500
235/235 [==============================] - 3s 13ms/step - loss: 4.6280e-05 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0427231e-01
 -5.3575593e-01 -3.3483583e-01]
Sparsity at: 0.0
Epoch 230/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6473e-05 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0428178e-01
 -5.3447664e-01 -3.3509734e-01]
Sparsity at: 0.0
Epoch 231/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3108e-05 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0493410e-01
 -5.3514642e-01 -3.3485800e-01]
Sparsity at: 0.0
Epoch 232/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4552e-05 - accuracy: 1.0000 - val_loss: 0.1021 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0522890e-01
 -5.3617030e-01 -3.3513358e-01]
Sparsity at: 0.0
Epoch 233/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3754e-05 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0550493e-01
 -5.3695834e-01 -3.3578059e-01]
Sparsity at: 0.0
Epoch 234/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3490e-05 - accuracy: 1.0000 - val_loss: 0.1021 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0590279e-01
 -5.3796405e-01 -3.3490509e-01]
Sparsity at: 0.0
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5802e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0634965e-01
 -5.3727800e-01 -3.3665434e-01]
Sparsity at: 0.0
Epoch 236/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1274 - val_accuracy: 0.9801
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2343144e-01
 -5.2422190e-01 -3.4441346e-01]
Sparsity at: 0.0
Epoch 237/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9986 - val_loss: 0.1342 - val_accuracy: 0.9797
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0570931e-01
 -5.2343947e-01 -3.6213100e-01]
Sparsity at: 0.0
Epoch 238/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1082 - val_accuracy: 0.9818
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1439687e-01
 -5.1665121e-01 -3.5663036e-01]
Sparsity at: 0.0
Epoch 239/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1581e-04 - accuracy: 0.9999 - val_loss: 0.1090 - val_accuracy: 0.9825
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.0527170e-01
 -5.2081329e-01 -3.4511650e-01]
Sparsity at: 0.0
Epoch 240/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2663e-04 - accuracy: 0.9999 - val_loss: 0.1035 - val_accuracy: 0.9829
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2369365e-01
 -5.2157253e-01 -3.5754129e-01]
Sparsity at: 0.0
Epoch 241/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1863e-04 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3262939e-01
 -5.2139896e-01 -3.5675633e-01]
Sparsity at: 0.0
Epoch 242/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9487e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9828
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2323916e-01
 -5.2096403e-01 -3.5696104e-01]
Sparsity at: 0.0
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9562e-05 - accuracy: 1.0000 - val_loss: 0.1041 - val_accuracy: 0.9833
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2304211e-01
 -5.2134120e-01 -3.5657158e-01]
Sparsity at: 0.0
Epoch 244/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7930e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2343466e-01
 -5.2140492e-01 -3.5696056e-01]
Sparsity at: 0.0
Epoch 245/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8687e-05 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2266308e-01
 -5.2152824e-01 -3.5601309e-01]
Sparsity at: 0.0
Epoch 246/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3092e-05 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2398028e-01
 -5.2178323e-01 -3.5591933e-01]
Sparsity at: 0.0
Epoch 247/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6499e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9827
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2495810e-01
 -5.2217859e-01 -3.5527581e-01]
Sparsity at: 0.0
Epoch 248/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1571e-05 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2548947e-01
 -5.2138007e-01 -3.5780495e-01]
Sparsity at: 0.0
Epoch 249/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2561e-05 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2659645e-01
 -5.2187186e-01 -3.5933426e-01]
Sparsity at: 0.0
Epoch 250/500
235/235 [==============================] - 3s 13ms/step - loss: 9.4007e-06 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2719643e-01
 -5.2243006e-01 -3.5930550e-01]
Sparsity at: 0.0
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.38124093667401837
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.4081035179382546
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.6657418879585748
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 209s 11ms/step - loss: 1.2386e-05 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9837
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2878633e-01
 -5.2314866e-01 -3.5944691e-01]
Sparsity at: 0.0
Epoch 252/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2838e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9842
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2868971e-01
 -5.2224553e-01 -3.5884094e-01]
Sparsity at: 0.0
Epoch 253/500
235/235 [==============================] - 3s 13ms/step - loss: 7.0250e-04 - accuracy: 0.9998 - val_loss: 0.1213 - val_accuracy: 0.9817
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5136111e-01
 -5.2299011e-01 -3.5210609e-01]
Sparsity at: 0.0
Epoch 254/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.1222 - val_accuracy: 0.9821
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2739509e-01
 -4.9890453e-01 -3.3374158e-01]
Sparsity at: 0.0
Epoch 255/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.0960 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3421464e-01
 -5.2200115e-01 -3.3001900e-01]
Sparsity at: 0.0
Epoch 256/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9957e-04 - accuracy: 0.9998 - val_loss: 0.0928 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2529624e-01
 -5.2404702e-01 -3.2779345e-01]
Sparsity at: 0.0
Epoch 257/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0174e-04 - accuracy: 0.9999 - val_loss: 0.1015 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2676585e-01
 -5.2519840e-01 -3.2440919e-01]
Sparsity at: 0.0
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7626e-04 - accuracy: 0.9999 - val_loss: 0.1015 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.1347389e-01
 -5.2910566e-01 -3.2490751e-01]
Sparsity at: 0.0
Epoch 259/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4313e-04 - accuracy: 0.9999 - val_loss: 0.0976 - val_accuracy: 0.9843
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.2336451e-01
 -5.2847540e-01 -3.2726857e-01]
Sparsity at: 0.0
Epoch 260/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2480e-04 - accuracy: 0.9999 - val_loss: 0.1010 - val_accuracy: 0.9826
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3136858e-01
 -5.2624869e-01 -3.3697766e-01]
Sparsity at: 0.0
Epoch 261/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0950e-04 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3385004e-01
 -5.2697378e-01 -3.3868650e-01]
Sparsity at: 0.0
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5391e-05 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9833
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3998175e-01
 -5.2718550e-01 -3.3963835e-01]
Sparsity at: 0.0
Epoch 263/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6144e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3847589e-01
 -5.2757126e-01 -3.4037256e-01]
Sparsity at: 0.0
Epoch 264/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7041e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9833
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3930726e-01
 -5.2752674e-01 -3.4020925e-01]
Sparsity at: 0.0
Epoch 265/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1979e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3923216e-01
 -5.2727044e-01 -3.4029010e-01]
Sparsity at: 0.0
Epoch 266/500
235/235 [==============================] - 3s 13ms/step - loss: 9.7601e-06 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3910252e-01
 -5.2789539e-01 -3.4019661e-01]
Sparsity at: 0.0
Epoch 267/500
235/235 [==============================] - 3s 13ms/step - loss: 8.5079e-06 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.3944036e-01
 -5.2793550e-01 -3.4031159e-01]
Sparsity at: 0.0
Epoch 268/500
235/235 [==============================] - 3s 13ms/step - loss: 9.5232e-06 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4026928e-01
 -5.2946043e-01 -3.4021139e-01]
Sparsity at: 0.0
Epoch 269/500
235/235 [==============================] - 3s 13ms/step - loss: 9.5407e-06 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4353728e-01
 -5.2862191e-01 -3.4088215e-01]
Sparsity at: 0.0
Epoch 270/500
235/235 [==============================] - 3s 13ms/step - loss: 6.0790e-06 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4340949e-01
 -5.2913517e-01 -3.4093595e-01]
Sparsity at: 0.0
Epoch 271/500
235/235 [==============================] - 3s 13ms/step - loss: 7.2693e-06 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4388776e-01
 -5.2918202e-01 -3.4088764e-01]
Sparsity at: 0.0
Epoch 272/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1534e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4251763e-01
 -5.3003001e-01 -3.3886793e-01]
Sparsity at: 0.0
Epoch 273/500
235/235 [==============================] - 3s 13ms/step - loss: 6.5365e-06 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9842
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4144254e-01
 -5.3202510e-01 -3.3753058e-01]
Sparsity at: 0.0
Epoch 274/500
235/235 [==============================] - 3s 13ms/step - loss: 6.0968e-06 - accuracy: 1.0000 - val_loss: 0.0958 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4319319e-01
 -5.3212756e-01 -3.3813250e-01]
Sparsity at: 0.0
Epoch 275/500
235/235 [==============================] - 3s 13ms/step - loss: 4.5047e-06 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4375597e-01
 -5.3256160e-01 -3.3832178e-01]
Sparsity at: 0.0
Epoch 276/500
235/235 [==============================] - 3s 13ms/step - loss: 4.0697e-06 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4356261e-01
 -5.3301734e-01 -3.3845496e-01]
Sparsity at: 0.0
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0959e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4830291e-01
 -5.3391296e-01 -3.3869076e-01]
Sparsity at: 0.0
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5371e-06 - accuracy: 1.0000 - val_loss: 0.0966 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4803529e-01
 -5.3480995e-01 -3.3888236e-01]
Sparsity at: 0.0
Epoch 279/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1643e-06 - accuracy: 1.0000 - val_loss: 0.0969 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4931709e-01
 -5.3604400e-01 -3.3942103e-01]
Sparsity at: 0.0
Epoch 280/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5175e-06 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9842
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4941317e-01
 -5.3699249e-01 -3.3933860e-01]
Sparsity at: 0.0
Epoch 281/500
235/235 [==============================] - 3s 13ms/step - loss: 2.8655e-06 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 0.9843
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5151393e-01
 -5.3807986e-01 -3.3978361e-01]
Sparsity at: 0.0
Epoch 282/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6941e-06 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5202975e-01
 -5.3808928e-01 -3.3889192e-01]
Sparsity at: 0.0
Epoch 283/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4472e-06 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5303344e-01
 -5.3946525e-01 -3.4164351e-01]
Sparsity at: 0.0
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3050e-06 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5430802e-01
 -5.4000169e-01 -3.4160930e-01]
Sparsity at: 0.0
Epoch 285/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7688e-06 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5452904e-01
 -5.4090899e-01 -3.4107906e-01]
Sparsity at: 0.0
Epoch 286/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7692e-06 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5524411e-01
 -5.4166412e-01 -3.4125334e-01]
Sparsity at: 0.0
Epoch 287/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4157e-06 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9843
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5493286e-01
 -5.4281932e-01 -3.4182611e-01]
Sparsity at: 0.0
Epoch 288/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2568e-06 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5572679e-01
 -5.4265201e-01 -3.4187028e-01]
Sparsity at: 0.0
Epoch 289/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9160e-06 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5667081e-01
 -5.4489005e-01 -3.4288964e-01]
Sparsity at: 0.0
Epoch 290/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1954e-06 - accuracy: 1.0000 - val_loss: 0.0993 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5741098e-01
 -5.4669249e-01 -3.4242389e-01]
Sparsity at: 0.0
Epoch 291/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2667e-06 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5799427e-01
 -5.5042946e-01 -3.4277761e-01]
Sparsity at: 0.0
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5017e-05 - accuracy: 1.0000 - val_loss: 0.1853 - val_accuracy: 0.9760
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5678263e-01
 -5.5002117e-01 -3.4314448e-01]
Sparsity at: 0.0
Epoch 293/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0093 - accuracy: 0.9975 - val_loss: 0.1343 - val_accuracy: 0.9806
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.6712904e-01
 -5.5039155e-01 -3.6629134e-01]
Sparsity at: 0.0
Epoch 294/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1173 - val_accuracy: 0.9818
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4340782e-01
 -5.5193847e-01 -3.7858319e-01]
Sparsity at: 0.0
Epoch 295/500
235/235 [==============================] - 3s 13ms/step - loss: 5.7169e-04 - accuracy: 0.9998 - val_loss: 0.1111 - val_accuracy: 0.9827
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4791560e-01
 -5.5488485e-01 -3.7428433e-01]
Sparsity at: 0.0
Epoch 296/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3328e-04 - accuracy: 0.9999 - val_loss: 0.1085 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4995569e-01
 -5.5822307e-01 -3.7549898e-01]
Sparsity at: 0.0
Epoch 297/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3872e-04 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9823
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5111738e-01
 -5.5866361e-01 -3.7619758e-01]
Sparsity at: 0.0
Epoch 298/500
235/235 [==============================] - 3s 13ms/step - loss: 6.9456e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9829
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4915556e-01
 -5.5945152e-01 -3.7620771e-01]
Sparsity at: 0.0
Epoch 299/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9311e-05 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9828
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4924091e-01
 -5.5903774e-01 -3.7618771e-01]
Sparsity at: 0.0
Epoch 300/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4901e-05 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.4857632e-01
 -5.5785728e-01 -3.7585333e-01]
Sparsity at: 0.0
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.4840575341487643
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.5021257151600054
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.7491546624237984
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 210s 11ms/step - loss: 6.5044e-05 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9828
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5228260e-01
 -5.5734634e-01 -3.7600517e-01]
Sparsity at: 0.0
Epoch 302/500
235/235 [==============================] - 3s 12ms/step - loss: 3.7288e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5375954e-01
 -5.5553335e-01 -3.7614349e-01]
Sparsity at: 0.0
Epoch 303/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6152e-05 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5406585e-01
 -5.6139666e-01 -3.7696561e-01]
Sparsity at: 0.0
Epoch 304/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4945e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5418690e-01
 -5.6010503e-01 -3.7628663e-01]
Sparsity at: 0.0
Epoch 305/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1716e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5429169e-01
 -5.6056571e-01 -3.7585855e-01]
Sparsity at: 0.0
Epoch 306/500
235/235 [==============================] - 3s 13ms/step - loss: 7.4003e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9823
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5462208e-01
 -5.6075162e-01 -3.7579769e-01]
Sparsity at: 0.0
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1395 - val_accuracy: 0.9799
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.5355134e-01
 -5.2712643e-01 -3.8587490e-01]
Sparsity at: 0.0
Epoch 308/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0021 - accuracy: 0.9993 - val_loss: 0.1451 - val_accuracy: 0.9794
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7497033e-01
 -5.3689229e-01 -3.9461696e-01]
Sparsity at: 0.0
Epoch 309/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1170 - val_accuracy: 0.9812
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.6212726e-01
 -5.4914683e-01 -4.0399969e-01]
Sparsity at: 0.0
Epoch 310/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1239 - val_accuracy: 0.9816
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.6534543e-01
 -5.5245560e-01 -3.7220427e-01]
Sparsity at: 0.0
Epoch 311/500
235/235 [==============================] - 3s 13ms/step - loss: 8.3574e-04 - accuracy: 0.9998 - val_loss: 0.1141 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7173475e-01
 -5.5312985e-01 -3.7821293e-01]
Sparsity at: 0.0
Epoch 312/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0177e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7269552e-01
 -5.5374986e-01 -3.8368362e-01]
Sparsity at: 0.0
Epoch 313/500
235/235 [==============================] - 3s 13ms/step - loss: 3.5104e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7167526e-01
 -5.5432463e-01 -3.8489488e-01]
Sparsity at: 0.0
Epoch 314/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6261e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7177296e-01
 -5.5459368e-01 -3.8556916e-01]
Sparsity at: 0.0
Epoch 315/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6205e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7240047e-01
 -5.5320251e-01 -3.8584906e-01]
Sparsity at: 0.0
Epoch 316/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4158e-05 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7290407e-01
 -5.5384910e-01 -3.8663006e-01]
Sparsity at: 0.0
Epoch 317/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3918e-05 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7150152e-01
 -5.5443913e-01 -3.8646147e-01]
Sparsity at: 0.0
Epoch 318/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5730e-05 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7176443e-01
 -5.5571240e-01 -3.8649982e-01]
Sparsity at: 0.0
Epoch 319/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2412e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7181522e-01
 -5.5701846e-01 -3.8615495e-01]
Sparsity at: 0.0
Epoch 320/500
235/235 [==============================] - 3s 13ms/step - loss: 8.8805e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7128992e-01
 -5.5714381e-01 -3.8658482e-01]
Sparsity at: 0.0
Epoch 321/500
235/235 [==============================] - 3s 13ms/step - loss: 7.4642e-06 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9842
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7117435e-01
 -5.5738598e-01 -3.8607830e-01]
Sparsity at: 0.0
Epoch 322/500
235/235 [==============================] - 3s 13ms/step - loss: 8.3088e-06 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9843
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.7052221e-01
 -5.5736411e-01 -3.8592941e-01]
Sparsity at: 0.0
Epoch 323/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1410 - val_accuracy: 0.9797
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.6706741e-01
 -5.7001120e-01 -3.9759171e-01]
Sparsity at: 0.0
Epoch 324/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1418 - val_accuracy: 0.9801
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.8438065e-01
 -5.2542758e-01 -3.6692393e-01]
Sparsity at: 0.0
Epoch 325/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1284 - val_accuracy: 0.9821
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1262634e-01
 -5.4822046e-01 -3.8940325e-01]
Sparsity at: 0.0
Epoch 326/500
235/235 [==============================] - 3s 13ms/step - loss: 8.9549e-04 - accuracy: 0.9998 - val_loss: 0.1225 - val_accuracy: 0.9821
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0459276e-01
 -5.5135691e-01 -3.7489915e-01]
Sparsity at: 0.0
Epoch 327/500
235/235 [==============================] - 3s 13ms/step - loss: 6.2977e-04 - accuracy: 0.9998 - val_loss: 0.1173 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0080155e-01
 -5.6030804e-01 -3.7608519e-01]
Sparsity at: 0.0
Epoch 328/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2176e-04 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9837
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.9982362e-01
 -5.6664425e-01 -3.7336165e-01]
Sparsity at: 0.0
Epoch 329/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0171e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9837
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  6.9964826e-01
 -5.6587100e-01 -3.7368536e-01]
Sparsity at: 0.0
Epoch 330/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5569e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0078719e-01
 -5.6707168e-01 -3.7355444e-01]
Sparsity at: 0.0
Epoch 331/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7089e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0200819e-01
 -5.6650913e-01 -3.7195584e-01]
Sparsity at: 0.0
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4384e-05 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0109540e-01
 -5.6838727e-01 -3.7140197e-01]
Sparsity at: 0.0
Epoch 333/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1887e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0155942e-01
 -5.6862968e-01 -3.7182504e-01]
Sparsity at: 0.0
Epoch 334/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0908e-04 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0635587e-01
 -5.6764275e-01 -3.7442493e-01]
Sparsity at: 0.0
Epoch 335/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3605e-05 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9842
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0906579e-01
 -5.6291771e-01 -3.7445685e-01]
Sparsity at: 0.0
Epoch 336/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5988e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0489526e-01
 -5.6233752e-01 -3.7392166e-01]
Sparsity at: 0.0
Epoch 337/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8824e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9848
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0397627e-01
 -5.6645334e-01 -3.6914515e-01]
Sparsity at: 0.0
Epoch 338/500
235/235 [==============================] - 3s 13ms/step - loss: 9.5715e-06 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0395643e-01
 -5.6660706e-01 -3.6929032e-01]
Sparsity at: 0.0
Epoch 339/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0100e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0404893e-01
 -5.6640774e-01 -3.6937401e-01]
Sparsity at: 0.0
Epoch 340/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1861e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0460624e-01
 -5.6706387e-01 -3.7056348e-01]
Sparsity at: 0.0
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0278e-05 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0501941e-01
 -5.6745756e-01 -3.7264466e-01]
Sparsity at: 0.0
Epoch 342/500
235/235 [==============================] - 3s 13ms/step - loss: 5.9632e-05 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0786303e-01
 -5.6676489e-01 -3.7610215e-01]
Sparsity at: 0.0
Epoch 343/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1395 - val_accuracy: 0.9809
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5297624e-01
 -5.6421214e-01 -4.0648663e-01]
Sparsity at: 0.0
Epoch 344/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.1281 - val_accuracy: 0.9807
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1896613e-01
 -5.6390363e-01 -3.9479387e-01]
Sparsity at: 0.0
Epoch 345/500
235/235 [==============================] - 3s 13ms/step - loss: 8.3142e-04 - accuracy: 0.9997 - val_loss: 0.1125 - val_accuracy: 0.9826
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.2380459e-01
 -5.6272143e-01 -3.9444566e-01]
Sparsity at: 0.0
Epoch 346/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6689e-04 - accuracy: 0.9999 - val_loss: 0.1127 - val_accuracy: 0.9819
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1746206e-01
 -5.6643170e-01 -3.9546755e-01]
Sparsity at: 0.0
Epoch 347/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2436e-04 - accuracy: 1.0000 - val_loss: 0.1128 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1617639e-01
 -5.6855470e-01 -3.9525202e-01]
Sparsity at: 0.0
Epoch 348/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1092e-04 - accuracy: 0.9999 - val_loss: 0.1148 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1348655e-01
 -5.6832910e-01 -3.9307153e-01]
Sparsity at: 0.0
Epoch 349/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5693e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1369278e-01
 -5.6889236e-01 -3.9391711e-01]
Sparsity at: 0.0
Epoch 350/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6908e-05 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1477169e-01
 -5.6885922e-01 -3.9647427e-01]
Sparsity at: 0.0
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.5940021586559041
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.5993241925912614
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.8300940364296068
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 214s 11ms/step - loss: 1.8836e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1529114e-01
 -5.6959337e-01 -3.9495748e-01]
Sparsity at: 0.0
Epoch 352/500
235/235 [==============================] - 3s 12ms/step - loss: 1.4700e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1578276e-01
 -5.7047671e-01 -3.9545009e-01]
Sparsity at: 0.0
Epoch 353/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5937e-05 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.2179407e-01
 -5.7083851e-01 -3.9582655e-01]
Sparsity at: 0.0
Epoch 354/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7916e-04 - accuracy: 0.9999 - val_loss: 0.1170 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.2937107e-01
 -5.6852484e-01 -4.0021750e-01]
Sparsity at: 0.0
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1260 - val_accuracy: 0.9837
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1368587e-01
 -5.7733423e-01 -3.8858193e-01]
Sparsity at: 0.0
Epoch 356/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1325 - val_accuracy: 0.9808
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1448648e-01
 -5.6969279e-01 -3.8571319e-01]
Sparsity at: 0.0
Epoch 357/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1265 - val_accuracy: 0.9820
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0180959e-01
 -5.7633668e-01 -4.0235785e-01]
Sparsity at: 0.0
Epoch 358/500
235/235 [==============================] - 3s 13ms/step - loss: 4.8881e-04 - accuracy: 0.9999 - val_loss: 0.1182 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0467728e-01
 -5.6393576e-01 -4.0121877e-01]
Sparsity at: 0.0
Epoch 359/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0939e-04 - accuracy: 0.9999 - val_loss: 0.1183 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0579892e-01
 -5.6408697e-01 -4.0253165e-01]
Sparsity at: 0.0
Epoch 360/500
235/235 [==============================] - 3s 13ms/step - loss: 6.9575e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0301265e-01
 -5.6143004e-01 -4.0327480e-01]
Sparsity at: 0.0
Epoch 361/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3759e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0206022e-01
 -5.5868477e-01 -4.0360621e-01]
Sparsity at: 0.0
Epoch 362/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6491e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0208901e-01
 -5.6069535e-01 -4.0419844e-01]
Sparsity at: 0.0
Epoch 363/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5677e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9851
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0221454e-01
 -5.6052208e-01 -4.0401483e-01]
Sparsity at: 0.0
Epoch 364/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0043e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0208615e-01
 -5.6182092e-01 -4.0402654e-01]
Sparsity at: 0.0
Epoch 365/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4164e-05 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0283574e-01
 -5.6466419e-01 -4.0959105e-01]
Sparsity at: 0.0
Epoch 366/500
235/235 [==============================] - 3s 13ms/step - loss: 2.8409e-04 - accuracy: 0.9999 - val_loss: 0.1247 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0692307e-01
 -5.6885320e-01 -4.0941599e-01]
Sparsity at: 0.0
Epoch 367/500
235/235 [==============================] - 3s 13ms/step - loss: 7.9737e-05 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9858
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0750093e-01
 -5.7378286e-01 -4.0056372e-01]
Sparsity at: 0.0
Epoch 368/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4833e-05 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9857
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0882583e-01
 -5.7588649e-01 -4.0319535e-01]
Sparsity at: 0.0
Epoch 369/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4380e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9862
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0953685e-01
 -5.8122599e-01 -4.0075362e-01]
Sparsity at: 0.0
Epoch 370/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4355e-05 - accuracy: 1.0000 - val_loss: 0.1111 - val_accuracy: 0.9855
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.0993143e-01
 -5.9116876e-01 -3.8649681e-01]
Sparsity at: 0.0
Epoch 371/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0129e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9854
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1026665e-01
 -5.9196216e-01 -3.8612637e-01]
Sparsity at: 0.0
Epoch 372/500
235/235 [==============================] - 3s 13ms/step - loss: 5.7895e-06 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9857
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1015251e-01
 -5.9211612e-01 -3.8570943e-01]
Sparsity at: 0.0
Epoch 373/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9729e-06 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9854
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1017760e-01
 -5.9285569e-01 -3.8497800e-01]
Sparsity at: 0.0
Epoch 374/500
235/235 [==============================] - 3s 13ms/step - loss: 5.1339e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9856
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1025163e-01
 -5.9300107e-01 -3.8514194e-01]
Sparsity at: 0.0
Epoch 375/500
235/235 [==============================] - 3s 13ms/step - loss: 5.2927e-06 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1067280e-01
 -5.9037477e-01 -3.8439739e-01]
Sparsity at: 0.0
Epoch 376/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3205e-06 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9855
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1095383e-01
 -5.9109157e-01 -3.8548061e-01]
Sparsity at: 0.0
Epoch 377/500
235/235 [==============================] - 3s 13ms/step - loss: 2.8044e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9855
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1098715e-01
 -5.9215111e-01 -3.8554680e-01]
Sparsity at: 0.0
Epoch 378/500
235/235 [==============================] - 3s 13ms/step - loss: 3.5292e-06 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9854
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1126789e-01
 -5.9289366e-01 -3.8870525e-01]
Sparsity at: 0.0
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7128e-06 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9855
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1131331e-01
 -5.9336215e-01 -3.8855308e-01]
Sparsity at: 0.0
Epoch 380/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5147e-06 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9853
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1130067e-01
 -5.9428436e-01 -3.8712949e-01]
Sparsity at: 0.0
Epoch 381/500
235/235 [==============================] - 3s 13ms/step - loss: 4.3417e-06 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1592885e-01
 -5.9577453e-01 -3.8571432e-01]
Sparsity at: 0.0
Epoch 382/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4059e-06 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9851
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1367675e-01
 -5.9682691e-01 -3.8676134e-01]
Sparsity at: 0.0
Epoch 383/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1154e-06 - accuracy: 1.0000 - val_loss: 0.1111 - val_accuracy: 0.9854
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1473700e-01
 -5.9591788e-01 -3.8876802e-01]
Sparsity at: 0.0
Epoch 384/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7320e-06 - accuracy: 1.0000 - val_loss: 0.1112 - val_accuracy: 0.9855
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1423244e-01
 -5.9560019e-01 -3.8925385e-01]
Sparsity at: 0.0
Epoch 385/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3127e-06 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9853
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1459347e-01
 -5.9645391e-01 -3.8985214e-01]
Sparsity at: 0.0
Epoch 386/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0702e-06 - accuracy: 1.0000 - val_loss: 0.1110 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1544689e-01
 -5.9748000e-01 -3.8986361e-01]
Sparsity at: 0.0
Epoch 387/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5183e-06 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1546721e-01
 -5.9847289e-01 -3.8869891e-01]
Sparsity at: 0.0
Epoch 388/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2015e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9851
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1595049e-01
 -6.0001868e-01 -3.8917929e-01]
Sparsity at: 0.0
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1568e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1584302e-01
 -6.0047722e-01 -3.8693663e-01]
Sparsity at: 0.0
Epoch 390/500
235/235 [==============================] - 3s 13ms/step - loss: 8.5372e-07 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.1574992e-01
 -6.0128659e-01 -3.8694969e-01]
Sparsity at: 0.0
Epoch 391/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9995 - val_loss: 0.1903 - val_accuracy: 0.9718
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.7610165e-01
 -5.9374231e-01 -4.4736576e-01]
Sparsity at: 0.0
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 0.1312 - val_accuracy: 0.9809
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.2421026e-01
 -5.7925481e-01 -4.3199533e-01]
Sparsity at: 0.0
Epoch 393/500
235/235 [==============================] - 3s 13ms/step - loss: 7.5205e-04 - accuracy: 0.9998 - val_loss: 0.1178 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.2492504e-01
 -5.7882887e-01 -4.1366154e-01]
Sparsity at: 0.0
Epoch 394/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6470e-04 - accuracy: 0.9999 - val_loss: 0.1091 - val_accuracy: 0.9842
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3260576e-01
 -5.7625413e-01 -4.2663273e-01]
Sparsity at: 0.0
Epoch 395/500
235/235 [==============================] - 3s 13ms/step - loss: 7.8114e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3305577e-01
 -5.8003402e-01 -4.2172727e-01]
Sparsity at: 0.0
Epoch 396/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4253e-05 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9848
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3229116e-01
 -5.8007765e-01 -4.2126936e-01]
Sparsity at: 0.0
Epoch 397/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6762e-05 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3286659e-01
 -5.7917684e-01 -4.2010236e-01]
Sparsity at: 0.0
Epoch 398/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5279e-04 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3296195e-01
 -5.7867706e-01 -4.2213607e-01]
Sparsity at: 0.0
Epoch 399/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2817e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9854
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3408920e-01
 -5.7804376e-01 -4.2255884e-01]
Sparsity at: 0.0
Epoch 400/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1646e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9854
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3430389e-01
 -5.7770503e-01 -4.2296356e-01]
Sparsity at: 0.0
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.6727506433394481
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.6748570536334668
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.8916560984470863
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 209s 11ms/step - loss: 1.0476e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9856
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3461992e-01
 -5.7798070e-01 -4.2328224e-01]
Sparsity at: 0.0
Epoch 402/500
235/235 [==============================] - 3s 12ms/step - loss: 1.0708e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9857
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3501724e-01
 -5.7910603e-01 -4.2368123e-01]
Sparsity at: 0.0
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 9.4804e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9862
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3581117e-01
 -5.7880759e-01 -4.2384246e-01]
Sparsity at: 0.0
Epoch 404/500
235/235 [==============================] - 3s 13ms/step - loss: 2.8060e-05 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9853
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3704863e-01
 -5.7966167e-01 -4.2404339e-01]
Sparsity at: 0.0
Epoch 405/500
235/235 [==============================] - 3s 13ms/step - loss: 9.8769e-06 - accuracy: 1.0000 - val_loss: 0.1020 - val_accuracy: 0.9855
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3696053e-01
 -5.8020210e-01 -4.2402256e-01]
Sparsity at: 0.0
Epoch 406/500
235/235 [==============================] - 3s 13ms/step - loss: 8.4792e-06 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9853
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3773038e-01
 -5.8093137e-01 -4.2508972e-01]
Sparsity at: 0.0
Epoch 407/500
235/235 [==============================] - 3s 13ms/step - loss: 5.1250e-06 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9856
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3812926e-01
 -5.8083844e-01 -4.2523092e-01]
Sparsity at: 0.0
Epoch 408/500
235/235 [==============================] - 3s 13ms/step - loss: 4.6084e-06 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9853
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3855639e-01
 -5.8066791e-01 -4.2530608e-01]
Sparsity at: 0.0
Epoch 409/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1166 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4693584e-01
 -5.7909679e-01 -4.2715743e-01]
Sparsity at: 0.0
Epoch 410/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.1236 - val_accuracy: 0.9829
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3885489e-01
 -5.7413155e-01 -4.0021637e-01]
Sparsity at: 0.0
Epoch 411/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1124 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4534070e-01
 -5.7154894e-01 -3.6661658e-01]
Sparsity at: 0.0
Epoch 412/500
235/235 [==============================] - 3s 13ms/step - loss: 6.6900e-04 - accuracy: 0.9998 - val_loss: 0.1116 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5580573e-01
 -5.7684404e-01 -3.8299996e-01]
Sparsity at: 0.0
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0554e-04 - accuracy: 0.9999 - val_loss: 0.1111 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5910127e-01
 -5.8389193e-01 -3.8520378e-01]
Sparsity at: 0.0
Epoch 414/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9216e-04 - accuracy: 0.9999 - val_loss: 0.1103 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.7360153e-01
 -5.8851850e-01 -3.8928694e-01]
Sparsity at: 0.0
Epoch 415/500
235/235 [==============================] - 3s 13ms/step - loss: 8.0992e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.7152365e-01
 -5.8784527e-01 -3.9880115e-01]
Sparsity at: 0.0
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6261e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.7058154e-01
 -5.8703071e-01 -3.9640656e-01]
Sparsity at: 0.0
Epoch 417/500
235/235 [==============================] - 4s 15ms/step - loss: 2.1384e-05 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6968920e-01
 -5.8879471e-01 -3.9701700e-01]
Sparsity at: 0.0
Epoch 418/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1601e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6954162e-01
 -5.8925849e-01 -3.9707559e-01]
Sparsity at: 0.0
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 8.6450e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6944101e-01
 -5.8986121e-01 -3.9709815e-01]
Sparsity at: 0.0
Epoch 420/500
235/235 [==============================] - 3s 13ms/step - loss: 7.0065e-06 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6967978e-01
 -5.9008545e-01 -3.9717415e-01]
Sparsity at: 0.0
Epoch 421/500
235/235 [==============================] - 3s 13ms/step - loss: 6.1661e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6935273e-01
 -5.8983612e-01 -3.9734587e-01]
Sparsity at: 0.0
Epoch 422/500
235/235 [==============================] - 3s 13ms/step - loss: 5.6247e-06 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6958573e-01
 -5.9043860e-01 -3.9740649e-01]
Sparsity at: 0.0
Epoch 423/500
235/235 [==============================] - 3s 13ms/step - loss: 6.2170e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6981461e-01
 -5.9111857e-01 -3.9734247e-01]
Sparsity at: 0.0
Epoch 424/500
235/235 [==============================] - 3s 13ms/step - loss: 5.4751e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6967418e-01
 -5.9147847e-01 -3.9737681e-01]
Sparsity at: 0.0
Epoch 425/500
235/235 [==============================] - 3s 13ms/step - loss: 5.1320e-06 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.7006787e-01
 -5.9181589e-01 -3.9693892e-01]
Sparsity at: 0.0
Epoch 426/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2057e-06 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9848
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6945156e-01
 -5.9196174e-01 -3.9696586e-01]
Sparsity at: 0.0
Epoch 427/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4202e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6864702e-01
 -5.9245372e-01 -3.9619201e-01]
Sparsity at: 0.0
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0746e-06 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6872611e-01
 -5.9274894e-01 -3.9603540e-01]
Sparsity at: 0.0
Epoch 429/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7672e-06 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6917666e-01
 -5.9277546e-01 -3.9620230e-01]
Sparsity at: 0.0
Epoch 430/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7326e-06 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6946557e-01
 -5.9260476e-01 -3.9626563e-01]
Sparsity at: 0.0
Epoch 431/500
235/235 [==============================] - 3s 13ms/step - loss: 4.0894e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6955831e-01
 -5.9552687e-01 -3.9629373e-01]
Sparsity at: 0.0
Epoch 432/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1424e-06 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9851
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6966625e-01
 -5.9631538e-01 -3.9657286e-01]
Sparsity at: 0.0
Epoch 433/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5376e-06 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9848
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6966262e-01
 -5.9683400e-01 -3.9721814e-01]
Sparsity at: 0.0
Epoch 434/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1321e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6971877e-01
 -5.9718692e-01 -3.9651179e-01]
Sparsity at: 0.0
Epoch 435/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6499e-06 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6947546e-01
 -5.9729528e-01 -3.9698821e-01]
Sparsity at: 0.0
Epoch 436/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2924e-06 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6969296e-01
 -5.9761596e-01 -3.9709359e-01]
Sparsity at: 0.0
Epoch 437/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3264e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.7013183e-01
 -5.9810382e-01 -3.9718273e-01]
Sparsity at: 0.0
Epoch 438/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6109e-04 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9758
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6544523e-01
 -5.9299684e-01 -3.9017054e-01]
Sparsity at: 0.0
Epoch 439/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0085 - accuracy: 0.9977 - val_loss: 0.1308 - val_accuracy: 0.9816
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3219794e-01
 -5.9769565e-01 -3.4375000e-01]
Sparsity at: 0.0
Epoch 440/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.1114 - val_accuracy: 0.9844
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4667168e-01
 -6.1183149e-01 -3.3176506e-01]
Sparsity at: 0.0
Epoch 441/500
235/235 [==============================] - 3s 15ms/step - loss: 2.9384e-04 - accuracy: 0.9999 - val_loss: 0.1024 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4285513e-01
 -6.1646193e-01 -3.4001493e-01]
Sparsity at: 0.0
Epoch 442/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4309e-04 - accuracy: 0.9999 - val_loss: 0.1053 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4833882e-01
 -6.1934280e-01 -3.5285234e-01]
Sparsity at: 0.0
Epoch 443/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2678e-05 - accuracy: 1.0000 - val_loss: 0.1044 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4881679e-01
 -6.1997378e-01 -3.5236308e-01]
Sparsity at: 0.0
Epoch 444/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7894e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4862301e-01
 -6.2043577e-01 -3.5233986e-01]
Sparsity at: 0.0
Epoch 445/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6884e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9833
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4912566e-01
 -6.2048554e-01 -3.5119575e-01]
Sparsity at: 0.0
Epoch 446/500
235/235 [==============================] - 3s 13ms/step - loss: 6.8490e-05 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4937463e-01
 -6.2040502e-01 -3.5087770e-01]
Sparsity at: 0.0
Epoch 447/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4858e-05 - accuracy: 1.0000 - val_loss: 0.0996 - val_accuracy: 0.9847
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5340468e-01
 -6.2293601e-01 -3.5134894e-01]
Sparsity at: 0.0
Epoch 448/500
235/235 [==============================] - 3s 13ms/step - loss: 7.8609e-05 - accuracy: 1.0000 - val_loss: 0.1031 - val_accuracy: 0.9836
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5069332e-01
 -6.2446421e-01 -3.4712365e-01]
Sparsity at: 0.0
Epoch 449/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0402e-04 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5074881e-01
 -6.2054253e-01 -3.4951410e-01]
Sparsity at: 0.0
Epoch 450/500
235/235 [==============================] - 3s 13ms/step - loss: 3.5749e-04 - accuracy: 0.9999 - val_loss: 0.1043 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5251329e-01
 -6.1540824e-01 -3.6382011e-01]
Sparsity at: 0.0
Epoch 451/500
235/235 [==============================] - 3s 13ms/step - loss: 6.7539e-04 - accuracy: 0.9999 - val_loss: 0.1120 - val_accuracy: 0.9842
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5435370e-01
 -6.0671258e-01 -3.6066976e-01]
Sparsity at: 0.0
Epoch 452/500
235/235 [==============================] - 3s 13ms/step - loss: 5.6391e-04 - accuracy: 0.9999 - val_loss: 0.1072 - val_accuracy: 0.9843
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5592023e-01
 -6.0904813e-01 -3.6552656e-01]
Sparsity at: 0.0
Epoch 453/500
235/235 [==============================] - 3s 13ms/step - loss: 9.3323e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9851
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5613189e-01
 -6.1462474e-01 -3.6918551e-01]
Sparsity at: 0.0
Epoch 454/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2709e-04 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9851
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6059026e-01
 -6.0792422e-01 -3.7340620e-01]
Sparsity at: 0.0
Epoch 455/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0792e-05 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9845
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6127696e-01
 -6.0936952e-01 -3.7385431e-01]
Sparsity at: 0.0
Epoch 456/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5655e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6228040e-01
 -6.0754859e-01 -3.7487000e-01]
Sparsity at: 0.0
Epoch 457/500
235/235 [==============================] - 3s 13ms/step - loss: 8.4194e-06 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9851
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6223189e-01
 -6.0826296e-01 -3.7493998e-01]
Sparsity at: 0.0
Epoch 458/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6868e-04 - accuracy: 0.9999 - val_loss: 0.1174 - val_accuracy: 0.9841
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4726385e-01
 -6.0825342e-01 -3.4532258e-01]
Sparsity at: 0.0
Epoch 459/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5223e-04 - accuracy: 0.9999 - val_loss: 0.1241 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.2740740e-01
 -6.1075622e-01 -3.2073340e-01]
Sparsity at: 0.0
Epoch 460/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1233e-04 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9839
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.2967494e-01
 -6.0117024e-01 -3.2066119e-01]
Sparsity at: 0.0
Epoch 461/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3525e-04 - accuracy: 0.9999 - val_loss: 0.1119 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4135548e-01
 -5.9574664e-01 -3.2225445e-01]
Sparsity at: 0.0
Epoch 462/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.1330 - val_accuracy: 0.9814
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6414126e-01
 -6.5896976e-01 -3.3058769e-01]
Sparsity at: 0.0
Epoch 463/500
235/235 [==============================] - 3s 13ms/step - loss: 6.7661e-04 - accuracy: 0.9998 - val_loss: 0.1216 - val_accuracy: 0.9827
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4377865e-01
 -6.6160327e-01 -3.0569476e-01]
Sparsity at: 0.0
Epoch 464/500
235/235 [==============================] - 3s 13ms/step - loss: 6.7809e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9842
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4701214e-01
 -6.5026665e-01 -3.1252399e-01]
Sparsity at: 0.0
Epoch 465/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1783e-05 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4871802e-01
 -6.5005457e-01 -3.1413868e-01]
Sparsity at: 0.0
Epoch 466/500
235/235 [==============================] - 3s 12ms/step - loss: 1.7099e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9846
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4841762e-01
 -6.4922339e-01 -3.1461388e-01]
Sparsity at: 0.0
Epoch 467/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1302e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4794185e-01
 -6.4062595e-01 -3.1482184e-01]
Sparsity at: 0.0
Epoch 468/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4846e-05 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4610692e-01
 -6.4239001e-01 -3.1474069e-01]
Sparsity at: 0.0
Epoch 469/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0385e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9849
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4585545e-01
 -6.4170700e-01 -3.1495425e-01]
Sparsity at: 0.0
Epoch 470/500
235/235 [==============================] - 3s 13ms/step - loss: 9.3403e-06 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9850
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4596298e-01
 -6.3994187e-01 -3.1587914e-01]
Sparsity at: 0.0
Epoch 471/500
235/235 [==============================] - 3s 13ms/step - loss: 7.6517e-06 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9853
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4574560e-01
 -6.3983369e-01 -3.1573099e-01]
Sparsity at: 0.0
Epoch 472/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8014e-06 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9854
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4720848e-01
 -6.3993770e-01 -3.1616858e-01]
Sparsity at: 0.0
Epoch 473/500
235/235 [==============================] - 3s 13ms/step - loss: 6.6018e-06 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9853
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4711269e-01
 -6.4036345e-01 -3.1714511e-01]
Sparsity at: 0.0
Epoch 474/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9154e-06 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4754840e-01
 -6.4072776e-01 -3.1738245e-01]
Sparsity at: 0.0
Epoch 475/500
235/235 [==============================] - 3s 13ms/step - loss: 5.2430e-06 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9852
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4876243e-01
 -6.4267790e-01 -3.1763420e-01]
Sparsity at: 0.0
Epoch 476/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3790e-06 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9853
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4699795e-01
 -6.4273345e-01 -3.1575289e-01]
Sparsity at: 0.0
Epoch 477/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2744e-05 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9854
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4720383e-01
 -6.4004707e-01 -3.1602839e-01]
Sparsity at: 0.0
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1503 - val_accuracy: 0.9809
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.3536319e-01
 -5.8803338e-01 -3.1316033e-01]
Sparsity at: 0.0
Epoch 479/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1369 - val_accuracy: 0.9822
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6523989e-01
 -6.0393202e-01 -3.2994890e-01]
Sparsity at: 0.0
Epoch 480/500
235/235 [==============================] - 3s 13ms/step - loss: 4.8271e-04 - accuracy: 0.9998 - val_loss: 0.1222 - val_accuracy: 0.9835
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6797712e-01
 -6.0805738e-01 -3.2744566e-01]
Sparsity at: 0.0
Epoch 481/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4052e-04 - accuracy: 0.9999 - val_loss: 0.1238 - val_accuracy: 0.9833
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.6056999e-01
 -6.0650009e-01 -3.2275259e-01]
Sparsity at: 0.0
Epoch 482/500
235/235 [==============================] - 3s 13ms/step - loss: 3.8407e-04 - accuracy: 0.9999 - val_loss: 0.1256 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5879365e-01
 -6.0564893e-01 -3.2200885e-01]
Sparsity at: 0.0
Epoch 483/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9926e-04 - accuracy: 0.9999 - val_loss: 0.1266 - val_accuracy: 0.9822
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5497359e-01
 -6.0548425e-01 -3.2120198e-01]
Sparsity at: 0.0
Epoch 484/500
235/235 [==============================] - 3s 13ms/step - loss: 2.8543e-05 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9821
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5703675e-01
 -6.0277194e-01 -3.2363817e-01]
Sparsity at: 0.0
Epoch 485/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3913e-05 - accuracy: 1.0000 - val_loss: 0.1224 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5733453e-01
 -6.0269451e-01 -3.2379088e-01]
Sparsity at: 0.0
Epoch 486/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6388e-04 - accuracy: 0.9999 - val_loss: 0.1261 - val_accuracy: 0.9829
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5811005e-01
 -6.0211420e-01 -3.2391828e-01]
Sparsity at: 0.0
Epoch 487/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1107e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4751836e-01
 -6.0466290e-01 -3.2242948e-01]
Sparsity at: 0.0
Epoch 488/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3391e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9834
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4699318e-01
 -6.0472918e-01 -3.2351807e-01]
Sparsity at: 0.0
Epoch 489/500
235/235 [==============================] - 3s 13ms/step - loss: 9.2000e-06 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4763680e-01
 -6.0612792e-01 -3.2381690e-01]
Sparsity at: 0.0
Epoch 490/500
235/235 [==============================] - 3s 13ms/step - loss: 6.8251e-06 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9831
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4891263e-01
 -6.0607654e-01 -3.2493290e-01]
Sparsity at: 0.0
Epoch 491/500
235/235 [==============================] - 3s 13ms/step - loss: 8.4724e-06 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9829
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5007588e-01
 -6.0527229e-01 -3.2631123e-01]
Sparsity at: 0.0
Epoch 492/500
235/235 [==============================] - 3s 13ms/step - loss: 7.5828e-06 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5083011e-01
 -6.0621345e-01 -3.2674077e-01]
Sparsity at: 0.0
Epoch 493/500
235/235 [==============================] - 3s 13ms/step - loss: 7.1515e-06 - accuracy: 1.0000 - val_loss: 0.1250 - val_accuracy: 0.9830
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.4888313e-01
 -6.0576189e-01 -3.2200968e-01]
Sparsity at: 0.0
Epoch 494/500
235/235 [==============================] - 3s 13ms/step - loss: 8.7922e-06 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9832
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5318819e-01
 -6.1297250e-01 -3.2618743e-01]
Sparsity at: 0.0
Epoch 495/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1993e-05 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9840
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5668854e-01
 -6.0483903e-01 -3.2665747e-01]
Sparsity at: 0.0
Epoch 496/500
235/235 [==============================] - 3s 13ms/step - loss: 6.8975e-05 - accuracy: 1.0000 - val_loss: 0.1358 - val_accuracy: 0.9827
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.5705552e-01
 -5.9470004e-01 -3.2464722e-01]
Sparsity at: 0.0
Epoch 497/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 0.1578 - val_accuracy: 0.9793
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.7798253e-01
 -5.7939446e-01 -3.6559799e-01]
Sparsity at: 0.0
Epoch 498/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.1281 - val_accuracy: 0.9823
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.9288447e-01
 -5.7333845e-01 -3.9446467e-01]
Sparsity at: 0.0
Epoch 499/500
235/235 [==============================] - 3s 13ms/step - loss: 5.9743e-04 - accuracy: 0.9998 - val_loss: 0.1325 - val_accuracy: 0.9838
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.8001994e-01
 -5.6135154e-01 -3.9496699e-01]
Sparsity at: 0.0
Epoch 500/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5556e-04 - accuracy: 0.9999 - val_loss: 0.1317 - val_accuracy: 0.9837
[-5.4509416e-02  1.0096259e-02 -5.4582953e-04 ...  7.7146870e-01
 -5.7559741e-01 -3.9863393e-01]
Sparsity at: 0.0
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.042017221450805664
Thresholhold -0.06162944808602333
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.08907948434352875
Thresholhold -0.10798515379428864
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10679344832897186
Thresholhold -0.06120911240577698
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 59:17 - loss: 4.5710 - accuracy: 0.1719WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0051s vs `on_train_batch_begin` time: 2.4617s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 1.5551 - accuracy: 0.8563 - val_loss: 0.9205 - val_accuracy: 0.9020
[-1.3205649e-06 -4.8343789e-08 -2.5207021e-09 ... -1.7735681e-01
 -4.2541802e-02  5.5344618e-04]
Sparsity at: 0.0
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8710 - accuracy: 0.8971 - val_loss: 0.8248 - val_accuracy: 0.9012
[ 5.61612735e-12  2.32025635e-13 -1.00695954e-14 ... -1.62530556e-01
 -1.96039286e-02  2.24268693e-03]
Sparsity at: 0.0
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8323 - accuracy: 0.8974 - val_loss: 0.8108 - val_accuracy: 0.9007
[-1.2643050e-18 -1.0408753e-18  5.6017149e-20 ... -1.4950198e-01
 -1.4024131e-03 -1.9112439e-03]
Sparsity at: 0.0
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8233 - accuracy: 0.8974 - val_loss: 0.8057 - val_accuracy: 0.8992
[ 1.1974747e-22 -3.0707448e-24  4.4265993e-26 ... -1.3795950e-01
  1.2661943e-02 -5.7758889e-03]
Sparsity at: 0.0
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8181 - accuracy: 0.8974 - val_loss: 0.8024 - val_accuracy: 0.8987
[ 2.9976895e-28  4.4539898e-30 -1.0685576e-30 ... -1.2915209e-01
  2.4032211e-02 -7.7792057e-03]
Sparsity at: 0.0
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8151 - accuracy: 0.8978 - val_loss: 0.7991 - val_accuracy: 0.8992
[ 5.9517933e-34  2.3500391e-34  2.2159964e-32 ... -1.2216675e-01
  3.4022849e-02 -8.5950708e-03]
Sparsity at: 0.0
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8131 - accuracy: 0.8979 - val_loss: 0.7969 - val_accuracy: 0.8987
[ 2.1660260e-34  2.3500391e-34 -3.6349234e-30 ... -1.1706496e-01
  4.2687733e-02 -8.4911967e-03]
Sparsity at: 0.0
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8113 - accuracy: 0.8981 - val_loss: 0.7962 - val_accuracy: 0.8994
[ 2.1660260e-34  2.3500391e-34 -1.3978995e-06 ... -1.1328870e-01
  5.0311845e-02 -7.3674787e-03]
Sparsity at: 0.0
Epoch 9/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8102 - accuracy: 0.8981 - val_loss: 0.7958 - val_accuracy: 0.8985
[ 2.1660260e-34  2.3500391e-34 -4.3361278e-12 ... -1.1087439e-01
  5.8021829e-02 -6.4131520e-03]
Sparsity at: 0.0
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8091 - accuracy: 0.8987 - val_loss: 0.7951 - val_accuracy: 0.8988
[ 2.1660260e-34  2.3500391e-34  2.6003137e-17 ... -1.0863491e-01
  6.4445101e-02 -5.2099591e-03]
Sparsity at: 0.0
Epoch 11/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8084 - accuracy: 0.8983 - val_loss: 0.7935 - val_accuracy: 0.8990
[ 2.1660260e-34  2.3500391e-34 -2.3381148e-22 ... -1.0696783e-01
  7.0514143e-02 -3.9761793e-03]
Sparsity at: 0.0
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8080 - accuracy: 0.8987 - val_loss: 0.7934 - val_accuracy: 0.8996
[ 2.16602604e-34  2.35003912e-34 -5.53556129e-06 ... -1.06254585e-01
  7.65981227e-02 -2.72436673e-03]
Sparsity at: 0.0
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8075 - accuracy: 0.8987 - val_loss: 0.7917 - val_accuracy: 0.9005
[ 2.1660260e-34  2.3500391e-34 -9.4719413e-11 ... -1.0558700e-01
  8.1611246e-02 -1.6018897e-03]
Sparsity at: 0.0
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8070 - accuracy: 0.8991 - val_loss: 0.7912 - val_accuracy: 0.9007
[ 2.16602604e-34  2.35003912e-34  5.26896051e-16 ... -1.05248705e-01
  8.64722282e-02 -7.24337180e-04]
Sparsity at: 0.0
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8067 - accuracy: 0.8993 - val_loss: 0.7918 - val_accuracy: 0.9007
[ 2.16602604e-34  2.35003912e-34 -1.47129431e-09 ... -1.04935884e-01
  9.06326100e-02  2.02169584e-04]
Sparsity at: 0.0
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8065 - accuracy: 0.8989 - val_loss: 0.7913 - val_accuracy: 0.9007
[ 2.1660260e-34  2.3500391e-34 -2.3916074e-09 ... -1.0455627e-01
  9.4427384e-02  1.0654160e-03]
Sparsity at: 0.0
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8062 - accuracy: 0.8992 - val_loss: 0.7908 - val_accuracy: 0.9006
[ 2.1660260e-34  2.3500391e-34  1.5097068e-14 ... -1.0460574e-01
  9.7965449e-02  1.5696820e-03]
Sparsity at: 0.0
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8057 - accuracy: 0.8995 - val_loss: 0.7910 - val_accuracy: 0.9004
[ 2.16602604e-34  2.35003912e-34  3.80127688e-07 ... -1.04720630e-01
  1.01084016e-01  2.52033910e-03]
Sparsity at: 0.0
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8059 - accuracy: 0.8994 - val_loss: 0.7901 - val_accuracy: 0.9018
[ 2.1660260e-34  2.3500391e-34  1.6393388e-09 ... -1.0455029e-01
  1.0347389e-01  3.0736746e-03]
Sparsity at: 0.0
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8055 - accuracy: 0.8996 - val_loss: 0.7902 - val_accuracy: 0.9011
[ 2.1660260e-34  2.3500391e-34  1.3422409e-14 ... -1.0462484e-01
  1.0621593e-01  3.5031275e-03]
Sparsity at: 0.0
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8059 - accuracy: 0.8997 - val_loss: 0.7890 - val_accuracy: 0.9024
[ 2.16602604e-34  2.35003912e-34 -1.07750475e-05 ... -1.04435094e-01
  1.07824951e-01  4.10581799e-03]
Sparsity at: 0.0
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8054 - accuracy: 0.8997 - val_loss: 0.7892 - val_accuracy: 0.9021
[ 2.1660260e-34  2.3500391e-34  5.6322599e-11 ... -1.0444410e-01
  1.0976944e-01  4.6280744e-03]
Sparsity at: 0.0
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.9000 - val_loss: 0.7899 - val_accuracy: 0.9014
[ 2.16602604e-34  2.35003912e-34 -1.98538255e-14 ... -1.04480505e-01
  1.11393012e-01  5.04942006e-03]
Sparsity at: 0.0
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.8999 - val_loss: 0.7898 - val_accuracy: 0.9015
[ 2.1660260e-34  2.3500391e-34 -8.5523766e-10 ... -1.0432904e-01
  1.1281988e-01  5.4170885e-03]
Sparsity at: 0.0
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9002 - val_loss: 0.7894 - val_accuracy: 0.9021
[ 2.16602604e-34  2.35003912e-34 -3.79743979e-13 ... -1.04096480e-01
  1.13918714e-01  5.92254661e-03]
Sparsity at: 0.0
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9000 - val_loss: 0.7891 - val_accuracy: 0.9024
[ 2.1660260e-34  2.3500391e-34  1.3922614e-05 ... -1.0404226e-01
  1.1462061e-01  6.5571698e-03]
Sparsity at: 0.0
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8049 - accuracy: 0.9002 - val_loss: 0.7894 - val_accuracy: 0.9023
[ 2.1660260e-34  2.3500391e-34  5.8171030e-11 ... -1.0361782e-01
  1.1562388e-01  6.1145765e-03]
Sparsity at: 0.0
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.9000 - val_loss: 0.7893 - val_accuracy: 0.9014
[ 2.16602604e-34  2.35003912e-34  7.79296201e-08 ... -1.03679374e-01
  1.16307065e-01  6.63901167e-03]
Sparsity at: 0.0
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9003 - val_loss: 0.7890 - val_accuracy: 0.9019
[ 2.1660260e-34  2.3500391e-34  9.4815200e-10 ... -1.0326217e-01
  1.1728546e-01  6.8496661e-03]
Sparsity at: 0.0
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9005 - val_loss: 0.7891 - val_accuracy: 0.9024
[ 2.1660260e-34  2.3500391e-34 -4.0738880e-13 ... -1.0345131e-01
  1.1790537e-01  7.0318724e-03]
Sparsity at: 0.0
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9002 - val_loss: 0.7890 - val_accuracy: 0.9026
[ 2.16602604e-34  2.35003912e-34 -4.27853948e-08 ... -1.02870174e-01
  1.18426777e-01  6.89500896e-03]
Sparsity at: 0.0
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9007 - val_loss: 0.7895 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34 -4.1693056e-14 ... -1.0257340e-01
  1.1884518e-01  7.1710302e-03]
Sparsity at: 0.0
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9007 - val_loss: 0.7881 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -1.6677791e-07 ... -1.0261066e-01
  1.1929432e-01  7.5754649e-03]
Sparsity at: 0.0
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9006 - val_loss: 0.7889 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34  2.6858646e-12 ... -1.0215940e-01
  1.1980158e-01  7.4140839e-03]
Sparsity at: 0.0
Epoch 35/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7895 - val_accuracy: 0.9023
[ 2.1660260e-34  2.3500391e-34 -4.1741819e-06 ... -1.0198027e-01
  1.2032953e-01  7.9710959e-03]
Sparsity at: 0.0
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9007 - val_loss: 0.7885 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34 -1.24806120e-11 ... -1.01966016e-01
  1.20785087e-01  8.18054006e-03]
Sparsity at: 0.0
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -2.73279293e-05 ... -1.01689473e-01
  1.20898284e-01  8.15034006e-03]
Sparsity at: 0.0
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34  1.38273476e-10 ... -1.01651676e-01
  1.21659353e-01  8.42591748e-03]
Sparsity at: 0.0
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9006 - val_loss: 0.7891 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34 -1.0361981e-04 ... -1.0180908e-01
  1.2199034e-01  8.3231432e-03]
Sparsity at: 0.0
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9009 - val_loss: 0.7887 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -9.4042518e-10 ... -1.0154715e-01
  1.2213847e-01  8.4032761e-03]
Sparsity at: 0.0
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7884 - val_accuracy: 0.9027
[ 2.16602604e-34  2.35003912e-34 -8.18828738e-09 ... -1.01530753e-01
  1.22399464e-01  8.43476132e-03]
Sparsity at: 0.0
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9008 - val_loss: 0.7883 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -4.94157959e-09 ... -1.01429515e-01
  1.22700371e-01  8.54835752e-03]
Sparsity at: 0.0
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34 -3.76863714e-11 ... -1.00892186e-01
  1.22679248e-01  8.47051200e-03]
Sparsity at: 0.0
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.9007 - val_loss: 0.7892 - val_accuracy: 0.9024
[ 2.1660260e-34  2.3500391e-34  5.2716231e-09 ... -1.0106509e-01
  1.2278392e-01  8.7984437e-03]
Sparsity at: 0.0
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7879 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  1.7454630e-12 ... -1.0067753e-01
  1.2328408e-01  8.5900435e-03]
Sparsity at: 0.0
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9031
[ 2.16602604e-34  2.35003912e-34  2.45417606e-08 ... -1.00501135e-01
  1.23044893e-01  8.60073604e-03]
Sparsity at: 0.0
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7881 - val_accuracy: 0.9036
[ 2.16602604e-34  2.35003912e-34 -4.93951993e-13 ... -1.00759186e-01
  1.23120397e-01  8.68034177e-03]
Sparsity at: 0.0
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34 -9.5595162e-08 ... -1.0045973e-01
  1.2335862e-01  8.5055120e-03]
Sparsity at: 0.0
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -8.7486192e-13 ... -1.0047456e-01
  1.2372674e-01  8.6227581e-03]
Sparsity at: 0.0
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7883 - val_accuracy: 0.9040
[ 2.16602604e-34  2.35003912e-34  2.84388648e-07 ... -1.00322999e-01
  1.23401135e-01  8.64373054e-03]
Sparsity at: 0.0
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.00915514860354194
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.036097921937487065
Thresholhold -0.06856929510831833
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.11574094451669836
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 46s 7ms/step - loss: 0.8040 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  1.6453102e-12 ... -1.0000724e-01
  1.2363015e-01  8.6111184e-03]
Sparsity at: 0.0
Epoch 52/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7886 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -7.1637118e-07 ... -1.0027568e-01
  1.2385195e-01  8.5295215e-03]
Sparsity at: 0.0
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34  7.3845825e-12 ... -9.9964030e-02
  1.2353751e-01  8.2884375e-03]
Sparsity at: 0.0
Epoch 54/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9021
[ 2.1660260e-34  2.3500391e-34  8.8664683e-07 ... -9.9746153e-02
  1.2373705e-01  8.2060015e-03]
Sparsity at: 0.0
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.9008 - val_loss: 0.7883 - val_accuracy: 0.9031
[ 2.16602604e-34  2.35003912e-34 -2.80812179e-11 ... -9.94590595e-02
  1.23957165e-01  8.07343237e-03]
Sparsity at: 0.0
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9028
[ 2.16602604e-34  2.35003912e-34 -4.34666617e-06 ... -9.95887965e-02
  1.23954244e-01  8.13579746e-03]
Sparsity at: 0.0
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7887 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  1.1211625e-10 ... -9.9212125e-02
  1.2372702e-01  7.6875449e-03]
Sparsity at: 0.0
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9044
[ 2.1660260e-34  2.3500391e-34 -1.0305887e-06 ... -9.9204823e-02
  1.2393509e-01  7.5690080e-03]
Sparsity at: 0.0
Epoch 59/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9029
[ 2.16602604e-34  2.35003912e-34  1.19795884e-09 ... -9.92273390e-02
  1.23632945e-01  7.61712156e-03]
Sparsity at: 0.0
Epoch 60/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -4.6252936e-12 ... -9.8711058e-02
  1.2364761e-01  7.5401450e-03]
Sparsity at: 0.0
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  1.8374916e-08 ... -9.8907493e-02
  1.2350740e-01  7.3776031e-03]
Sparsity at: 0.0
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7885 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  3.0171525e-13 ... -9.8878734e-02
  1.2382139e-01  7.5573400e-03]
Sparsity at: 0.0
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7888 - val_accuracy: 0.9021
[ 2.16602604e-34  2.35003912e-34 -1.74478032e-07 ... -9.86311659e-02
  1.23427935e-01  7.68833980e-03]
Sparsity at: 0.0
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  4.9983780e-13 ... -9.8617427e-02
  1.2338685e-01  7.3522748e-03]
Sparsity at: 0.0
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -6.6352311e-07 ... -9.8587722e-02
  1.2323004e-01  7.2905472e-03]
Sparsity at: 0.0
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7889 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  6.0581462e-12 ... -9.8664761e-02
  1.2345547e-01  7.4921534e-03]
Sparsity at: 0.0
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9009 - val_loss: 0.7889 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -4.9139799e-06 ... -9.8300889e-02
  1.2318038e-01  7.1581635e-03]
Sparsity at: 0.0
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.9005 - val_loss: 0.7882 - val_accuracy: 0.9042
[ 2.1660260e-34  2.3500391e-34 -7.4312882e-11 ... -9.7884536e-02
  1.2294847e-01  7.0410557e-03]
Sparsity at: 0.0
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  7.9210302e-05 ... -9.8067157e-02
  1.2327841e-01  6.9400631e-03]
Sparsity at: 0.0
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7873 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  6.9163408e-10 ... -9.7821139e-02
  1.2315126e-01  6.6557755e-03]
Sparsity at: 0.0
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7886 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34  5.3430538e-10 ... -9.7845003e-02
  1.2295124e-01  7.1684401e-03]
Sparsity at: 0.0
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -7.3171340e-09 ... -9.8003305e-02
  1.2300588e-01  7.2030090e-03]
Sparsity at: 0.0
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -6.5043684e-13 ... -9.8043591e-02
  1.2297179e-01  6.9011776e-03]
Sparsity at: 0.0
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -3.72407172e-08 ... -9.79931429e-02
  1.22921996e-01  7.26905884e-03]
Sparsity at: 0.0
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -4.5371825e-14 ... -9.7844921e-02
  1.2295132e-01  7.2539728e-03]
Sparsity at: 0.0
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7880 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  2.5068209e-07 ... -9.7756229e-02
  1.2298052e-01  7.0465095e-03]
Sparsity at: 0.0
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7886 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -1.2570969e-12 ... -9.8069221e-02
  1.2293230e-01  6.9023226e-03]
Sparsity at: 0.0
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34 -6.5326759e-07 ... -9.7691648e-02
  1.2275473e-01  7.0728757e-03]
Sparsity at: 0.0
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7882 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -1.5054334e-12 ... -9.7587541e-02
  1.2255965e-01  6.5375259e-03]
Sparsity at: 0.0
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  6.6796798e-05 ... -9.7386755e-02
  1.2223854e-01  6.8525658e-03]
Sparsity at: 0.0
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9024
[ 2.1660260e-34  2.3500391e-34 -2.5403077e-10 ... -9.7440563e-02
  1.2239065e-01  6.8055037e-03]
Sparsity at: 0.0
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9005 - val_loss: 0.7873 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34 -6.22334895e-09 ... -9.71255153e-02
  1.21845916e-01  6.54944032e-03]
Sparsity at: 0.0
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7892 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34  5.0010618e-09 ... -9.7704850e-02
  1.2256291e-01  6.4622951e-03]
Sparsity at: 0.0
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -1.1566091e-12 ... -9.7668409e-02
  1.2254113e-01  6.2182229e-03]
Sparsity at: 0.0
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34  4.11459240e-08 ... -9.71793234e-02
  1.22211255e-01  6.36436744e-03]
Sparsity at: 0.0
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7892 - val_accuracy: 0.9023
[ 2.1660260e-34  2.3500391e-34 -2.3269505e-13 ... -9.7313061e-02
  1.2256114e-01  6.2853787e-03]
Sparsity at: 0.0
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7888 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -9.8411135e-08 ... -9.7215168e-02
  1.2226288e-01  6.3559138e-03]
Sparsity at: 0.0
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7886 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -1.4530465e-12 ... -9.7305700e-02
  1.2194739e-01  6.3968627e-03]
Sparsity at: 0.0
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7876 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -1.8861141e-07 ... -9.7074747e-02
  1.2176514e-01  6.0217646e-03]
Sparsity at: 0.0
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -2.5579309e-12 ... -9.7093269e-02
  1.2192043e-01  6.1159977e-03]
Sparsity at: 0.0
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7883 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  1.0252292e-05 ... -9.7039454e-02
  1.2164108e-01  5.8161723e-03]
Sparsity at: 0.0
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  4.7190005e-11 ... -9.7213544e-02
  1.2182612e-01  5.9348890e-03]
Sparsity at: 0.0
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7879 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  1.4972247e-04 ... -9.7126305e-02
  1.2150061e-01  5.8374153e-03]
Sparsity at: 0.0
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7879 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -1.2245449e-10 ... -9.7019352e-02
  1.2190378e-01  5.5013690e-03]
Sparsity at: 0.0
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7886 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  6.4689498e-10 ... -9.7127266e-02
  1.2179118e-01  5.7266690e-03]
Sparsity at: 0.0
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7880 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34  4.52062254e-09 ... -9.68534946e-02
  1.21476084e-01  5.42884693e-03]
Sparsity at: 0.0
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34 -3.0263845e-13 ... -9.7083762e-02
  1.2131371e-01  5.4557747e-03]
Sparsity at: 0.0
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -6.0808212e-08 ... -9.7220466e-02
  1.2147357e-01  5.5546863e-03]
Sparsity at: 0.0
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34  4.01593147e-13 ... -9.68614668e-02
  1.21158734e-01  5.36596077e-03]
Sparsity at: 0.0
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7884 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34  4.9359312e-07 ... -9.7060896e-02
  1.2133976e-01  5.1674028e-03]
Sparsity at: 0.0
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.014708364099192517
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.04548157419801857
Thresholhold -0.0645592212677002
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.1286502590551155
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 53s 7ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9027
[ 2.16602604e-34  2.35003912e-34  2.79422731e-12 ... -9.71402302e-02
  1.20879635e-01  4.90643457e-03]
Sparsity at: 0.0
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9025
[ 2.16602604e-34  2.35003912e-34 -3.03700449e-06 ... -9.72235948e-02
  1.21013544e-01  4.88534849e-03]
Sparsity at: 0.0
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -6.9283364e-12 ... -9.7325288e-02
  1.2118961e-01  4.4934880e-03]
Sparsity at: 0.0
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -1.5197233e-05 ... -9.7079918e-02
  1.2105858e-01  4.5556165e-03]
Sparsity at: 0.0
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  7.3672374e-11 ... -9.7418077e-02
  1.2105993e-01  4.7238539e-03]
Sparsity at: 0.0
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  3.1915839e-05 ... -9.7155385e-02
  1.2078542e-01  4.4937059e-03]
Sparsity at: 0.0
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7890 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  2.8996230e-10 ... -9.7243927e-02
  1.2086421e-01  4.6679997e-03]
Sparsity at: 0.0
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7879 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -1.8639948e-05 ... -9.7475395e-02
  1.2105953e-01  4.2736516e-03]
Sparsity at: 0.0
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7871 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -6.5653027e-10 ... -9.7421840e-02
  1.2049760e-01  4.0403479e-03]
Sparsity at: 0.0
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7870 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -1.6173111e-11 ... -9.7342648e-02
  1.2074792e-01  4.0978370e-03]
Sparsity at: 0.0
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7870 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -1.1586430e-08 ... -9.6977845e-02
  1.2075350e-01  3.3640112e-03]
Sparsity at: 0.0
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7889 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -2.2562814e-13 ... -9.7149476e-02
  1.2063864e-01  3.4207613e-03]
Sparsity at: 0.0
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -8.4847329e-08 ... -9.7358614e-02
  1.2024760e-01  3.9689345e-03]
Sparsity at: 0.0
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -1.1080773e-12 ... -9.7101718e-02
  1.2062112e-01  3.8814293e-03]
Sparsity at: 0.0
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7883 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34  8.4616335e-07 ... -9.7484112e-02
  1.2063508e-01  3.9754892e-03]
Sparsity at: 0.0
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7893 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  4.7346424e-12 ... -9.7356521e-02
  1.2043872e-01  3.8454600e-03]
Sparsity at: 0.0
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7887 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  1.0483108e-05 ... -9.7376145e-02
  1.2058238e-01  3.5123341e-03]
Sparsity at: 0.0
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7885 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -4.0463074e-11 ... -9.7385406e-02
  1.2084827e-01  3.2860136e-03]
Sparsity at: 0.0
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  2.1756416e-05 ... -9.7490899e-02
  1.2088891e-01  3.6296572e-03]
Sparsity at: 0.0
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  1.3137491e-09 ... -9.7259887e-02
  1.2064642e-01  3.4481718e-03]
Sparsity at: 0.0
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -4.7470278e-11 ... -9.7076550e-02
  1.2051379e-01  3.3611022e-03]
Sparsity at: 0.0
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  1.4670893e-08 ... -9.6782044e-02
  1.2027787e-01  2.8280038e-03]
Sparsity at: 0.0
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9028
[ 2.16602604e-34  2.35003912e-34  1.51341396e-13 ... -9.70256329e-02
  1.20238714e-01  3.01626162e-03]
Sparsity at: 0.0
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7883 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -8.9415053e-08 ... -9.7184233e-02
  1.2040429e-01  3.0033137e-03]
Sparsity at: 0.0
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34 -8.32622816e-14 ... -9.73508954e-02
  1.20344274e-01  3.12919798e-03]
Sparsity at: 0.0
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7885 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  6.4694416e-07 ... -9.7192332e-02
  1.2018148e-01  2.7840296e-03]
Sparsity at: 0.0
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -7.3657261e-12 ... -9.7190410e-02
  1.1988987e-01  2.7156852e-03]
Sparsity at: 0.0
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34  9.98311589e-06 ... -9.68742520e-02
  1.19972415e-01  2.79180845e-03]
Sparsity at: 0.0
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34 -9.30401589e-13 ... -9.67142284e-02
  1.19725816e-01  2.98653310e-03]
Sparsity at: 0.0
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  1.5978871e-06 ... -9.6988514e-02
  1.1991366e-01  3.0524095e-03]
Sparsity at: 0.0
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  6.7893874e-10 ... -9.7167835e-02
  1.1985377e-01  3.0049200e-03]
Sparsity at: 0.0
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  1.0064081e-12 ... -9.6865274e-02
  1.1976625e-01  2.8798364e-03]
Sparsity at: 0.0
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9024
[ 2.16602604e-34  2.35003912e-34 -3.97779232e-09 ... -9.69731957e-02
  1.19776495e-01  2.80328165e-03]
Sparsity at: 0.0
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7886 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  2.7068240e-13 ... -9.7379789e-02
  1.2036324e-01  2.9289066e-03]
Sparsity at: 0.0
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -4.8864769e-07 ... -9.6915834e-02
  1.1986538e-01  2.8026041e-03]
Sparsity at: 0.0
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7880 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  2.9037278e-12 ... -9.6954107e-02
  1.1982196e-01  2.8092824e-03]
Sparsity at: 0.0
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7886 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -1.2274721e-05 ... -9.6897244e-02
  1.1952463e-01  2.9610861e-03]
Sparsity at: 0.0
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34  8.25594523e-11 ... -9.71164778e-02
  1.19513415e-01  2.96823401e-03]
Sparsity at: 0.0
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7887 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  5.5957571e-06 ... -9.7000144e-02
  1.1974403e-01  2.4567340e-03]
Sparsity at: 0.0
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7883 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -8.6247898e-10 ... -9.6962549e-02
  1.1942009e-01  2.7084923e-03]
Sparsity at: 0.0
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34 -3.03921888e-11 ... -9.67779681e-02
  1.19541444e-01  2.69276882e-03]
Sparsity at: 0.0
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -9.2552490e-09 ... -9.6977256e-02
  1.1937625e-01  2.7654774e-03]
Sparsity at: 0.0
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34 -2.0934207e-13 ... -9.6662499e-02
  1.1892152e-01  2.8035061e-03]
Sparsity at: 0.0
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7884 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -1.8825972e-09 ... -9.6725717e-02
  1.1925315e-01  2.9784557e-03]
Sparsity at: 0.0
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7885 - val_accuracy: 0.9024
[ 2.1660260e-34  2.3500391e-34 -6.2213906e-13 ... -9.6685015e-02
  1.1931898e-01  2.7437985e-03]
Sparsity at: 0.0
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7890 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -8.6099720e-07 ... -9.6654192e-02
  1.1942066e-01  2.5409604e-03]
Sparsity at: 0.0
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -4.9153685e-12 ... -9.6880190e-02
  1.1949421e-01  2.7386195e-03]
Sparsity at: 0.0
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7890 - val_accuracy: 0.9020
[ 2.1660260e-34  2.3500391e-34 -3.8123526e-06 ... -9.6700191e-02
  1.1912562e-01  2.4994416e-03]
Sparsity at: 0.0
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7876 - val_accuracy: 0.9036
[ 2.16602604e-34  2.35003912e-34 -4.09335621e-11 ... -9.65089798e-02
  1.19078375e-01  2.55264482e-03]
Sparsity at: 0.0
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9026
[ 2.16602604e-34  2.35003912e-34 -5.23788913e-05 ... -9.64474380e-02
  1.19286716e-01  2.45680753e-03]
Sparsity at: 0.0
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.020803199581568288
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.05591662801998787
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.1408818395884932
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 49s 7ms/step - loss: 0.8033 - accuracy: 0.9018 - val_loss: 0.7881 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34  3.6619929e-11 ... -9.6580260e-02
  1.1910656e-01  2.6568747e-03]
Sparsity at: 0.0
Epoch 152/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34 -2.2391673e-06 ... -9.6932195e-02
  1.1919407e-01  2.7150910e-03]
Sparsity at: 0.0
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -1.7990511e-09 ... -9.6603841e-02
  1.1926375e-01  2.8114533e-03]
Sparsity at: 0.0
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7876 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -3.6222533e-10 ... -9.6340299e-02
  1.1917290e-01  2.8354160e-03]
Sparsity at: 0.0
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7884 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -5.8077898e-09 ... -9.6539445e-02
  1.1905880e-01  2.8775437e-03]
Sparsity at: 0.0
Epoch 156/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -2.6255072e-12 ... -9.6509233e-02
  1.1883052e-01  2.7396532e-03]
Sparsity at: 0.0
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7885 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  3.4824858e-08 ... -9.6605420e-02
  1.1912230e-01  2.9710745e-03]
Sparsity at: 0.0
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -5.6038683e-16 ... -9.6622750e-02
  1.1941165e-01  2.8413485e-03]
Sparsity at: 0.0
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7890 - val_accuracy: 0.9029
[ 2.16602604e-34  2.35003912e-34  1.40526708e-07 ... -9.66509134e-02
  1.19145416e-01  2.59761140e-03]
Sparsity at: 0.0
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -1.0402730e-12 ... -9.6590072e-02
  1.1878300e-01  2.6390764e-03]
Sparsity at: 0.0
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7878 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -9.4023585e-07 ... -9.6608140e-02
  1.1920593e-01  2.7647687e-03]
Sparsity at: 0.0
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  6.7455584e-13 ... -9.6854463e-02
  1.1936492e-01  2.7359154e-03]
Sparsity at: 0.0
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34  2.7540141e-06 ... -9.6537173e-02
  1.1872142e-01  2.6772402e-03]
Sparsity at: 0.0
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7888 - val_accuracy: 0.9025
[ 2.1660260e-34  2.3500391e-34 -1.3675547e-11 ... -9.6797854e-02
  1.1905740e-01  2.6128897e-03]
Sparsity at: 0.0
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34 -1.3585609e-05 ... -9.6634559e-02
  1.1906743e-01  2.8046893e-03]
Sparsity at: 0.0
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -3.5801785e-11 ... -9.6938528e-02
  1.1915258e-01  2.7632308e-03]
Sparsity at: 0.0
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9029
[ 2.16602604e-34  2.35003912e-34  1.68348910e-04 ... -9.66620147e-02
  1.18900634e-01  2.85071414e-03]
Sparsity at: 0.0
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -1.3919457e-10 ... -9.6317515e-02
  1.1889163e-01  2.7481902e-03]
Sparsity at: 0.0
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9036
[ 2.16602604e-34  2.35003912e-34 -3.14312576e-09 ... -9.65864137e-02
  1.19008616e-01  2.51066568e-03]
Sparsity at: 0.0
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34 -1.47188794e-09 ... -9.65026543e-02
  1.18838936e-01  2.48828111e-03]
Sparsity at: 0.0
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  7.3569709e-12 ... -9.6462451e-02
  1.1888755e-01  2.3645042e-03]
Sparsity at: 0.0
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7884 - val_accuracy: 0.9029
[ 2.16602604e-34  2.35003912e-34  2.55236881e-08 ... -9.65768918e-02
  1.19034834e-01  2.88954074e-03]
Sparsity at: 0.0
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  6.3266059e-13 ... -9.6415073e-02
  1.1883083e-01  2.8056968e-03]
Sparsity at: 0.0
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -3.1154045e-08 ... -9.6525386e-02
  1.1915801e-01  3.1197423e-03]
Sparsity at: 0.0
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  1.5149950e-14 ... -9.6221969e-02
  1.1855579e-01  2.6419265e-03]
Sparsity at: 0.0
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34 -1.31637080e-07 ... -9.64419469e-02
  1.18899465e-01  2.68231006e-03]
Sparsity at: 0.0
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7887 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34  1.60267112e-12 ... -9.63397697e-02
  1.18625335e-01  2.96521722e-03]
Sparsity at: 0.0
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  3.5516737e-07 ... -9.6298061e-02
  1.1903628e-01  2.8488128e-03]
Sparsity at: 0.0
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -2.6913020e-13 ... -9.6474864e-02
  1.1915302e-01  2.7468621e-03]
Sparsity at: 0.0
Epoch 180/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7887 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34 -1.9917941e-07 ... -9.6267074e-02
  1.1880699e-01  2.6461885e-03]
Sparsity at: 0.0
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  3.7669746e-12 ... -9.6356757e-02
  1.1881157e-01  3.0019286e-03]
Sparsity at: 0.0
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7880 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -5.9884201e-07 ... -9.6398540e-02
  1.1877456e-01  2.7909693e-03]
Sparsity at: 0.0
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -2.1847019e-12 ... -9.6387528e-02
  1.1889373e-01  2.9383923e-03]
Sparsity at: 0.0
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  5.3655244e-06 ... -9.6158706e-02
  1.1894097e-01  3.0706285e-03]
Sparsity at: 0.0
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34  3.44929710e-11 ... -9.63250399e-02
  1.19054615e-01  3.05892900e-03]
Sparsity at: 0.0
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34  5.0792511e-05 ... -9.6353278e-02
  1.1879031e-01  2.9770581e-03]
Sparsity at: 0.0
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -2.3545893e-10 ... -9.6241102e-02
  1.1863084e-01  2.8560949e-03]
Sparsity at: 0.0
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -1.1394823e-05 ... -9.6046142e-02
  1.1877998e-01  2.7560133e-03]
Sparsity at: 0.0
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7883 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34  1.72704517e-10 ... -9.62879434e-02
  1.18974574e-01  3.02266539e-03]
Sparsity at: 0.0
Epoch 190/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34 -5.4520000e-11 ... -9.6491553e-02
  1.1929038e-01  3.3337504e-03]
Sparsity at: 0.0
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9039
[ 2.16602604e-34  2.35003912e-34 -3.63323238e-09 ... -9.64286700e-02
  1.19018145e-01  3.30658117e-03]
Sparsity at: 0.0
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -6.4167814e-14 ... -9.6307836e-02
  1.1880210e-01  3.0820197e-03]
Sparsity at: 0.0
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7887 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34  4.7288302e-07 ... -9.6134439e-02
  1.1928761e-01  3.0975449e-03]
Sparsity at: 0.0
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34 -2.84533681e-12 ... -9.60668996e-02
  1.18882336e-01  2.92986888e-03]
Sparsity at: 0.0
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  8.4072490e-06 ... -9.6113347e-02
  1.1886991e-01  3.0404455e-03]
Sparsity at: 0.0
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -2.6811109e-10 ... -9.6393518e-02
  1.1909443e-01  3.1580201e-03]
Sparsity at: 0.0
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9027
[ 2.16602604e-34  2.35003912e-34 -6.78626183e-11 ... -9.60306600e-02
  1.18720554e-01  3.00747599e-03]
Sparsity at: 0.0
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7883 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  4.8426294e-09 ... -9.6100725e-02
  1.1879939e-01  3.0489608e-03]
Sparsity at: 0.0
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9020 - val_loss: 0.7881 - val_accuracy: 0.9024
[ 2.16602604e-34  2.35003912e-34  1.41832320e-13 ... -9.61509645e-02
  1.18734434e-01  3.43717332e-03]
Sparsity at: 0.0
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7872 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34 -1.43744515e-07 ... -9.62034985e-02
  1.18793294e-01  3.19583109e-03]
Sparsity at: 0.0
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.027556433328772556
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.0659883460651347
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.15769569784143656
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 50s 7ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  9.3493854e-13 ... -9.6538365e-02
  1.1886690e-01  2.9982189e-03]
Sparsity at: 0.0
Epoch 202/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -1.2972907e-07 ... -9.6250750e-02
  1.1906825e-01  3.1209202e-03]
Sparsity at: 0.0
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  3.6280094e-13 ... -9.6179582e-02
  1.1904890e-01  3.4341931e-03]
Sparsity at: 0.0
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7891 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -1.4574807e-06 ... -9.6239060e-02
  1.1927409e-01  3.3770159e-03]
Sparsity at: 0.0
Epoch 205/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34  6.5037600e-11 ... -9.6329443e-02
  1.1863651e-01  2.8768338e-03]
Sparsity at: 0.0
Epoch 206/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  9.1593480e-05 ... -9.6054897e-02
  1.1879218e-01  3.0702408e-03]
Sparsity at: 0.0
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7882 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -7.6268769e-11 ... -9.6451513e-02
  1.1897182e-01  2.9313217e-03]
Sparsity at: 0.0
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7881 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  8.5456859e-12 ... -9.6411280e-02
  1.1885888e-01  2.9694489e-03]
Sparsity at: 0.0
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7889 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  8.8605248e-09 ... -9.6175134e-02
  1.1901124e-01  3.0101412e-03]
Sparsity at: 0.0
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -1.2308070e-13 ... -9.6559554e-02
  1.1892750e-01  3.0880678e-03]
Sparsity at: 0.0
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34 -4.33963578e-07 ... -9.63640511e-02
  1.19063586e-01  3.06394137e-03]
Sparsity at: 0.0
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -5.1551108e-13 ... -9.6371450e-02
  1.1898365e-01  3.1051950e-03]
Sparsity at: 0.0
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7886 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -7.6709221e-06 ... -9.6671633e-02
  1.1911969e-01  3.1551465e-03]
Sparsity at: 0.0
Epoch 214/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7886 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34 -4.40653555e-11 ... -9.65431333e-02
  1.19192265e-01  3.22962878e-03]
Sparsity at: 0.0
Epoch 215/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  6.0191516e-05 ... -9.6514642e-02
  1.1932000e-01  3.3573692e-03]
Sparsity at: 0.0
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7868 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -3.7454451e-10 ... -9.6780226e-02
  1.1917350e-01  3.2635916e-03]
Sparsity at: 0.0
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7874 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -3.7221553e-09 ... -9.6471243e-02
  1.1910212e-01  3.0926869e-03]
Sparsity at: 0.0
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  5.3804827e-09 ... -9.6443877e-02
  1.1925979e-01  3.3886791e-03]
Sparsity at: 0.0
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -1.9796494e-13 ... -9.6502274e-02
  1.1885010e-01  3.2876329e-03]
Sparsity at: 0.0
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9036
[ 2.16602604e-34  2.35003912e-34  6.24459773e-08 ... -9.65651870e-02
  1.18894674e-01  2.98376079e-03]
Sparsity at: 0.0
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -4.1764853e-14 ... -9.6426047e-02
  1.1891383e-01  3.2049033e-03]
Sparsity at: 0.0
Epoch 222/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -7.8621895e-07 ... -9.6229345e-02
  1.1911226e-01  3.2866602e-03]
Sparsity at: 0.0
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9045
[ 2.1660260e-34  2.3500391e-34  4.8542637e-12 ... -9.6650369e-02
  1.1909971e-01  3.2494243e-03]
Sparsity at: 0.0
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7884 - val_accuracy: 0.9039
[ 2.16602604e-34  2.35003912e-34  4.30070440e-06 ... -9.64922979e-02
  1.18824914e-01  3.60909826e-03]
Sparsity at: 0.0
Epoch 225/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7868 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  9.1109290e-12 ... -9.6560977e-02
  1.1921288e-01  3.5429737e-03]
Sparsity at: 0.0
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7869 - val_accuracy: 0.9036
[ 2.16602604e-34  2.35003912e-34  1.13392452e-05 ... -9.63642821e-02
  1.19124055e-01  3.35759437e-03]
Sparsity at: 0.0
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7873 - val_accuracy: 0.9041
[ 2.16602604e-34  2.35003912e-34 -5.88086108e-11 ... -9.66008604e-02
  1.19056754e-01  3.48671270e-03]
Sparsity at: 0.0
Epoch 228/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7882 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34 -6.7843575e-05 ... -9.6594557e-02
  1.1922316e-01  3.7597457e-03]
Sparsity at: 0.0
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  8.5243326e-11 ... -9.6335448e-02
  1.1927572e-01  3.2777374e-03]
Sparsity at: 0.0
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -5.5014636e-05 ... -9.6648894e-02
  1.1897836e-01  3.4741948e-03]
Sparsity at: 0.0
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7882 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34 -2.1246993e-10 ... -9.6568026e-02
  1.1910061e-01  3.3937739e-03]
Sparsity at: 0.0
Epoch 232/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7867 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  2.4068009e-07 ... -9.6539691e-02
  1.1903459e-01  3.2571326e-03]
Sparsity at: 0.0
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -2.1436353e-09 ... -9.6227027e-02
  1.1937067e-01  3.3909483e-03]
Sparsity at: 0.0
Epoch 234/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9038
[ 2.16602604e-34  2.35003912e-34  7.05914616e-09 ... -9.65277478e-02
  1.18935116e-01  3.35481972e-03]
Sparsity at: 0.0
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9021
[ 2.1660260e-34  2.3500391e-34 -4.4412976e-09 ... -9.6783206e-02
  1.1925901e-01  3.4774737e-03]
Sparsity at: 0.0
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7871 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -5.7329497e-10 ... -9.6331649e-02
  1.1878636e-01  3.4949395e-03]
Sparsity at: 0.0
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7867 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  8.7771461e-09 ... -9.6157141e-02
  1.1880552e-01  3.2888025e-03]
Sparsity at: 0.0
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -4.9343854e-12 ... -9.6540190e-02
  1.1913497e-01  3.3184981e-03]
Sparsity at: 0.0
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7880 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -2.1100488e-08 ... -9.6483000e-02
  1.1908186e-01  3.0491564e-03]
Sparsity at: 0.0
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  3.1995653e-14 ... -9.6581399e-02
  1.1922559e-01  3.4348133e-03]
Sparsity at: 0.0
Epoch 241/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7871 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  3.0316048e-07 ... -9.6461095e-02
  1.1907679e-01  3.3511606e-03]
Sparsity at: 0.0
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7884 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -1.9065847e-12 ... -9.6547812e-02
  1.1914340e-01  3.6021469e-03]
Sparsity at: 0.0
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -1.7671662e-06 ... -9.6647032e-02
  1.1890531e-01  3.8230347e-03]
Sparsity at: 0.0
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7872 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34 -1.1744130e-11 ... -9.6731819e-02
  1.1914517e-01  3.7350510e-03]
Sparsity at: 0.0
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9018 - val_loss: 0.7884 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  4.2391039e-06 ... -9.6824832e-02
  1.1919289e-01  3.8685901e-03]
Sparsity at: 0.0
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34  4.0742507e-11 ... -9.6519634e-02
  1.1903399e-01  3.5170321e-03]
Sparsity at: 0.0
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7886 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34  1.5634807e-05 ... -9.6735716e-02
  1.1924642e-01  3.5634537e-03]
Sparsity at: 0.0
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7870 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -1.7084140e-11 ... -9.6776724e-02
  1.1923430e-01  3.8675345e-03]
Sparsity at: 0.0
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -2.2168499e-07 ... -9.6635878e-02
  1.1918229e-01  3.5754694e-03]
Sparsity at: 0.0
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9015 - val_loss: 0.7867 - val_accuracy: 0.9039
[ 2.16602604e-34  2.35003912e-34 -8.55577165e-10 ... -9.67387557e-02
  1.19049884e-01  3.63536761e-03]
Sparsity at: 0.0
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.035314610557317216
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.0793650176673859
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.17453197713261837
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 49s 7ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7874 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34 -4.3937979e-13 ... -9.6344724e-02
  1.1902300e-01  3.6545179e-03]
Sparsity at: 0.0
Epoch 252/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -2.0693072e-08 ... -9.6620277e-02
  1.1909591e-01  3.7679132e-03]
Sparsity at: 0.0
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9029
[ 2.16602604e-34  2.35003912e-34 -1.59345112e-13 ... -9.63765383e-02
  1.19106606e-01  3.73522611e-03]
Sparsity at: 0.0
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9017 - val_loss: 0.7873 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -3.1098520e-07 ... -9.6692465e-02
  1.1925478e-01  3.7910873e-03]
Sparsity at: 0.0
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7868 - val_accuracy: 0.9037
[ 2.16602604e-34  2.35003912e-34 -2.75451493e-12 ... -9.65031013e-02
  1.19260825e-01  4.02578712e-03]
Sparsity at: 0.0
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -1.1235820e-06 ... -9.6647441e-02
  1.1908788e-01  3.7425335e-03]
Sparsity at: 0.0
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34 -1.8085713e-11 ... -9.6525319e-02
  1.1919469e-01  3.8959847e-03]
Sparsity at: 0.0
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34 -3.45957851e-05 ... -9.68064517e-02
  1.19119704e-01  4.28887922e-03]
Sparsity at: 0.0
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  2.0061147e-10 ... -9.6539557e-02
  1.1902673e-01  3.5956630e-03]
Sparsity at: 0.0
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34  2.4618037e-05 ... -9.6303917e-02
  1.1909779e-01  3.7636594e-03]
Sparsity at: 0.0
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -9.1779034e-10 ... -9.6516147e-02
  1.1901443e-01  3.6777228e-03]
Sparsity at: 0.0
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -8.82078321e-09 ... -9.66440216e-02
  1.19224764e-01  3.80826066e-03]
Sparsity at: 0.0
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7867 - val_accuracy: 0.9048
[ 2.1660260e-34  2.3500391e-34 -3.0509590e-09 ... -9.6331522e-02
  1.1912048e-01  3.7990536e-03]
Sparsity at: 0.0
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  1.6957579e-11 ... -9.6750781e-02
  1.1934923e-01  3.6006470e-03]
Sparsity at: 0.0
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34  2.90179258e-09 ... -9.67331752e-02
  1.19020246e-01  3.88651621e-03]
Sparsity at: 0.0
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  2.0668889e-13 ... -9.6745804e-02
  1.1914514e-01  3.7794630e-03]
Sparsity at: 0.0
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9038
[ 2.16602604e-34  2.35003912e-34  1.81970847e-08 ... -9.68090072e-02
  1.19248435e-01  4.04160097e-03]
Sparsity at: 0.0
Epoch 268/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7874 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -3.3846271e-14 ... -9.6479014e-02
  1.1925084e-01  4.0319730e-03]
Sparsity at: 0.0
Epoch 269/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7889 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  5.6178385e-07 ... -9.6509315e-02
  1.1925300e-01  4.0081032e-03]
Sparsity at: 0.0
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  8.1456474e-12 ... -9.6462391e-02
  1.1911639e-01  3.9660623e-03]
Sparsity at: 0.0
Epoch 271/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9031
[ 2.16602604e-34  2.35003912e-34 -7.21448077e-06 ... -9.66551751e-02
  1.19135655e-01  4.02177451e-03]
Sparsity at: 0.0
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7869 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -4.1428083e-11 ... -9.6609071e-02
  1.1917197e-01  4.0219193e-03]
Sparsity at: 0.0
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7872 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -3.4298679e-05 ... -9.6777789e-02
  1.1939617e-01  4.2797136e-03]
Sparsity at: 0.0
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7883 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  2.4402824e-10 ... -9.6785866e-02
  1.1927629e-01  4.2709447e-03]
Sparsity at: 0.0
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9029
[ 2.16602604e-34  2.35003912e-34 -1.01073645e-04 ... -9.66128930e-02
  1.19029298e-01  4.03866824e-03]
Sparsity at: 0.0
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9040
[ 2.16602604e-34  2.35003912e-34  1.06242404e-09 ... -9.69587564e-02
  1.19063824e-01  4.32316307e-03]
Sparsity at: 0.0
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9026
[ 2.16602604e-34  2.35003912e-34 -1.05213324e-07 ... -9.67454612e-02
  1.19146951e-01  4.19961335e-03]
Sparsity at: 0.0
Epoch 278/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9031
[ 2.16602604e-34  2.35003912e-34  1.81060367e-09 ... -9.67132971e-02
  1.19107194e-01  4.42450866e-03]
Sparsity at: 0.0
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7889 - val_accuracy: 0.9025
[ 2.1660260e-34  2.3500391e-34 -2.0965583e-10 ... -9.6451685e-02
  1.1926340e-01  4.4435658e-03]
Sparsity at: 0.0
Epoch 280/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9044
[ 2.1660260e-34  2.3500391e-34 -7.7143074e-09 ... -9.6578516e-02
  1.1942077e-01  4.3690898e-03]
Sparsity at: 0.0
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7872 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -7.2700353e-13 ... -9.6828096e-02
  1.1902361e-01  4.2440724e-03]
Sparsity at: 0.0
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -2.64753055e-08 ... -9.67255011e-02
  1.19246304e-01  4.21977649e-03]
Sparsity at: 0.0
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7874 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  4.7735551e-13 ... -9.6346095e-02
  1.1930844e-01  4.1203029e-03]
Sparsity at: 0.0
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  1.9558996e-07 ... -9.6711539e-02
  1.1906434e-01  4.4111614e-03]
Sparsity at: 0.0
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -4.0981736e-13 ... -9.6676365e-02
  1.1907287e-01  4.5553432e-03]
Sparsity at: 0.0
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9025
[ 2.1660260e-34  2.3500391e-34 -1.9513877e-06 ... -9.6382804e-02
  1.1925644e-01  4.1261520e-03]
Sparsity at: 0.0
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34  7.99192656e-12 ... -9.65142846e-02
  1.18844025e-01  4.43081558e-03]
Sparsity at: 0.0
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -5.4785551e-06 ... -9.6287690e-02
  1.1934008e-01  4.3406389e-03]
Sparsity at: 0.0
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7874 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  3.8219351e-11 ... -9.6472383e-02
  1.1912185e-01  4.6075368e-03]
Sparsity at: 0.0
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7870 - val_accuracy: 0.9045
[ 2.1660260e-34  2.3500391e-34  7.7534685e-05 ... -9.6596919e-02
  1.1946670e-01  4.3263799e-03]
Sparsity at: 0.0
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34  4.6739579e-10 ... -9.6622415e-02
  1.1919808e-01  4.2999303e-03]
Sparsity at: 0.0
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9029
[ 2.16602604e-34  2.35003912e-34  1.32014788e-10 ... -9.70803350e-02
  1.19679615e-01  4.47730999e-03]
Sparsity at: 0.0
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -1.0928803e-08 ... -9.6585058e-02
  1.1913126e-01  4.4846023e-03]
Sparsity at: 0.0
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34  8.21644185e-14 ... -9.65961143e-02
  1.19230196e-01  4.52770572e-03]
Sparsity at: 0.0
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  1.9438795e-07 ... -9.6789561e-02
  1.1920497e-01  4.2616460e-03]
Sparsity at: 0.0
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9031
[ 2.16602604e-34  2.35003912e-34 -8.17650255e-13 ... -9.68965217e-02
  1.19466454e-01  4.70501557e-03]
Sparsity at: 0.0
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -6.9717753e-06 ... -9.6400127e-02
  1.1943810e-01  4.3673129e-03]
Sparsity at: 0.0
Epoch 298/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7867 - val_accuracy: 0.9038
[ 2.16602604e-34  2.35003912e-34  1.84110244e-11 ... -9.63126719e-02
  1.19198784e-01  4.33429051e-03]
Sparsity at: 0.0
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -1.8145724e-05 ... -9.6655823e-02
  1.1944599e-01  4.4929753e-03]
Sparsity at: 0.0
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  9.0569180e-10 ... -9.6863449e-02
  1.1928176e-01  4.4454457e-03]
Sparsity at: 0.0
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.04354055087536146
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.09655690938234329
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.1891767531633377
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 51s 7ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  1.9813654e-12 ... -9.6758649e-02
  1.1963684e-01  4.7810404e-03]
Sparsity at: 0.0
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7874 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34  1.4954420e-08 ... -9.7065508e-02
  1.1963763e-01  4.4830227e-03]
Sparsity at: 0.0
Epoch 303/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8034 - accuracy: 0.9017 - val_loss: 0.7876 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -2.2151934e-13 ... -9.6511647e-02
  1.1920562e-01  4.5002829e-03]
Sparsity at: 0.0
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7883 - val_accuracy: 0.9027
[ 2.16602604e-34  2.35003912e-34  3.25067958e-07 ... -9.64846984e-02
  1.19379744e-01  4.47428552e-03]
Sparsity at: 0.0
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7879 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34  2.2351882e-12 ... -9.6837774e-02
  1.1939311e-01  4.7096675e-03]
Sparsity at: 0.0
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9017 - val_loss: 0.7875 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34 -1.92370680e-06 ... -9.66595784e-02
  1.19235486e-01  4.49894508e-03]
Sparsity at: 0.0
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7876 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -2.4853144e-11 ... -9.6467055e-02
  1.1942970e-01  4.4892719e-03]
Sparsity at: 0.0
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7873 - val_accuracy: 0.9036
[ 2.16602604e-34  2.35003912e-34 -5.94379526e-06 ... -9.67059210e-02
  1.19311534e-01  4.66202479e-03]
Sparsity at: 0.0
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7878 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  1.3937974e-10 ... -9.6830338e-02
  1.1907794e-01  4.5704502e-03]
Sparsity at: 0.0
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  1.0975641e-05 ... -9.6449256e-02
  1.1920325e-01  4.6798955e-03]
Sparsity at: 0.0
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7874 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -1.6697754e-11 ... -9.6872523e-02
  1.1951569e-01  4.5997868e-03]
Sparsity at: 0.0
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7866 - val_accuracy: 0.9038
[ 2.16602604e-34  2.35003912e-34 -2.48979420e-10 ... -9.67431739e-02
  1.18946634e-01  4.69627418e-03]
Sparsity at: 0.0
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9031
[ 2.16602604e-34  2.35003912e-34 -9.64496216e-09 ... -9.67529193e-02
  1.19327635e-01  4.82939230e-03]
Sparsity at: 0.0
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -1.1436792e-13 ... -9.6809402e-02
  1.1935494e-01  5.0393119e-03]
Sparsity at: 0.0
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  1.8283681e-07 ... -9.6587539e-02
  1.1930241e-01  4.7898130e-03]
Sparsity at: 0.0
Epoch 316/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7868 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -1.3485259e-12 ... -9.6862890e-02
  1.1943786e-01  4.5788363e-03]
Sparsity at: 0.0
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -1.3091324e-06 ... -9.6672483e-02
  1.1953855e-01  4.6290169e-03]
Sparsity at: 0.0
Epoch 318/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  1.0908414e-11 ... -9.6951917e-02
  1.1968986e-01  4.7119153e-03]
Sparsity at: 0.0
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -7.3423880e-06 ... -9.6485481e-02
  1.1951679e-01  4.6446328e-03]
Sparsity at: 0.0
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34  5.75806729e-11 ... -9.68058780e-02
  1.19720496e-01  4.68989322e-03]
Sparsity at: 0.0
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9031
[ 2.16602604e-34  2.35003912e-34  5.65887094e-05 ... -9.67035815e-02
  1.19318314e-01  4.86098789e-03]
Sparsity at: 0.0
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7871 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -3.2973174e-10 ... -9.6723825e-02
  1.1966369e-01  4.6176454e-03]
Sparsity at: 0.0
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -2.09763493e-05 ... -9.65095162e-02
  1.19338565e-01  4.88473289e-03]
Sparsity at: 0.0
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -1.15962240e-09 ... -9.64239538e-02
  1.19232625e-01  4.63174284e-03]
Sparsity at: 0.0
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34 -1.13957963e-08 ... -9.60857198e-02
  1.18897706e-01  4.66355728e-03]
Sparsity at: 0.0
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7886 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  4.2230637e-09 ... -9.6534915e-02
  1.1929124e-01  4.7262451e-03]
Sparsity at: 0.0
Epoch 327/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9027
[ 2.16602604e-34  2.35003912e-34  8.70957494e-13 ... -9.65014920e-02
  1.19195096e-01  4.85195080e-03]
Sparsity at: 0.0
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9018 - val_loss: 0.7872 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  3.0507934e-08 ... -9.6479036e-02
  1.1901334e-01  4.9957731e-03]
Sparsity at: 0.0
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -2.1914743e-13 ... -9.6440524e-02
  1.1922778e-01  4.5225793e-03]
Sparsity at: 0.0
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7871 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -4.9577784e-07 ... -9.6479788e-02
  1.1930700e-01  4.7602993e-03]
Sparsity at: 0.0
Epoch 331/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7864 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  2.5175803e-12 ... -9.6628048e-02
  1.1924239e-01  4.7345008e-03]
Sparsity at: 0.0
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7886 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34 -5.19743207e-06 ... -9.68395472e-02
  1.19826764e-01  5.04155038e-03]
Sparsity at: 0.0
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34 -2.97040934e-11 ... -9.65827703e-02
  1.19302906e-01  4.90569044e-03]
Sparsity at: 0.0
Epoch 334/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9025
[ 2.1660260e-34  2.3500391e-34  6.0509337e-05 ... -9.6771836e-02
  1.1961913e-01  4.8657497e-03]
Sparsity at: 0.0
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7873 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34 -3.66719544e-10 ... -9.67598334e-02
  1.19739056e-01  5.06662717e-03]
Sparsity at: 0.0
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7884 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -4.8988111e-09 ... -9.6538819e-02
  1.1952799e-01  4.8865764e-03]
Sparsity at: 0.0
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  3.9914392e-09 ... -9.6825629e-02
  1.1969762e-01  5.1835999e-03]
Sparsity at: 0.0
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9025
[ 2.16602604e-34  2.35003912e-34 -1.23081504e-12 ... -9.65617672e-02
  1.19647324e-01  5.02907345e-03]
Sparsity at: 0.0
Epoch 339/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  1.3323797e-08 ... -9.6829847e-02
  1.1963277e-01  4.9272422e-03]
Sparsity at: 0.0
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7876 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -1.6279609e-14 ... -9.6451759e-02
  1.1965431e-01  5.0872872e-03]
Sparsity at: 0.0
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9037
[ 2.16602604e-34  2.35003912e-34 -2.14681364e-07 ... -9.64742228e-02
  1.19442746e-01  4.93557937e-03]
Sparsity at: 0.0
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34  5.0729711e-13 ... -9.7101122e-02
  1.1981982e-01  5.0652577e-03]
Sparsity at: 0.0
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7867 - val_accuracy: 0.9042
[ 2.1660260e-34  2.3500391e-34  3.0280276e-07 ... -9.6620522e-02
  1.1969310e-01  4.7933762e-03]
Sparsity at: 0.0
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9017 - val_loss: 0.7876 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  2.2298647e-12 ... -9.6711516e-02
  1.1975234e-01  4.7575212e-03]
Sparsity at: 0.0
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  1.5909659e-06 ... -9.6821472e-02
  1.1988509e-01  5.0525502e-03]
Sparsity at: 0.0
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7888 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34  2.1747937e-11 ... -9.6737847e-02
  1.2001068e-01  5.0700046e-03]
Sparsity at: 0.0
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -1.3313786e-05 ... -9.6733183e-02
  1.1966812e-01  5.3257551e-03]
Sparsity at: 0.0
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34  1.3955864e-11 ... -9.6871786e-02
  1.1981682e-01  5.3792899e-03]
Sparsity at: 0.0
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -2.5418412e-05 ... -9.7003378e-02
  1.1982142e-01  5.3846450e-03]
Sparsity at: 0.0
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -1.2926575e-10 ... -9.6693955e-02
  1.1954377e-01  5.1366673e-03]
Sparsity at: 0.0
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.05145336870375461
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.10824567258369111
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.2044821729245374
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7872 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  9.8307639e-05 ... -9.7003378e-02
  1.1950693e-01  5.2235881e-03]
Sparsity at: 0.0
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7873 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -2.7235830e-10 ... -9.6948385e-02
  1.2008926e-01  5.4087066e-03]
Sparsity at: 0.0
Epoch 353/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8030 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34  9.02552256e-06 ... -9.68917087e-02
  1.20336965e-01  5.30159194e-03]
Sparsity at: 0.0
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -3.7707426e-10 ... -9.6661590e-02
  1.1938381e-01  5.4519870e-03]
Sparsity at: 0.0
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -7.3293333e-12 ... -9.6732311e-02
  1.1958466e-01  5.6256307e-03]
Sparsity at: 0.0
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7878 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -6.7991874e-09 ... -9.6412078e-02
  1.1975115e-01  5.7636732e-03]
Sparsity at: 0.0
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  1.5820258e-13 ... -9.7008251e-02
  1.1969633e-01  5.5577708e-03]
Sparsity at: 0.0
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7879 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -3.0235458e-07 ... -9.6487977e-02
  1.1962325e-01  5.5306531e-03]
Sparsity at: 0.0
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7880 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  5.9825289e-13 ... -9.6958421e-02
  1.2016310e-01  5.3909556e-03]
Sparsity at: 0.0
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -1.3786761e-05 ... -9.6753158e-02
  1.1949889e-01  5.7063168e-03]
Sparsity at: 0.0
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9018 - val_loss: 0.7878 - val_accuracy: 0.9045
[ 2.1660260e-34  2.3500391e-34 -7.1528880e-11 ... -9.6739851e-02
  1.1973554e-01  5.7600029e-03]
Sparsity at: 0.0
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7887 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -5.5535310e-08 ... -9.7103253e-02
  1.1998576e-01  5.7675806e-03]
Sparsity at: 0.0
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7884 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -3.2936032e-09 ... -9.7003855e-02
  1.2026122e-01  5.8288397e-03]
Sparsity at: 0.0
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7881 - val_accuracy: 0.9026
[ 2.16602604e-34  2.35003912e-34 -6.90607172e-14 ... -9.69852731e-02
  1.20121114e-01  5.72779169e-03]
Sparsity at: 0.0
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9017 - val_loss: 0.7875 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -1.0825247e-07 ... -9.6848883e-02
  1.1989435e-01  5.6389375e-03]
Sparsity at: 0.0
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7885 - val_accuracy: 0.9024
[ 2.1660260e-34  2.3500391e-34 -2.2451767e-13 ... -9.7046338e-02
  1.1985630e-01  5.9226602e-03]
Sparsity at: 0.0
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  6.3173502e-08 ... -9.6872389e-02
  1.1989870e-01  5.6399065e-03]
Sparsity at: 0.0
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9041
[ 2.1660260e-34  2.3500391e-34 -1.0421018e-11 ... -9.6868396e-02
  1.1972114e-01  5.8509554e-03]
Sparsity at: 0.0
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9016 - val_loss: 0.7881 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -1.0213105e-05 ... -9.7003013e-02
  1.2012153e-01  5.8855652e-03]
Sparsity at: 0.0
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -1.1101617e-10 ... -9.6890718e-02
  1.1999429e-01  5.8214618e-03]
Sparsity at: 0.0
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34  4.6373668e-07 ... -9.6690968e-02
  1.2004442e-01  5.6810924e-03]
Sparsity at: 0.0
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34  2.41512188e-09 ... -9.66891497e-02
  1.19978495e-01  5.78028103e-03]
Sparsity at: 0.0
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  1.9671905e-13 ... -9.7123556e-02
  1.2004318e-01  6.2524495e-03]
Sparsity at: 0.0
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9036
[ 2.16602604e-34  2.35003912e-34 -4.60832723e-08 ... -9.71458852e-02
  1.20091215e-01  6.10441016e-03]
Sparsity at: 0.0
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  8.4539445e-14 ... -9.6581340e-02
  1.1950767e-01  6.1184750e-03]
Sparsity at: 0.0
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -2.3984039e-06 ... -9.6876912e-02
  1.1959945e-01  5.7548177e-03]
Sparsity at: 0.0
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7865 - val_accuracy: 0.9040
[ 2.16602604e-34  2.35003912e-34  1.08241003e-11 ... -9.68324915e-02
  1.19781375e-01  5.71073405e-03]
Sparsity at: 0.0
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34 -5.75929917e-05 ... -9.66646522e-02
  1.19968995e-01  5.81616024e-03]
Sparsity at: 0.0
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34 -1.40503678e-10 ... -9.71320048e-02
  1.20432705e-01  5.98466583e-03]
Sparsity at: 0.0
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9031
[ 2.16602604e-34  2.35003912e-34  3.84834303e-10 ... -9.69519243e-02
  1.20028965e-01  6.13523042e-03]
Sparsity at: 0.0
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7880 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -8.9390895e-10 ... -9.6935637e-02
  1.1984949e-01  6.1177881e-03]
Sparsity at: 0.0
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  2.7136625e-14 ... -9.6559122e-02
  1.1982359e-01  6.0703331e-03]
Sparsity at: 0.0
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  6.2195369e-09 ... -9.6723981e-02
  1.1982583e-01  6.0848384e-03]
Sparsity at: 0.0
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7871 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -8.3749094e-13 ... -9.6717395e-02
  1.1964802e-01  5.7577873e-03]
Sparsity at: 0.0
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7876 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -3.8387143e-06 ... -9.6656099e-02
  1.1975571e-01  5.9236684e-03]
Sparsity at: 0.0
Epoch 386/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -3.7445090e-12 ... -9.6875966e-02
  1.1975334e-01  5.9791021e-03]
Sparsity at: 0.0
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34  1.6371628e-04 ... -9.6999265e-02
  1.1970727e-01  5.8977683e-03]
Sparsity at: 0.0
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7883 - val_accuracy: 0.9017
[ 2.1660260e-34  2.3500391e-34  2.1304680e-11 ... -9.6909545e-02
  1.1974877e-01  6.0302406e-03]
Sparsity at: 0.0
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  4.0213388e-12 ... -9.6977040e-02
  1.1966834e-01  5.8283499e-03]
Sparsity at: 0.0
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7873 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  1.9608642e-08 ... -9.6805379e-02
  1.1951526e-01  5.8710417e-03]
Sparsity at: 0.0
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7872 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -1.1520227e-13 ... -9.6653968e-02
  1.1946594e-01  5.8363043e-03]
Sparsity at: 0.0
Epoch 392/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9037
[ 2.16602604e-34  2.35003912e-34 -5.55430006e-07 ... -9.65471342e-02
  1.19637795e-01  5.66599751e-03]
Sparsity at: 0.0
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  5.7021861e-12 ... -9.6932977e-02
  1.1960710e-01  5.8986577e-03]
Sparsity at: 0.0
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34  3.7718397e-05 ... -9.6699521e-02
  1.1938030e-01  5.8800378e-03]
Sparsity at: 0.0
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  1.7203144e-10 ... -9.7028583e-02
  1.1972045e-01  5.8591082e-03]
Sparsity at: 0.0
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34  2.9206672e-07 ... -9.6768357e-02
  1.1972195e-01  5.9745307e-03]
Sparsity at: 0.0
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9016 - val_loss: 0.7872 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34 -2.49674037e-09 ... -9.69161913e-02
  1.19651906e-01  5.85845439e-03]
Sparsity at: 0.0
Epoch 398/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7874 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34 -1.13944696e-11 ... -9.69721898e-02
  1.19592935e-01  6.19879598e-03]
Sparsity at: 0.0
Epoch 399/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7871 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -1.9942899e-08 ... -9.6477263e-02
  1.1951710e-01  6.0139969e-03]
Sparsity at: 0.0
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7884 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34 -1.25924051e-13 ... -9.66862217e-02
  1.19570866e-01  6.08662562e-03]
Sparsity at: 0.0
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.05604266125902191
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.1164555019028306
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.2093774694217707
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 47s 7ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34  2.11803837e-08 ... -9.68910083e-02
  1.19653165e-01  6.18669903e-03]
Sparsity at: 0.0
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -8.2226381e-13 ... -9.6993789e-02
  1.1996815e-01  6.0370034e-03]
Sparsity at: 0.0
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -3.2960776e-07 ... -9.6749894e-02
  1.2017904e-01  5.9685670e-03]
Sparsity at: 0.0
Epoch 404/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -1.6154834e-12 ... -9.6703604e-02
  1.2003152e-01  6.1346986e-03]
Sparsity at: 0.0
Epoch 405/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7868 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -2.5189538e-06 ... -9.7272448e-02
  1.2017930e-01  6.0802735e-03]
Sparsity at: 0.0
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9017 - val_loss: 0.7877 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -1.0730449e-11 ... -9.7314313e-02
  1.2002343e-01  6.3132341e-03]
Sparsity at: 0.0
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7870 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -4.2505904e-05 ... -9.6687689e-02
  1.1983128e-01  6.2026680e-03]
Sparsity at: 0.0
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7882 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34  1.71331338e-10 ... -9.68058631e-02
  1.19859695e-01  5.86007209e-03]
Sparsity at: 0.0
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9017 - val_loss: 0.7864 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34 -2.0586026e-06 ... -9.6797362e-02
  1.1959352e-01  6.1214152e-03]
Sparsity at: 0.0
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  1.9371993e-09 ... -9.6964777e-02
  1.1994149e-01  6.0078702e-03]
Sparsity at: 0.0
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  2.5125373e-12 ... -9.6986793e-02
  1.1979263e-01  6.2496606e-03]
Sparsity at: 0.0
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34  2.49314080e-08 ... -9.68761966e-02
  1.19823396e-01  6.05533179e-03]
Sparsity at: 0.0
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -2.2053271e-13 ... -9.6797533e-02
  1.1988019e-01  6.0640452e-03]
Sparsity at: 0.0
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34 -4.51945596e-07 ... -9.67677832e-02
  1.20063774e-01  6.09964831e-03]
Sparsity at: 0.0
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7877 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  2.5493010e-12 ... -9.6984856e-02
  1.2006588e-01  6.0387384e-03]
Sparsity at: 0.0
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9034
[ 2.16602604e-34  2.35003912e-34 -4.28598241e-06 ... -9.70633179e-02
  1.20159596e-01  6.27308572e-03]
Sparsity at: 0.0
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7866 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  3.1537772e-11 ... -9.6733533e-02
  1.2000912e-01  6.1094481e-03]
Sparsity at: 0.0
Epoch 418/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34 -9.3952549e-05 ... -9.6853152e-02
  1.2001436e-01  6.1458792e-03]
Sparsity at: 0.0
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9029
[ 2.16602604e-34  2.35003912e-34  4.03115652e-10 ... -9.68397930e-02
  1.19782664e-01  6.07515452e-03]
Sparsity at: 0.0
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7876 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34  8.2856708e-09 ... -9.7064942e-02
  1.2006970e-01  6.3092215e-03]
Sparsity at: 0.0
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9016 - val_loss: 0.7880 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -1.6366297e-09 ... -9.6863873e-02
  1.2016517e-01  6.0936185e-03]
Sparsity at: 0.0
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9017 - val_loss: 0.7879 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34  3.47464006e-12 ... -9.69051123e-02
  1.19845696e-01  6.19111676e-03]
Sparsity at: 0.0
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7886 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34  2.7047111e-08 ... -9.6784107e-02
  1.2018630e-01  6.0146796e-03]
Sparsity at: 0.0
Epoch 424/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7864 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34  2.5319110e-13 ... -9.6873581e-02
  1.1975906e-01  6.0938802e-03]
Sparsity at: 0.0
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7864 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -2.26299392e-07 ... -9.70703736e-02
  1.19921975e-01  5.97802410e-03]
Sparsity at: 0.0
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -1.2849633e-12 ... -9.6831903e-02
  1.1997407e-01  6.1513130e-03]
Sparsity at: 0.0
Epoch 427/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -3.7824166e-06 ... -9.6877001e-02
  1.1998094e-01  6.1150845e-03]
Sparsity at: 0.0
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34 -6.7700598e-12 ... -9.6610129e-02
  1.1990828e-01  6.1241817e-03]
Sparsity at: 0.0
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7870 - val_accuracy: 0.9041
[ 2.1660260e-34  2.3500391e-34 -1.1619132e-05 ... -9.6801765e-02
  1.1985122e-01  6.4164218e-03]
Sparsity at: 0.0
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7871 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  1.5070659e-10 ... -9.6866682e-02
  1.2019607e-01  6.1873957e-03]
Sparsity at: 0.0
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9017 - val_loss: 0.7882 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34 -6.6356870e-07 ... -9.6852802e-02
  1.2018902e-01  6.2581245e-03]
Sparsity at: 0.0
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7872 - val_accuracy: 0.9038
[ 2.16602604e-34  2.35003912e-34 -2.40354914e-09 ... -9.68077853e-02
  1.20021254e-01  6.34369720e-03]
Sparsity at: 0.0
Epoch 433/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9026
[ 2.1660260e-34  2.3500391e-34  2.5558396e-14 ... -9.6869588e-02
  1.1994986e-01  6.4454237e-03]
Sparsity at: 0.0
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34  3.22880105e-08 ... -9.66106653e-02
  1.19935594e-01  6.26665773e-03]
Sparsity at: 0.0
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34  3.94372848e-14 ... -9.66278687e-02
  1.19856775e-01  6.34732330e-03]
Sparsity at: 0.0
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7884 - val_accuracy: 0.9036
[ 2.1660260e-34  2.3500391e-34  3.0181363e-05 ... -9.6726850e-02
  1.2010156e-01  6.0569569e-03]
Sparsity at: 0.0
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34 -7.58396679e-11 ... -9.66314375e-02
  1.20232135e-01  6.24407222e-03]
Sparsity at: 0.0
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34  3.62845452e-15 ... -9.67943892e-02
  1.20058715e-01  6.38965983e-03]
Sparsity at: 0.0
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34 -1.0136169e-07 ... -9.6574388e-02
  1.2011247e-01  5.9472765e-03]
Sparsity at: 0.0
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7879 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -9.0069694e-13 ... -9.6664295e-02
  1.2008607e-01  6.3667754e-03]
Sparsity at: 0.0
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7873 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -1.8208662e-05 ... -9.6821517e-02
  1.1988639e-01  6.4522256e-03]
Sparsity at: 0.0
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7888 - val_accuracy: 0.9027
[ 2.1660260e-34  2.3500391e-34 -1.4914363e-10 ... -9.6652083e-02
  1.1993245e-01  6.4796647e-03]
Sparsity at: 0.0
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7871 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  3.6563585e-10 ... -9.6730076e-02
  1.2016911e-01  6.0588238e-03]
Sparsity at: 0.0
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7887 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  9.1120782e-09 ... -9.6796393e-02
  1.2006035e-01  6.1519514e-03]
Sparsity at: 0.0
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -8.7613077e-14 ... -9.6633457e-02
  1.2002222e-01  6.2637120e-03]
Sparsity at: 0.0
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34 -1.7388149e-07 ... -9.6840307e-02
  1.2035176e-01  6.0975980e-03]
Sparsity at: 0.0
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7888 - val_accuracy: 0.9028
[ 2.1660260e-34  2.3500391e-34 -4.9876769e-13 ... -9.7027496e-02
  1.2016390e-01  6.5167742e-03]
Sparsity at: 0.0
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7872 - val_accuracy: 0.9042
[ 2.16602604e-34  2.35003912e-34 -3.13358237e-06 ... -9.67752635e-02
  1.19794026e-01  6.25483645e-03]
Sparsity at: 0.0
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9019 - val_loss: 0.7867 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  1.3018302e-12 ... -9.6899807e-02
  1.1963556e-01  6.4656343e-03]
Sparsity at: 0.0
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9041
[ 2.16602604e-34  2.35003912e-34 -4.21410587e-05 ... -9.68064517e-02
  1.19539544e-01  6.40239334e-03]
Sparsity at: 0.0
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  2.1450042e-10 ... -9.6703365e-02
  1.1972029e-01  6.0641859e-03]
Sparsity at: 0.0
Epoch 452/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7871 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  1.5870432e-05 ... -9.6656941e-02
  1.1982519e-01  5.8807763e-03]
Sparsity at: 0.0
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7872 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -2.2737554e-10 ... -9.6391819e-02
  1.1990812e-01  5.9275297e-03]
Sparsity at: 0.0
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7871 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -5.4503957e-09 ... -9.6634492e-02
  1.1957248e-01  6.3576996e-03]
Sparsity at: 0.0
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  1.7715203e-09 ... -9.6448474e-02
  1.1943516e-01  6.1257286e-03]
Sparsity at: 0.0
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -1.2278869e-12 ... -9.6641734e-02
  1.1943556e-01  6.0009961e-03]
Sparsity at: 0.0
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34  4.6767150e-09 ... -9.6941188e-02
  1.1988121e-01  6.3115200e-03]
Sparsity at: 0.0
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -2.0568784e-13 ... -9.6694328e-02
  1.1969359e-01  6.1274185e-03]
Sparsity at: 0.0
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9015 - val_loss: 0.7879 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  6.8728997e-07 ... -9.6564814e-02
  1.1977622e-01  5.9234733e-03]
Sparsity at: 0.0
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -9.6142599e-12 ... -9.6528895e-02
  1.1958755e-01  6.4544128e-03]
Sparsity at: 0.0
Epoch 461/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34 -3.3623728e-05 ... -9.6462905e-02
  1.1981018e-01  6.5388810e-03]
Sparsity at: 0.0
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -2.2112900e-10 ... -9.6497320e-02
  1.1966681e-01  6.3337944e-03]
Sparsity at: 0.0
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9036
[ 2.16602604e-34  2.35003912e-34 -3.43370250e-14 ... -9.64868590e-02
  1.19646326e-01  6.32484723e-03]
Sparsity at: 0.0
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -6.2970102e-08 ... -9.6439809e-02
  1.1963749e-01  6.4403615e-03]
Sparsity at: 0.0
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -6.01244098e-13 ... -9.63304564e-02
  1.19518206e-01  6.16516592e-03]
Sparsity at: 0.0
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  8.9890773e-07 ... -9.6772701e-02
  1.1963927e-01  6.5734200e-03]
Sparsity at: 0.0
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  1.0336487e-11 ... -9.6511684e-02
  1.1973165e-01  6.1983364e-03]
Sparsity at: 0.0
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34  1.3799511e-04 ... -9.6806854e-02
  1.2002606e-01  6.0931677e-03]
Sparsity at: 0.0
Epoch 469/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  3.8945291e-11 ... -9.6508339e-02
  1.1983407e-01  6.4912629e-03]
Sparsity at: 0.0
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9016 - val_loss: 0.7877 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  2.8059024e-12 ... -9.6576661e-02
  1.1978862e-01  6.4580557e-03]
Sparsity at: 0.0
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9035
[ 2.16602604e-34  2.35003912e-34  2.24938788e-08 ... -9.66281071e-02
  1.19458735e-01  6.53707841e-03]
Sparsity at: 0.0
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9014 - val_loss: 0.7887 - val_accuracy: 0.9024
[ 2.1660260e-34  2.3500391e-34  4.3516406e-14 ... -9.6558474e-02
  1.2012070e-01  6.3651972e-03]
Sparsity at: 0.0
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9031
[ 2.1660260e-34  2.3500391e-34  6.3062373e-07 ... -9.6434094e-02
  1.1962295e-01  6.0375966e-03]
Sparsity at: 0.0
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -1.9675771e-12 ... -9.6351877e-02
  1.1966482e-01  5.7850243e-03]
Sparsity at: 0.0
Epoch 475/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7865 - val_accuracy: 0.9045
[ 2.1660260e-34  2.3500391e-34  3.9097718e-05 ... -9.6481562e-02
  1.1972946e-01  6.2424713e-03]
Sparsity at: 0.0
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -2.3405267e-10 ... -9.6507706e-02
  1.1951510e-01  6.3635283e-03]
Sparsity at: 0.0
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -3.3314522e-09 ... -9.6587010e-02
  1.1939046e-01  6.4207520e-03]
Sparsity at: 0.0
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  5.6890537e-09 ... -9.6447788e-02
  1.1964595e-01  6.4400886e-03]
Sparsity at: 0.0
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  4.5537695e-13 ... -9.6305668e-02
  1.1946895e-01  6.4479001e-03]
Sparsity at: 0.0
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7869 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34 -1.52319544e-08 ... -9.64807943e-02
  1.19484685e-01  6.56530866e-03]
Sparsity at: 0.0
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7879 - val_accuracy: 0.9032
[ 2.1660260e-34  2.3500391e-34  5.0520140e-13 ... -9.6256882e-02
  1.1955718e-01  6.2320633e-03]
Sparsity at: 0.0
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7872 - val_accuracy: 0.9039
[ 2.1660260e-34  2.3500391e-34 -3.4109598e-07 ... -9.6293196e-02
  1.1936905e-01  5.8118533e-03]
Sparsity at: 0.0
Epoch 483/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7878 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34  2.1875045e-12 ... -9.6251570e-02
  1.1964322e-01  6.3000531e-03]
Sparsity at: 0.0
Epoch 484/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9044
[ 2.1660260e-34  2.3500391e-34  9.0741116e-07 ... -9.6350372e-02
  1.1956038e-01  6.0452777e-03]
Sparsity at: 0.0
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7880 - val_accuracy: 0.9028
[ 2.16602604e-34  2.35003912e-34  2.10113307e-12 ... -9.65864584e-02
  1.19602114e-01  6.43824181e-03]
Sparsity at: 0.0
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9037
[ 2.1660260e-34  2.3500391e-34  6.9361881e-06 ... -9.6337333e-02
  1.1962793e-01  6.2364973e-03]
Sparsity at: 0.0
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7874 - val_accuracy: 0.9035
[ 2.1660260e-34  2.3500391e-34 -1.6157064e-11 ... -9.6428081e-02
  1.1945501e-01  6.1982074e-03]
Sparsity at: 0.0
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9015 - val_loss: 0.7873 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34  6.8449899e-06 ... -9.6295506e-02
  1.1927847e-01  6.2280563e-03]
Sparsity at: 0.0
Epoch 489/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7885 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -1.0986220e-10 ... -9.6211970e-02
  1.1972855e-01  6.2315315e-03]
Sparsity at: 0.0
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7882 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -7.9979473e-05 ... -9.6423574e-02
  1.1982947e-01  6.3107652e-03]
Sparsity at: 0.0
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9030
[ 2.16602604e-34  2.35003912e-34  1.95383321e-10 ... -9.62549597e-02
  1.19502805e-01  6.25532959e-03]
Sparsity at: 0.0
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9040
[ 2.1660260e-34  2.3500391e-34 -7.1219179e-06 ... -9.6297696e-02
  1.1940506e-01  6.5222029e-03]
Sparsity at: 0.0
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9033
[ 2.16602604e-34  2.35003912e-34 -7.03366088e-10 ... -9.62297618e-02
  1.19293556e-01  6.43690070e-03]
Sparsity at: 0.0
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9034
[ 2.1660260e-34  2.3500391e-34 -5.7153233e-09 ... -9.6396729e-02
  1.1946530e-01  6.3012154e-03]
Sparsity at: 0.0
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9038
[ 2.1660260e-34  2.3500391e-34 -1.5976465e-09 ... -9.6504703e-02
  1.1962247e-01  6.5393555e-03]
Sparsity at: 0.0
Epoch 496/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7883 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34 -3.8030327e-13 ... -9.6533053e-02
  1.1964298e-01  6.4152358e-03]
Sparsity at: 0.0
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9029
[ 2.1660260e-34  2.3500391e-34 -4.1943888e-08 ... -9.6567661e-02
  1.1965421e-01  6.4011645e-03]
Sparsity at: 0.0
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9032
[ 2.16602604e-34  2.35003912e-34  2.13910703e-13 ... -9.63872373e-02
  1.19854644e-01  6.28406275e-03]
Sparsity at: 0.0
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9033
[ 2.1660260e-34  2.3500391e-34 -3.7488508e-07 ... -9.6168473e-02
  1.1950279e-01  6.4220098e-03]
Sparsity at: 0.0
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7881 - val_accuracy: 0.9030
[ 2.1660260e-34  2.3500391e-34  6.9685707e-13 ... -9.6381672e-02
  1.1948452e-01  6.2607042e-03]
Sparsity at: 0.0
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.042017221450805664
Thresholhold -0.06162944808602333
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.08907948434352875
Thresholhold -0.10798515379428864
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10679344832897186
Thresholhold -0.06120911240577698
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 59:55 - loss: 2.3590 - accuracy: 0.1562WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_begin` time: 2.5039s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 0.4625 - accuracy: 0.8706 - val_loss: 0.2481 - val_accuracy: 0.9271
[-0.06162945  0.01141503 -0.00061712 ... -0.24551293 -0.07918725
 -0.01226392]
Sparsity at: 0.0
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2256 - accuracy: 0.9347 - val_loss: 0.1875 - val_accuracy: 0.9459
[-0.06162945  0.01141503 -0.00061712 ... -0.27347594 -0.08841381
 -0.01025021]
Sparsity at: 0.0
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1710 - accuracy: 0.9496 - val_loss: 0.1556 - val_accuracy: 0.9545
[-0.06162945  0.01141503 -0.00061712 ... -0.29839796 -0.09410758
 -0.00589253]
Sparsity at: 0.0
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1375 - accuracy: 0.9596 - val_loss: 0.1364 - val_accuracy: 0.9598
[-0.06162945  0.01141503 -0.00061712 ... -0.3188608  -0.0981459
 -0.0017014 ]
Sparsity at: 0.0
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1135 - accuracy: 0.9670 - val_loss: 0.1237 - val_accuracy: 0.9633
[-0.06162945  0.01141503 -0.00061712 ... -0.33559248 -0.10105435
  0.00084789]
Sparsity at: 0.0
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0954 - accuracy: 0.9725 - val_loss: 0.1148 - val_accuracy: 0.9646
[-0.06162945  0.01141503 -0.00061712 ... -0.34973148 -0.10330356
  0.00239677]
Sparsity at: 0.0
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0810 - accuracy: 0.9761 - val_loss: 0.1089 - val_accuracy: 0.9660
[-0.06162945  0.01141503 -0.00061712 ... -0.36135045 -0.1054047
  0.00306919]
Sparsity at: 0.0
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0694 - accuracy: 0.9801 - val_loss: 0.1059 - val_accuracy: 0.9682
[-0.06162945  0.01141503 -0.00061712 ... -0.37208366 -0.10731635
  0.0033695 ]
Sparsity at: 0.0
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.1024 - val_accuracy: 0.9692
[-0.06162945  0.01141503 -0.00061712 ... -0.38165015 -0.10870424
  0.00371294]
Sparsity at: 0.0
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0518 - accuracy: 0.9854 - val_loss: 0.1010 - val_accuracy: 0.9703
[-0.06162945  0.01141503 -0.00061712 ... -0.3913611  -0.10980254
  0.00381318]
Sparsity at: 0.0
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0450 - accuracy: 0.9873 - val_loss: 0.0996 - val_accuracy: 0.9706
[-0.06162945  0.01141503 -0.00061712 ... -0.40049657 -0.11058482
  0.00409445]
Sparsity at: 0.0
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0390 - accuracy: 0.9894 - val_loss: 0.1004 - val_accuracy: 0.9712
[-0.06162945  0.01141503 -0.00061712 ... -0.41025305 -0.11113498
  0.00488364]
Sparsity at: 0.0
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0340 - accuracy: 0.9910 - val_loss: 0.1008 - val_accuracy: 0.9714
[-0.06162945  0.01141503 -0.00061712 ... -0.42007586 -0.11131507
  0.00613106]
Sparsity at: 0.0
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0292 - accuracy: 0.9926 - val_loss: 0.1015 - val_accuracy: 0.9708
[-0.06162945  0.01141503 -0.00061712 ... -0.43122864 -0.1111505
  0.00819975]
Sparsity at: 0.0
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0250 - accuracy: 0.9939 - val_loss: 0.1031 - val_accuracy: 0.9710
[-0.06162945  0.01141503 -0.00061712 ... -0.44218895 -0.11258055
  0.01155277]
Sparsity at: 0.0
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0213 - accuracy: 0.9954 - val_loss: 0.1045 - val_accuracy: 0.9720
[-0.06162945  0.01141503 -0.00061712 ... -0.45352656 -0.11415644
  0.01519095]
Sparsity at: 0.0
Epoch 17/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0183 - accuracy: 0.9964 - val_loss: 0.1090 - val_accuracy: 0.9705
[-0.06162945  0.01141503 -0.00061712 ... -0.46490172 -0.11548899
  0.01807612]
Sparsity at: 0.0
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0157 - accuracy: 0.9973 - val_loss: 0.1107 - val_accuracy: 0.9706
[-0.06162945  0.01141503 -0.00061712 ... -0.47607267 -0.11764724
  0.02133511]
Sparsity at: 0.0
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0135 - accuracy: 0.9978 - val_loss: 0.1123 - val_accuracy: 0.9711
[-0.06162945  0.01141503 -0.00061712 ... -0.4872291  -0.12064837
  0.02294913]
Sparsity at: 0.0
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0119 - accuracy: 0.9981 - val_loss: 0.1181 - val_accuracy: 0.9704
[-0.06162945  0.01141503 -0.00061712 ... -0.49602535 -0.12553558
  0.02708043]
Sparsity at: 0.0
Epoch 21/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0108 - accuracy: 0.9981 - val_loss: 0.1206 - val_accuracy: 0.9713
[-0.06162945  0.01141503 -0.00061712 ... -0.50377405 -0.13176425
  0.03063378]
Sparsity at: 0.0
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0104 - accuracy: 0.9979 - val_loss: 0.1180 - val_accuracy: 0.9718
[-0.06162945  0.01141503 -0.00061712 ... -0.5136285  -0.13778561
  0.02540568]
Sparsity at: 0.0
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0106 - accuracy: 0.9975 - val_loss: 0.1241 - val_accuracy: 0.9703
[-0.06162945  0.01141503 -0.00061712 ... -0.5221462  -0.14337744
  0.02686568]
Sparsity at: 0.0
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0114 - accuracy: 0.9971 - val_loss: 0.1265 - val_accuracy: 0.9701
[-0.06162945  0.01141503 -0.00061712 ... -0.5349539  -0.14867003
  0.02780755]
Sparsity at: 0.0
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0106 - accuracy: 0.9970 - val_loss: 0.1244 - val_accuracy: 0.9719
[-0.06162945  0.01141503 -0.00061712 ... -0.5375083  -0.1524253
  0.01640458]
Sparsity at: 0.0
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.1402 - val_accuracy: 0.9672
[-0.06162945  0.01141503 -0.00061712 ... -0.5509785  -0.14408737
  0.0099421 ]
Sparsity at: 0.0
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9976 - val_loss: 0.1204 - val_accuracy: 0.9735
[-0.06162945  0.01141503 -0.00061712 ... -0.55805826 -0.14622964
  0.01139615]
Sparsity at: 0.0
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0068 - accuracy: 0.9985 - val_loss: 0.1179 - val_accuracy: 0.9746
[-0.06162945  0.01141503 -0.00061712 ... -0.56111133 -0.14597611
 -0.00142569]
Sparsity at: 0.0
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0057 - accuracy: 0.9987 - val_loss: 0.1211 - val_accuracy: 0.9750
[-0.06162945  0.01141503 -0.00061712 ... -0.5670354  -0.14752737
  0.00125402]
Sparsity at: 0.0
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0042 - accuracy: 0.9992 - val_loss: 0.1270 - val_accuracy: 0.9741
[-0.06162945  0.01141503 -0.00061712 ... -0.572229   -0.15037076
  0.00352814]
Sparsity at: 0.0
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0040 - accuracy: 0.9994 - val_loss: 0.1307 - val_accuracy: 0.9731
[-0.06162945  0.01141503 -0.00061712 ... -0.57740575 -0.15446031
  0.00388532]
Sparsity at: 0.0
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 0.9999 - val_loss: 0.1193 - val_accuracy: 0.9760
[-0.06162945  0.01141503 -0.00061712 ... -0.5823741  -0.15239309
  0.00552276]
Sparsity at: 0.0
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.1225 - val_accuracy: 0.9758
[-0.06162945  0.01141503 -0.00061712 ... -0.5870701  -0.15869783
  0.00642516]
Sparsity at: 0.0
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0018 - accuracy: 0.9998 - val_loss: 0.1264 - val_accuracy: 0.9749
[-0.06162945  0.01141503 -0.00061712 ... -0.5896417  -0.15583056
  0.00965309]
Sparsity at: 0.0
Epoch 35/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1275 - val_accuracy: 0.9749
[-0.06162945  0.01141503 -0.00061712 ... -0.5919178  -0.16490872
  0.01448976]
Sparsity at: 0.0
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0048 - accuracy: 0.9986 - val_loss: 0.1602 - val_accuracy: 0.9687
[-0.06162945  0.01141503 -0.00061712 ... -0.6050982  -0.16071376
  0.01137671]
Sparsity at: 0.0
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0088 - accuracy: 0.9972 - val_loss: 0.1420 - val_accuracy: 0.9726
[-0.06162945  0.01141503 -0.00061712 ... -0.6028426  -0.16374318
  0.00796165]
Sparsity at: 0.0
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.1361 - val_accuracy: 0.9740
[-0.06162945  0.01141503 -0.00061712 ... -0.604199   -0.16657922
  0.02173672]
Sparsity at: 0.0
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.1404 - val_accuracy: 0.9741
[-0.06162945  0.01141503 -0.00061712 ... -0.6109027  -0.15875584
  0.02848339]
Sparsity at: 0.0
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 0.1461 - val_accuracy: 0.9726
[-0.06162945  0.01141503 -0.00061712 ... -0.6128059  -0.17361939
  0.02422939]
Sparsity at: 0.0
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.1458 - val_accuracy: 0.9734
[-0.06162945  0.01141503 -0.00061712 ... -0.6146821  -0.17851873
  0.0223424 ]
Sparsity at: 0.0
Epoch 42/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1442 - val_accuracy: 0.9745
[-0.06162945  0.01141503 -0.00061712 ... -0.6156024  -0.18271302
  0.02176825]
Sparsity at: 0.0
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9995 - val_loss: 0.1607 - val_accuracy: 0.9736
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.18947625e-01
 -1.84821114e-01  2.75941491e-02]
Sparsity at: 0.0
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1521 - val_accuracy: 0.9739
[-0.06162945  0.01141503 -0.00061712 ... -0.6144523  -0.1768818
  0.02819831]
Sparsity at: 0.0
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 9.1685e-04 - accuracy: 0.9999 - val_loss: 0.1464 - val_accuracy: 0.9751
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.20002091e-01
 -1.78854421e-01  2.96269972e-02]
Sparsity at: 0.0
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6178e-04 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.22364402e-01
 -1.75523400e-01  2.79622562e-02]
Sparsity at: 0.0
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9974 - val_loss: 0.1655 - val_accuracy: 0.9699
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.26023233e-01
 -1.90008566e-01  4.00779694e-02]
Sparsity at: 0.0
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0099 - accuracy: 0.9965 - val_loss: 0.1449 - val_accuracy: 0.9732
[-0.06162945  0.01141503 -0.00061712 ... -0.6134141  -0.20012322
  0.03351247]
Sparsity at: 0.0
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 0.9996 - val_loss: 0.1463 - val_accuracy: 0.9754
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.30596995e-01
 -1.92892626e-01  4.25914526e-02]
Sparsity at: 0.0
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 8.8911e-04 - accuracy: 0.9999 - val_loss: 0.1415 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.25612915e-01
 -1.93727970e-01  4.42183465e-02]
Sparsity at: 0.0
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.12711033645731717
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.20644520067197902
Thresholhold -0.32372161746025085
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.42589658486443227
Thresholhold -0.12965960800647736
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 49s 7ms/step - loss: 4.0956e-04 - accuracy: 1.0000 - val_loss: 0.1412 - val_accuracy: 0.9764
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.25857770e-01
 -1.95057571e-01  4.21078727e-02]
Sparsity at: 0.0
Epoch 52/500
235/235 [==============================] - 2s 7ms/step - loss: 2.4635e-04 - accuracy: 1.0000 - val_loss: 0.1416 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.26612961e-01
 -1.95202604e-01  4.08315845e-02]
Sparsity at: 0.0
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0437e-04 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.27985895e-01
 -1.95445284e-01  3.99722308e-02]
Sparsity at: 0.0
Epoch 54/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7960e-04 - accuracy: 1.0000 - val_loss: 0.1424 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.29442573e-01
 -1.95800722e-01  3.92398424e-02]
Sparsity at: 0.0
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6111e-04 - accuracy: 1.0000 - val_loss: 0.1429 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.30954683e-01
 -1.96197331e-01  3.86395603e-02]
Sparsity at: 0.0
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4591e-04 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.32520378e-01
 -1.96601465e-01  3.81386764e-02]
Sparsity at: 0.0
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3296e-04 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.34155512e-01
 -1.97023571e-01  3.76701392e-02]
Sparsity at: 0.0
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2165e-04 - accuracy: 1.0000 - val_loss: 0.1447 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.35838509e-01
 -1.97454825e-01  3.72747742e-02]
Sparsity at: 0.0
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1156e-04 - accuracy: 1.0000 - val_loss: 0.1453 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.37630582e-01
 -1.97915986e-01  3.69126461e-02]
Sparsity at: 0.0
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0234e-04 - accuracy: 1.0000 - val_loss: 0.1460 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.39488399e-01
 -1.98367834e-01  3.65847126e-02]
Sparsity at: 0.0
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 9.4091e-05 - accuracy: 1.0000 - val_loss: 0.1468 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.41410351e-01
 -1.98837072e-01  3.62924114e-02]
Sparsity at: 0.0
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 8.6385e-05 - accuracy: 1.0000 - val_loss: 0.1476 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.43404365e-01
 -1.99307263e-01  3.60989273e-02]
Sparsity at: 0.0
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9399e-05 - accuracy: 1.0000 - val_loss: 0.1483 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.45496964e-01
 -1.99821174e-01  3.59319001e-02]
Sparsity at: 0.0
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2917e-05 - accuracy: 1.0000 - val_loss: 0.1492 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.47642672e-01
 -2.00294822e-01  3.57794166e-02]
Sparsity at: 0.0
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6827e-05 - accuracy: 1.0000 - val_loss: 0.1501 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.49870813e-01
 -2.00779572e-01  3.56225558e-02]
Sparsity at: 0.0
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1336e-05 - accuracy: 1.0000 - val_loss: 0.1510 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.52199149e-01
 -2.01286897e-01  3.55644450e-02]
Sparsity at: 0.0
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6086e-05 - accuracy: 1.0000 - val_loss: 0.1520 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.54571235e-01
 -2.01773405e-01  3.54774818e-02]
Sparsity at: 0.0
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1319e-05 - accuracy: 1.0000 - val_loss: 0.1530 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.57033682e-01
 -2.02323779e-01  3.54717933e-02]
Sparsity at: 0.0
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6801e-05 - accuracy: 1.0000 - val_loss: 0.1540 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.59595847e-01
 -2.02833235e-01  3.54407355e-02]
Sparsity at: 0.0
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2671e-05 - accuracy: 1.0000 - val_loss: 0.1551 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.62196040e-01
 -2.03397498e-01  3.54460850e-02]
Sparsity at: 0.0
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8902e-05 - accuracy: 1.0000 - val_loss: 0.1563 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.64930940e-01
 -2.03952521e-01  3.54560837e-02]
Sparsity at: 0.0
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5332e-05 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.67739511e-01
 -2.04535782e-01  3.54936197e-02]
Sparsity at: 0.0
Epoch 73/500
235/235 [==============================] - 2s 7ms/step - loss: 3.2029e-05 - accuracy: 1.0000 - val_loss: 0.1586 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.70611739e-01
 -2.05119058e-01  3.55706848e-02]
Sparsity at: 0.0
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9069e-05 - accuracy: 1.0000 - val_loss: 0.1599 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.73581064e-01
 -2.05759287e-01  3.56531031e-02]
Sparsity at: 0.0
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6289e-05 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9772
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.76608860e-01
 -2.06385598e-01  3.57374586e-02]
Sparsity at: 0.0
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3732e-05 - accuracy: 1.0000 - val_loss: 0.1624 - val_accuracy: 0.9773
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.79711461e-01
 -2.07035825e-01  3.58657874e-02]
Sparsity at: 0.0
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1416e-05 - accuracy: 1.0000 - val_loss: 0.1638 - val_accuracy: 0.9774
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.82892621e-01
 -2.07658365e-01  3.59435454e-02]
Sparsity at: 0.0
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9277e-05 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9773
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.86146915e-01
 -2.08323061e-01  3.61156762e-02]
Sparsity at: 0.0
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7339e-05 - accuracy: 1.0000 - val_loss: 0.1664 - val_accuracy: 0.9771
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.89458787e-01
 -2.08996817e-01  3.62223499e-02]
Sparsity at: 0.0
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5588e-05 - accuracy: 1.0000 - val_loss: 0.1679 - val_accuracy: 0.9771
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.92806542e-01
 -2.09692389e-01  3.63398939e-02]
Sparsity at: 0.0
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3978e-05 - accuracy: 1.0000 - val_loss: 0.1693 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.96232140e-01
 -2.10378379e-01  3.64778563e-02]
Sparsity at: 0.0
Epoch 82/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2512e-05 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -6.99711800e-01
 -2.11038306e-01  3.65896411e-02]
Sparsity at: 0.0
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1200e-05 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.03220665e-01
 -2.11757019e-01  3.67024280e-02]
Sparsity at: 0.0
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 9.9987e-06 - accuracy: 1.0000 - val_loss: 0.1735 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.06746817e-01
 -2.12458625e-01  3.68241109e-02]
Sparsity at: 0.0
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 8.9228e-06 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.10331440e-01
 -2.13185370e-01  3.69695798e-02]
Sparsity at: 0.0
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9703e-06 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.13927746e-01
 -2.13878572e-01  3.71495970e-02]
Sparsity at: 0.0
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 7.0984e-06 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.17498720e-01
 -2.14588717e-01  3.72266546e-02]
Sparsity at: 0.0
Epoch 88/500
235/235 [==============================] - 2s 9ms/step - loss: 6.3176e-06 - accuracy: 1.0000 - val_loss: 0.1796 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.21130133e-01
 -2.15345189e-01  3.73477787e-02]
Sparsity at: 0.0
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6260e-06 - accuracy: 1.0000 - val_loss: 0.1810 - val_accuracy: 0.9771
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.24777043e-01
 -2.16045722e-01  3.74784730e-02]
Sparsity at: 0.0
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9992e-06 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.28432059e-01
 -2.16751575e-01  3.75722237e-02]
Sparsity at: 0.0
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4432e-06 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.32082605e-01
 -2.17433602e-01  3.76952626e-02]
Sparsity at: 0.0
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9461e-06 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.35769510e-01
 -2.18151763e-01  3.77665460e-02]
Sparsity at: 0.0
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5066e-06 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9771
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.39442170e-01
 -2.18845710e-01  3.78184691e-02]
Sparsity at: 0.0
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1093e-06 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.43112743e-01
 -2.19543487e-01  3.78916115e-02]
Sparsity at: 0.0
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7563e-06 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9770
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.46812046e-01
 -2.20248595e-01  3.80217545e-02]
Sparsity at: 0.0
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4470e-06 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.50449181e-01
 -2.20953107e-01  3.80829386e-02]
Sparsity at: 0.0
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1697e-06 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.54138470e-01
 -2.21677557e-01  3.81468795e-02]
Sparsity at: 0.0
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9263e-06 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.57808089e-01
 -2.22348854e-01  3.81894335e-02]
Sparsity at: 0.0
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7083e-06 - accuracy: 1.0000 - val_loss: 0.1967 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.61428177e-01
 -2.23013178e-01  3.82183827e-02]
Sparsity at: 0.0
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5145e-06 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.65046656e-01
 -2.23710507e-01  3.82093713e-02]
Sparsity at: 0.0
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.1786200046124975
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.27929918435745904
Thresholhold -0.3375190496444702
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.6444302967839235
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 1.3421e-06 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.68685102e-01
 -2.24385381e-01  3.82571928e-02]
Sparsity at: 0.0
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 1.1916e-06 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.72287667e-01
 -2.25008085e-01  3.82519439e-02]
Sparsity at: 0.0
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0551e-06 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.75840223e-01
 -2.25661933e-01  3.82906348e-02]
Sparsity at: 0.0
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3854e-07 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.79343963e-01
 -2.26262301e-01  3.83147486e-02]
Sparsity at: 0.0
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3317e-07 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.82865644e-01
 -2.26891458e-01  3.83352414e-02]
Sparsity at: 0.0
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 7.4032e-07 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.86381841e-01
 -2.27530435e-01  3.84084545e-02]
Sparsity at: 0.0
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5754e-07 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.89868712e-01
 -2.28118062e-01  3.84691805e-02]
Sparsity at: 0.0
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8472e-07 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.93344796e-01
 -2.28746548e-01  3.84795889e-02]
Sparsity at: 0.0
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2085e-07 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -7.96737015e-01
 -2.29373425e-01  3.84733044e-02]
Sparsity at: 0.0
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6456e-07 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.00145805e-01
 -2.29992673e-01  3.85238752e-02]
Sparsity at: 0.0
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1363e-07 - accuracy: 1.0000 - val_loss: 0.2150 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.03495705e-01
 -2.30574384e-01  3.85139883e-02]
Sparsity at: 0.0
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6902e-07 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.06790948e-01
 -2.31189400e-01  3.84893939e-02]
Sparsity at: 0.0
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2997e-07 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.10080588e-01
 -2.31762692e-01  3.85242626e-02]
Sparsity at: 0.0
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9508e-07 - accuracy: 1.0000 - val_loss: 0.2192 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.13330531e-01
 -2.32369795e-01  3.85279506e-02]
Sparsity at: 0.0
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6406e-07 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.16471279e-01
 -2.32887834e-01  3.85581776e-02]
Sparsity at: 0.0
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3699e-07 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.19550395e-01
 -2.33468518e-01  3.85265723e-02]
Sparsity at: 0.0
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1256e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9765
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.22639108e-01
 -2.34005883e-01  3.85492742e-02]
Sparsity at: 0.0
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9134e-07 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.25674713e-01
 -2.34564021e-01  3.85700241e-02]
Sparsity at: 0.0
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7225e-07 - accuracy: 1.0000 - val_loss: 0.2260 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.28621328e-01
 -2.35114768e-01  3.85724232e-02]
Sparsity at: 0.0
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5548e-07 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.31497133e-01
 -2.35651076e-01  3.86031568e-02]
Sparsity at: 0.0
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4068e-07 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.34326982e-01
 -2.36192048e-01  3.86074074e-02]
Sparsity at: 0.0
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2730e-07 - accuracy: 1.0000 - val_loss: 0.2295 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.37076187e-01
 -2.36699954e-01  3.86157744e-02]
Sparsity at: 0.0
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1544e-07 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.39757979e-01
 -2.37209916e-01  3.86303775e-02]
Sparsity at: 0.0
Epoch 124/500
235/235 [==============================] - 2s 7ms/step - loss: 1.0505e-07 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.42364609e-01
 -2.37701640e-01  3.86427678e-02]
Sparsity at: 0.0
Epoch 125/500
235/235 [==============================] - 2s 7ms/step - loss: 9.6011e-08 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.44921470e-01
 -2.38213971e-01  3.86302695e-02]
Sparsity at: 0.0
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 8.7577e-08 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.47416461e-01
 -2.38721207e-01  3.86386663e-02]
Sparsity at: 0.0
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 8.0188e-08 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.49864542e-01
 -2.39186317e-01  3.86222452e-02]
Sparsity at: 0.0
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3634e-08 - accuracy: 1.0000 - val_loss: 0.2361 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.52162004e-01
 -2.39656612e-01  3.86305675e-02]
Sparsity at: 0.0
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7633e-08 - accuracy: 1.0000 - val_loss: 0.2369 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.54442239e-01
 -2.40120471e-01  3.85817252e-02]
Sparsity at: 0.0
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2412e-08 - accuracy: 1.0000 - val_loss: 0.2379 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.56645644e-01
 -2.40544215e-01  3.85874137e-02]
Sparsity at: 0.0
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7606e-08 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.58747959e-01
 -2.40945205e-01  3.85392494e-02]
Sparsity at: 0.0
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3308e-08 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.60825837e-01
 -2.41367340e-01  3.85020450e-02]
Sparsity at: 0.0
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9432e-08 - accuracy: 1.0000 - val_loss: 0.2404 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.62779140e-01
 -2.41723835e-01  3.84941548e-02]
Sparsity at: 0.0
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6128e-08 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.64676595e-01
 -2.42143527e-01  3.84639129e-02]
Sparsity at: 0.0
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3001e-08 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.66502225e-01
 -2.42497161e-01  3.84116545e-02]
Sparsity at: 0.0
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0241e-08 - accuracy: 1.0000 - val_loss: 0.2426 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.68249118e-01
 -2.42863253e-01  3.83799672e-02]
Sparsity at: 0.0
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7654e-08 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.69973421e-01
 -2.43236393e-01  3.83756831e-02]
Sparsity at: 0.0
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5357e-08 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.71574163e-01
 -2.43592024e-01  3.83719280e-02]
Sparsity at: 0.0
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3249e-08 - accuracy: 1.0000 - val_loss: 0.2447 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.73110533e-01
 -2.43911177e-01  3.83442529e-02]
Sparsity at: 0.0
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1348e-08 - accuracy: 1.0000 - val_loss: 0.2453 - val_accuracy: 0.9765
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.74634564e-01
 -2.44285271e-01  3.83453779e-02]
Sparsity at: 0.0
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9616e-08 - accuracy: 1.0000 - val_loss: 0.2459 - val_accuracy: 0.9765
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.76099169e-01
 -2.44626507e-01  3.83300520e-02]
Sparsity at: 0.0
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8008e-08 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.77531469e-01
 -2.44956121e-01  3.83266397e-02]
Sparsity at: 0.0
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6524e-08 - accuracy: 1.0000 - val_loss: 0.2470 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.78861487e-01
 -2.45257944e-01  3.82626876e-02]
Sparsity at: 0.0
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5177e-08 - accuracy: 1.0000 - val_loss: 0.2475 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.80145013e-01
 -2.45569840e-01  3.82449813e-02]
Sparsity at: 0.0
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4031e-08 - accuracy: 1.0000 - val_loss: 0.2480 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.81384790e-01
 -2.45836064e-01  3.81923653e-02]
Sparsity at: 0.0
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2858e-08 - accuracy: 1.0000 - val_loss: 0.2485 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.82537663e-01
 -2.46114358e-01  3.81703116e-02]
Sparsity at: 0.0
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1793e-08 - accuracy: 1.0000 - val_loss: 0.2490 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.83661330e-01
 -2.46371999e-01  3.81385796e-02]
Sparsity at: 0.0
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0792e-08 - accuracy: 1.0000 - val_loss: 0.2493 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.84719789e-01
 -2.46628493e-01  3.81027535e-02]
Sparsity at: 0.0
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9936e-08 - accuracy: 1.0000 - val_loss: 0.2498 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.85771871e-01
 -2.46893898e-01  3.80806401e-02]
Sparsity at: 0.0
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9095e-08 - accuracy: 1.0000 - val_loss: 0.2502 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.86786222e-01
 -2.47147068e-01  3.80780213e-02]
Sparsity at: 0.0
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.23349668905418675
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.3637283748039408
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.8807889858131297
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 47s 7ms/step - loss: 1.8344e-08 - accuracy: 1.0000 - val_loss: 0.2506 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.87766242e-01
 -2.47375876e-01  3.80660407e-02]
Sparsity at: 0.0
Epoch 152/500
235/235 [==============================] - 2s 7ms/step - loss: 1.7645e-08 - accuracy: 1.0000 - val_loss: 0.2511 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.88698697e-01
 -2.47616738e-01  3.80187072e-02]
Sparsity at: 0.0
Epoch 153/500
235/235 [==============================] - 2s 10ms/step - loss: 1.6956e-08 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.89619648e-01
 -2.47834235e-01  3.79927456e-02]
Sparsity at: 0.0
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6365e-08 - accuracy: 1.0000 - val_loss: 0.2518 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.90494585e-01
 -2.48063758e-01  3.79710905e-02]
Sparsity at: 0.0
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5765e-08 - accuracy: 1.0000 - val_loss: 0.2522 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.91367853e-01
 -2.48332337e-01  3.79396603e-02]
Sparsity at: 0.0
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5142e-08 - accuracy: 1.0000 - val_loss: 0.2525 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.92202854e-01
 -2.48555094e-01  3.78836878e-02]
Sparsity at: 0.0
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4647e-08 - accuracy: 1.0000 - val_loss: 0.2529 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.93022776e-01
 -2.48802245e-01  3.78507935e-02]
Sparsity at: 0.0
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4212e-08 - accuracy: 1.0000 - val_loss: 0.2532 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.93809974e-01
 -2.49039039e-01  3.77945639e-02]
Sparsity at: 0.0
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3723e-08 - accuracy: 1.0000 - val_loss: 0.2536 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.94581199e-01
 -2.49277666e-01  3.77601832e-02]
Sparsity at: 0.0
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3314e-08 - accuracy: 1.0000 - val_loss: 0.2539 - val_accuracy: 0.9769
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.95328820e-01
 -2.49494717e-01  3.77357081e-02]
Sparsity at: 0.0
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2892e-08 - accuracy: 1.0000 - val_loss: 0.2541 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.96038830e-01
 -2.49718621e-01  3.77015471e-02]
Sparsity at: 0.0
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2547e-08 - accuracy: 1.0000 - val_loss: 0.2544 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.96735668e-01
 -2.49904469e-01  3.77042815e-02]
Sparsity at: 0.0
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2157e-08 - accuracy: 1.0000 - val_loss: 0.2547 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.97418916e-01
 -2.50112623e-01  3.76848988e-02]
Sparsity at: 0.0
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1772e-08 - accuracy: 1.0000 - val_loss: 0.2550 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.98071826e-01
 -2.50316799e-01  3.76756787e-02]
Sparsity at: 0.0
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1458e-08 - accuracy: 1.0000 - val_loss: 0.2553 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.98714602e-01
 -2.50518382e-01  3.76418270e-02]
Sparsity at: 0.0
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1188e-08 - accuracy: 1.0000 - val_loss: 0.2555 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.99335206e-01
 -2.50679493e-01  3.75848711e-02]
Sparsity at: 0.0
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0933e-08 - accuracy: 1.0000 - val_loss: 0.2558 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -8.99972856e-01
 -2.50870347e-01  3.75116169e-02]
Sparsity at: 0.0
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0620e-08 - accuracy: 1.0000 - val_loss: 0.2561 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.00587142e-01
 -2.51068354e-01  3.74415889e-02]
Sparsity at: 0.0
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0463e-08 - accuracy: 1.0000 - val_loss: 0.2563 - val_accuracy: 0.9768
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.01199877e-01
 -2.51278073e-01  3.73902954e-02]
Sparsity at: 0.0
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0184e-08 - accuracy: 1.0000 - val_loss: 0.2566 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.01780903e-01
 -2.51471400e-01  3.73461396e-02]
Sparsity at: 0.0
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 9.9778e-09 - accuracy: 1.0000 - val_loss: 0.2568 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.02333498e-01
 -2.51660168e-01  3.72826569e-02]
Sparsity at: 0.0
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7255e-09 - accuracy: 1.0000 - val_loss: 0.2570 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.02888894e-01
 -2.51870662e-01  3.72395217e-02]
Sparsity at: 0.0
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5407e-09 - accuracy: 1.0000 - val_loss: 0.2573 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.03409481e-01
 -2.52093822e-01  3.71811017e-02]
Sparsity at: 0.0
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 9.2924e-09 - accuracy: 1.0000 - val_loss: 0.2575 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.03915823e-01
 -2.52305686e-01  3.71289700e-02]
Sparsity at: 0.0
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0897e-09 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.04441357e-01
 -2.52519518e-01  3.70614640e-02]
Sparsity at: 0.0
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 8.8771e-09 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.04922366e-01
 -2.52677858e-01  3.70196067e-02]
Sparsity at: 0.0
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 8.6844e-09 - accuracy: 1.0000 - val_loss: 0.2582 - val_accuracy: 0.9767
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.05424118e-01
 -2.52865523e-01  3.69873196e-02]
Sparsity at: 0.0
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 8.4956e-09 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.05921340e-01
 -2.53046930e-01  3.69356796e-02]
Sparsity at: 0.0
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2354e-09 - accuracy: 1.0000 - val_loss: 0.2587 - val_accuracy: 0.9765
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.06389952e-01
 -2.53208697e-01  3.69024053e-02]
Sparsity at: 0.0
Epoch 180/500
235/235 [==============================] - 2s 8ms/step - loss: 8.1420e-09 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 0.9766
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.06857669e-01
 -2.53375292e-01  3.68436314e-02]
Sparsity at: 0.0
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9691e-09 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9765
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.07332718e-01
 -2.53554881e-01  3.67706679e-02]
Sparsity at: 0.0
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7724e-09 - accuracy: 1.0000 - val_loss: 0.2593 - val_accuracy: 0.9765
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.07797277e-01
 -2.53728628e-01  3.66903357e-02]
Sparsity at: 0.0
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 7.6512e-09 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.08234000e-01
 -2.53887951e-01  3.66353840e-02]
Sparsity at: 0.0
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 7.4546e-09 - accuracy: 1.0000 - val_loss: 0.2596 - val_accuracy: 0.9764
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.08669353e-01
 -2.54042059e-01  3.65685709e-02]
Sparsity at: 0.0
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3493e-09 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.09079969e-01
 -2.54154831e-01  3.65021378e-02]
Sparsity at: 0.0
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 7.1863e-09 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9764
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.09473240e-01
 -2.54305243e-01  3.64245661e-02]
Sparsity at: 0.0
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0492e-09 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9764
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.09852743e-01
 -2.54437536e-01  3.63791436e-02]
Sparsity at: 0.0
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 6.9420e-09 - accuracy: 1.0000 - val_loss: 0.2603 - val_accuracy: 0.9762
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.10262227e-01
 -2.54620194e-01  3.62895429e-02]
Sparsity at: 0.0
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8208e-09 - accuracy: 1.0000 - val_loss: 0.2605 - val_accuracy: 0.9762
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.10653830e-01
 -2.54777402e-01  3.62312198e-02]
Sparsity at: 0.0
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7552e-09 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.9764
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.11043406e-01
 -2.54913747e-01  3.61530446e-02]
Sparsity at: 0.0
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6121e-09 - accuracy: 1.0000 - val_loss: 0.2608 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.11433756e-01
 -2.55091101e-01  3.60769965e-02]
Sparsity at: 0.0
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 6.4870e-09 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.11791921e-01
 -2.55247861e-01  3.59976143e-02]
Sparsity at: 0.0
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 6.3837e-09 - accuracy: 1.0000 - val_loss: 0.2611 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.12145734e-01
 -2.55401284e-01  3.59476320e-02]
Sparsity at: 0.0
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 6.3161e-09 - accuracy: 1.0000 - val_loss: 0.2613 - val_accuracy: 0.9762
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.12492037e-01
 -2.55557775e-01  3.58512141e-02]
Sparsity at: 0.0
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1949e-09 - accuracy: 1.0000 - val_loss: 0.2614 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.12839890e-01
 -2.55712658e-01  3.57962959e-02]
Sparsity at: 0.0
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0856e-09 - accuracy: 1.0000 - val_loss: 0.2615 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.13161993e-01
 -2.55864739e-01  3.57208997e-02]
Sparsity at: 0.0
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0320e-09 - accuracy: 1.0000 - val_loss: 0.2617 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.13472593e-01
 -2.56018966e-01  3.56645025e-02]
Sparsity at: 0.0
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9187e-09 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9762
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.13775861e-01
 -2.56156266e-01  3.56170535e-02]
Sparsity at: 0.0
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8591e-09 - accuracy: 1.0000 - val_loss: 0.2620 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.14086223e-01
 -2.56304771e-01  3.55563872e-02]
Sparsity at: 0.0
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7578e-09 - accuracy: 1.0000 - val_loss: 0.2621 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.14390981e-01
 -2.56449252e-01  3.55049074e-02]
Sparsity at: 0.0
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.29015515959103055
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.4289406053256215
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 1.0377554696051163
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 47s 7ms/step - loss: 5.6704e-09 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.14671004e-01
 -2.56605625e-01  3.54711302e-02]
Sparsity at: 0.0
Epoch 202/500
235/235 [==============================] - 2s 7ms/step - loss: 5.5869e-09 - accuracy: 1.0000 - val_loss: 0.2625 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.14955258e-01
 -2.56763905e-01  3.54210809e-02]
Sparsity at: 0.0
Epoch 203/500
235/235 [==============================] - 2s 7ms/step - loss: 5.5253e-09 - accuracy: 1.0000 - val_loss: 0.2626 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.15228128e-01
 -2.56920069e-01  3.53657976e-02]
Sparsity at: 0.0
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 5.4359e-09 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.15491402e-01
 -2.57055789e-01  3.53299007e-02]
Sparsity at: 0.0
Epoch 205/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3267e-09 - accuracy: 1.0000 - val_loss: 0.2628 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.15749371e-01
 -2.57224709e-01  3.53000872e-02]
Sparsity at: 0.0
Epoch 206/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2551e-09 - accuracy: 1.0000 - val_loss: 0.2629 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.15998399e-01
 -2.57363349e-01  3.52587216e-02]
Sparsity at: 0.0
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1975e-09 - accuracy: 1.0000 - val_loss: 0.2631 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.16250169e-01
 -2.57522374e-01  3.51937748e-02]
Sparsity at: 0.0
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 5.1081e-09 - accuracy: 1.0000 - val_loss: 0.2632 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.16496754e-01
 -2.57682413e-01  3.51673961e-02]
Sparsity at: 0.0
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0048e-09 - accuracy: 1.0000 - val_loss: 0.2634 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.16747510e-01
 -2.57848680e-01  3.51176858e-02]
Sparsity at: 0.0
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0108e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.16990101e-01
 -2.58017093e-01  3.50633748e-02]
Sparsity at: 0.0
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8757e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.17223513e-01
 -2.58166850e-01  3.50154191e-02]
Sparsity at: 0.0
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8657e-09 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.17463958e-01
 -2.58360386e-01  3.49781290e-02]
Sparsity at: 0.0
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8161e-09 - accuracy: 1.0000 - val_loss: 0.2638 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.17696536e-01
 -2.58528113e-01  3.49233560e-02]
Sparsity at: 0.0
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7664e-09 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.17910755e-01
 -2.58705020e-01  3.48703973e-02]
Sparsity at: 0.0
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6233e-09 - accuracy: 1.0000 - val_loss: 0.2640 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.18130279e-01
 -2.58882880e-01  3.48255001e-02]
Sparsity at: 0.0
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6313e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.18362021e-01
 -2.59070545e-01  3.47708426e-02]
Sparsity at: 0.0
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5876e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.18572187e-01
 -2.59242654e-01  3.47032584e-02]
Sparsity at: 0.0
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4942e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.18786049e-01
 -2.59433955e-01  3.46616656e-02]
Sparsity at: 0.0
Epoch 219/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4545e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.18996572e-01
 -2.59616017e-01  3.46292928e-02]
Sparsity at: 0.0
Epoch 220/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4505e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.19211626e-01
 -2.59799838e-01  3.45857665e-02]
Sparsity at: 0.0
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3690e-09 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.19399023e-01
 -2.59985745e-01  3.45336236e-02]
Sparsity at: 0.0
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3313e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.19601619e-01
 -2.60172993e-01  3.44541743e-02]
Sparsity at: 0.0
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2836e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.19802368e-01
 -2.60383666e-01  3.44018005e-02]
Sparsity at: 0.0
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2339e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.20005023e-01
 -2.60588288e-01  3.43582146e-02]
Sparsity at: 0.0
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2121e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.20192719e-01
 -2.60786414e-01  3.43149863e-02]
Sparsity at: 0.0
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1743e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.20379937e-01
 -2.60970563e-01  3.42692733e-02]
Sparsity at: 0.0
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1624e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.20589268e-01
 -2.61175066e-01  3.42335515e-02]
Sparsity at: 0.0
Epoch 228/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0551e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9762
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.20776010e-01
 -2.61332154e-01  3.41747925e-02]
Sparsity at: 0.0
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0650e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9762
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.20961797e-01
 -2.61539131e-01  3.41474488e-02]
Sparsity at: 0.0
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9955e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.21143115e-01
 -2.61735678e-01  3.40811573e-02]
Sparsity at: 0.0
Epoch 231/500
235/235 [==============================] - 2s 9ms/step - loss: 3.9538e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9762
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.21331465e-01
 -2.61911452e-01  3.40303034e-02]
Sparsity at: 0.0
Epoch 232/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9518e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9763
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.21515286e-01
 -2.62104690e-01  3.39871645e-02]
Sparsity at: 0.0
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9061e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.21713591e-01
 -2.62274295e-01  3.39459814e-02]
Sparsity at: 0.0
Epoch 234/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8127e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.21899974e-01
 -2.62462616e-01  3.39102410e-02]
Sparsity at: 0.0
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8127e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.22066033e-01
 -2.62648851e-01  3.38765308e-02]
Sparsity at: 0.0
Epoch 236/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7710e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.22255576e-01
 -2.62851536e-01  3.38436365e-02]
Sparsity at: 0.0
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7412e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.22424376e-01
 -2.63003439e-01  3.38062197e-02]
Sparsity at: 0.0
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7193e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.22604740e-01
 -2.63177961e-01  3.37628834e-02]
Sparsity at: 0.0
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6418e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.22775328e-01
 -2.63387471e-01  3.37248072e-02]
Sparsity at: 0.0
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6498e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9761
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.22941923e-01
 -2.63570428e-01  3.36993597e-02]
Sparsity at: 0.0
Epoch 241/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6120e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.23106253e-01
 -2.63746798e-01  3.36866267e-02]
Sparsity at: 0.0
Epoch 242/500
235/235 [==============================] - 2s 7ms/step - loss: 3.6061e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.23285961e-01
 -2.63909280e-01  3.36660109e-02]
Sparsity at: 0.0
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5524e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.23451483e-01
 -2.64106601e-01  3.36506292e-02]
Sparsity at: 0.0
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5147e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.23603773e-01
 -2.64274925e-01  3.36309187e-02]
Sparsity at: 0.0
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4928e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.23749089e-01
 -2.64473379e-01  3.36190313e-02]
Sparsity at: 0.0
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4591e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.23919141e-01
 -2.64666617e-01  3.36114913e-02]
Sparsity at: 0.0
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4432e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.24050450e-01
 -2.64836729e-01  3.35773490e-02]
Sparsity at: 0.0
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4114e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.24211681e-01
 -2.65033036e-01  3.35514136e-02]
Sparsity at: 0.0
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3696e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.24376667e-01
 -2.65216380e-01  3.35313305e-02]
Sparsity at: 0.0
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3736e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.24528062e-01
 -2.65405357e-01  3.35179195e-02]
Sparsity at: 0.0
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.3503402787513785
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.4885466332617341
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 1.1681934371301281
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 3.3458e-09 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.24670041e-01
 -2.65589476e-01  3.35141756e-02]
Sparsity at: 0.0
Epoch 252/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3120e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.24823880e-01
 -2.65767068e-01  3.34855989e-02]
Sparsity at: 0.0
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2763e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.24977839e-01
 -2.65944660e-01  3.34512144e-02]
Sparsity at: 0.0
Epoch 254/500
235/235 [==============================] - 2s 10ms/step - loss: 3.2226e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.25129414e-01
 -2.66131401e-01  3.34366970e-02]
Sparsity at: 0.0
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2524e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.25261557e-01
 -2.66307682e-01  3.34103853e-02]
Sparsity at: 0.0
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2167e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.25418079e-01
 -2.66495347e-01  3.33547890e-02]
Sparsity at: 0.0
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1273e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.25549388e-01
 -2.66678303e-01  3.33302841e-02]
Sparsity at: 0.0
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2187e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.25707161e-01
 -2.66844451e-01  3.32996286e-02]
Sparsity at: 0.0
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1392e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.25861955e-01
 -2.67017305e-01  3.32547203e-02]
Sparsity at: 0.0
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1710e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.26003277e-01
 -2.67192781e-01  3.32199000e-02]
Sparsity at: 0.0
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0637e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.26138043e-01
 -2.67359793e-01  3.31771597e-02]
Sparsity at: 0.0
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0816e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.26255643e-01
 -2.67516524e-01  3.31262834e-02]
Sparsity at: 0.0
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0716e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.26396668e-01
 -2.67682999e-01  3.30832936e-02]
Sparsity at: 0.0
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0418e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.26541388e-01
 -2.67832726e-01  3.30551378e-02]
Sparsity at: 0.0
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0359e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.26660895e-01
 -2.68008381e-01  3.30281854e-02]
Sparsity at: 0.0
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9882e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.26809251e-01
 -2.68177360e-01  3.29838581e-02]
Sparsity at: 0.0
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0080e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.26921129e-01
 -2.68325478e-01  3.29505801e-02]
Sparsity at: 0.0
Epoch 268/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9782e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.27057743e-01
 -2.68476188e-01  3.29090133e-02]
Sparsity at: 0.0
Epoch 269/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9624e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.27197754e-01
 -2.68637538e-01  3.28612365e-02]
Sparsity at: 0.0
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9325e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.27317798e-01
 -2.68785268e-01  3.28159109e-02]
Sparsity at: 0.0
Epoch 271/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9345e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.27437484e-01
 -2.68936992e-01  3.27634439e-02]
Sparsity at: 0.0
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.27565992e-01
 -2.69120842e-01  3.27210948e-02]
Sparsity at: 0.0
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8690e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.27707613e-01
 -2.69284785e-01  3.26653272e-02]
Sparsity at: 0.0
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8570e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.27833319e-01
 -2.69439399e-01  3.26155275e-02]
Sparsity at: 0.0
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.27961588e-01
 -2.69596279e-01  3.25694568e-02]
Sparsity at: 0.0
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8491e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.28091466e-01
 -2.69764632e-01  3.25070955e-02]
Sparsity at: 0.0
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8014e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.28217173e-01
 -2.69907743e-01  3.24538723e-02]
Sparsity at: 0.0
Epoch 278/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8451e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.28338230e-01
 -2.70053893e-01  3.24195884e-02]
Sparsity at: 0.0
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7398e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.28449988e-01
 -2.70204633e-01  3.23816091e-02]
Sparsity at: 0.0
Epoch 280/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.28579807e-01
 -2.70364761e-01  3.23268808e-02]
Sparsity at: 0.0
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7716e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.28684056e-01
 -2.70496249e-01  3.22792418e-02]
Sparsity at: 0.0
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7676e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.28806484e-01
 -2.70665675e-01  3.22445706e-02]
Sparsity at: 0.0
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.28937733e-01
 -2.70820051e-01  3.21911611e-02]
Sparsity at: 0.0
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7676e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29046094e-01
 -2.70958185e-01  3.21371406e-02]
Sparsity at: 0.0
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6862e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29148197e-01
 -2.71110624e-01  3.20917107e-02]
Sparsity at: 0.0
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29271698e-01
 -2.71261036e-01  3.20300087e-02]
Sparsity at: 0.0
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6882e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29389060e-01
 -2.71424919e-01  3.19824405e-02]
Sparsity at: 0.0
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6902e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29484844e-01
 -2.71579772e-01  3.19306254e-02]
Sparsity at: 0.0
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6921e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29609537e-01
 -2.71734685e-01  3.18796709e-02]
Sparsity at: 0.0
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6902e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29724455e-01
 -2.71894217e-01  3.18323933e-02]
Sparsity at: 0.0
Epoch 291/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6226e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29827809e-01
 -2.72025287e-01  3.17711011e-02]
Sparsity at: 0.0
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6941e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.29921627e-01
 -2.72164434e-01  3.17262337e-02]
Sparsity at: 0.0
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6584e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30057406e-01
 -2.72311896e-01  3.16710137e-02]
Sparsity at: 0.0
Epoch 294/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5690e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30153012e-01
 -2.72473395e-01  3.16102467e-02]
Sparsity at: 0.0
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6107e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30259347e-01
 -2.72633404e-01  3.15474123e-02]
Sparsity at: 0.0
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5888e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30386722e-01
 -2.72813857e-01  3.14740911e-02]
Sparsity at: 0.0
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6186e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30484474e-01
 -2.72970378e-01  3.14183012e-02]
Sparsity at: 0.0
Epoch 298/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6286e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30597365e-01
 -2.73115486e-01  3.13339047e-02]
Sparsity at: 0.0
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6067e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30727839e-01
 -2.73280054e-01  3.12782265e-02]
Sparsity at: 0.0
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5471e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30838823e-01
 -2.73430228e-01  3.12118568e-02]
Sparsity at: 0.0
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.42071751571042526
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.5465529025184779
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 1.32168250607576
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 42s 7ms/step - loss: 2.5888e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.30943191e-01
 -2.73569554e-01  3.11563313e-02]
Sparsity at: 0.0
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 2.5888e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31062758e-01
 -2.73723036e-01  3.10694650e-02]
Sparsity at: 0.0
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5610e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31175709e-01
 -2.73886263e-01  3.09984479e-02]
Sparsity at: 0.0
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5590e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31276441e-01
 -2.74011970e-01  3.09351925e-02]
Sparsity at: 0.0
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5372e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31389153e-01
 -2.74166137e-01  3.08516752e-02]
Sparsity at: 0.0
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5431e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31466818e-01
 -2.74326801e-01  3.07944044e-02]
Sparsity at: 0.0
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4776e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31572795e-01
 -2.74478078e-01  3.06993369e-02]
Sparsity at: 0.0
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5233e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31696892e-01
 -2.74643004e-01  3.06342077e-02]
Sparsity at: 0.0
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5153e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31790948e-01
 -2.74796277e-01  3.05601545e-02]
Sparsity at: 0.0
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5372e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.31907952e-01
 -2.74943173e-01  3.04721408e-02]
Sparsity at: 0.0
Epoch 311/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5570e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32008624e-01
 -2.75076777e-01  3.03975996e-02]
Sparsity at: 0.0
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5113e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32140231e-01
 -2.75252402e-01  3.02885715e-02]
Sparsity at: 0.0
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4935e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32246327e-01
 -2.75404513e-01  3.02186869e-02]
Sparsity at: 0.0
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4498e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32355762e-01
 -2.75569916e-01  3.01008262e-02]
Sparsity at: 0.0
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4875e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32476521e-01
 -2.75717050e-01  3.00080311e-02]
Sparsity at: 0.0
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4478e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32577670e-01
 -2.75901645e-01  2.98965834e-02]
Sparsity at: 0.0
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4001e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32669640e-01
 -2.76065528e-01  2.98235286e-02]
Sparsity at: 0.0
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4994e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32773888e-01
 -2.76246756e-01  2.97185611e-02]
Sparsity at: 0.0
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4557e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.32896674e-01
 -2.76427358e-01  2.96193399e-02]
Sparsity at: 0.0
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4418e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33004141e-01
 -2.76580632e-01  2.95249335e-02]
Sparsity at: 0.0
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4319e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33114290e-01
 -2.76739955e-01  2.94403601e-02]
Sparsity at: 0.0
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4696e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33220446e-01
 -2.76904047e-01  2.93307547e-02]
Sparsity at: 0.0
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4617e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33327258e-01
 -2.77078480e-01  2.92143244e-02]
Sparsity at: 0.0
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3941e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33432817e-01
 -2.77232975e-01  2.91330144e-02]
Sparsity at: 0.0
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33532476e-01
 -2.77407616e-01  2.90375426e-02]
Sparsity at: 0.0
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4438e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33655977e-01
 -2.77554125e-01  2.89466288e-02]
Sparsity at: 0.0
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4219e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33762908e-01
 -2.77727157e-01  2.88372748e-02]
Sparsity at: 0.0
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33875382e-01
 -2.77905524e-01  2.87212264e-02]
Sparsity at: 0.0
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3941e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.33978021e-01
 -2.78071344e-01  2.86270641e-02]
Sparsity at: 0.0
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3901e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34066951e-01
 -2.78246313e-01  2.85144839e-02]
Sparsity at: 0.0
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3743e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34182227e-01
 -2.78432906e-01  2.84102540e-02]
Sparsity at: 0.0
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4100e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34286058e-01
 -2.78578788e-01  2.83053778e-02]
Sparsity at: 0.0
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4021e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34391439e-01
 -2.78786570e-01  2.82143913e-02]
Sparsity at: 0.0
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3723e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34511960e-01
 -2.78952211e-01  2.80919932e-02]
Sparsity at: 0.0
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3862e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34615195e-01
 -2.79102743e-01  2.79988945e-02]
Sparsity at: 0.0
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4080e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34739351e-01
 -2.79313087e-01  2.78884340e-02]
Sparsity at: 0.0
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3206e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34842765e-01
 -2.79505819e-01  2.77688317e-02]
Sparsity at: 0.0
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4021e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.34953094e-01
 -2.79678524e-01  2.76506562e-02]
Sparsity at: 0.0
Epoch 339/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35078919e-01
 -2.79857576e-01  2.75072195e-02]
Sparsity at: 0.0
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35183048e-01
 -2.80020237e-01  2.74019502e-02]
Sparsity at: 0.0
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3444e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35287058e-01
 -2.80185521e-01  2.73041949e-02]
Sparsity at: 0.0
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35399055e-01
 -2.80346304e-01  2.71734055e-02]
Sparsity at: 0.0
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35505390e-01
 -2.80540049e-01  2.70613991e-02]
Sparsity at: 0.0
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35604811e-01
 -2.80752480e-01  2.69550905e-02]
Sparsity at: 0.0
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35702622e-01
 -2.80921936e-01  2.68561095e-02]
Sparsity at: 0.0
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3286e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35807824e-01
 -2.81096786e-01  2.67528109e-02]
Sparsity at: 0.0
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.35918272e-01
 -2.81267792e-01  2.66409628e-02]
Sparsity at: 0.0
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36012983e-01
 -2.81458706e-01  2.65191514e-02]
Sparsity at: 0.0
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3246e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36115980e-01
 -2.81638324e-01  2.63936780e-02]
Sparsity at: 0.0
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36217189e-01
 -2.81829774e-01  2.62851212e-02]
Sparsity at: 0.0
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.4900760404196802
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.5907562922668816
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 1.418425701269939
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 41s 7ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36327040e-01
 -2.82030821e-01  2.61641461e-02]
Sparsity at: 0.0
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 2.3663e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36432660e-01
 -2.82208681e-01  2.60592867e-02]
Sparsity at: 0.0
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3385e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36538637e-01
 -2.82387346e-01  2.59393919e-02]
Sparsity at: 0.0
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3286e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36642170e-01
 -2.82590419e-01  2.58339141e-02]
Sparsity at: 0.0
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36771572e-01
 -2.82791495e-01  2.57414468e-02]
Sparsity at: 0.0
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36893165e-01
 -2.82959610e-01  2.56321710e-02]
Sparsity at: 0.0
Epoch 357/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.36969459e-01
 -2.83139676e-01  2.55147386e-02]
Sparsity at: 0.0
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3186e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37088847e-01
 -2.83327878e-01  2.53991373e-02]
Sparsity at: 0.0
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3107e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37186301e-01
 -2.83514231e-01  2.52768602e-02]
Sparsity at: 0.0
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37287807e-01
 -2.83707619e-01  2.51426268e-02]
Sparsity at: 0.0
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3067e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37413037e-01
 -2.83901155e-01  2.50448585e-02]
Sparsity at: 0.0
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37532008e-01
 -2.84112006e-01  2.49501243e-02]
Sparsity at: 0.0
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3146e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37625945e-01
 -2.84277260e-01  2.48101316e-02]
Sparsity at: 0.0
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3186e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37716067e-01
 -2.84475356e-01  2.47137081e-02]
Sparsity at: 0.0
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2729e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37812865e-01
 -2.84677655e-01  2.45961174e-02]
Sparsity at: 0.0
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.37907994e-01
 -2.84860909e-01  2.44844966e-02]
Sparsity at: 0.0
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3027e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38012064e-01
 -2.85059512e-01  2.43404638e-02]
Sparsity at: 0.0
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2789e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38129425e-01
 -2.85266370e-01  2.42407601e-02]
Sparsity at: 0.0
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38231409e-01
 -2.85465866e-01  2.41196100e-02]
Sparsity at: 0.0
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3047e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38334584e-01
 -2.85666704e-01  2.40133647e-02]
Sparsity at: 0.0
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2531e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38457489e-01
 -2.85873383e-01  2.38948204e-02]
Sparsity at: 0.0
Epoch 372/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3007e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38540399e-01
 -2.86066264e-01  2.37517226e-02]
Sparsity at: 0.0
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3007e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38648343e-01
 -2.86266267e-01  2.36288980e-02]
Sparsity at: 0.0
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38743472e-01
 -2.86470681e-01  2.35154796e-02]
Sparsity at: 0.0
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38855410e-01
 -2.86665767e-01  2.33872812e-02]
Sparsity at: 0.0
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2908e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.38963354e-01
 -2.86864430e-01  2.32622307e-02]
Sparsity at: 0.0
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39056993e-01
 -2.87077785e-01  2.31701396e-02]
Sparsity at: 0.0
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3186e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39167202e-01
 -2.87282735e-01  2.30443683e-02]
Sparsity at: 0.0
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3107e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39286888e-01
 -2.87479997e-01  2.29265317e-02]
Sparsity at: 0.0
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39377248e-01
 -2.87684679e-01  2.27985866e-02]
Sparsity at: 0.0
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2650e-09 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39487159e-01
 -2.87876308e-01  2.26705745e-02]
Sparsity at: 0.0
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39607322e-01
 -2.88083732e-01  2.25424264e-02]
Sparsity at: 0.0
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2451e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39715862e-01
 -2.88298368e-01  2.24129353e-02]
Sparsity at: 0.0
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39840376e-01
 -2.88499236e-01  2.22983621e-02]
Sparsity at: 0.0
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2888e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.39944029e-01
 -2.88727343e-01  2.21916419e-02]
Sparsity at: 0.0
Epoch 386/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2511e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40082133e-01
 -2.88931608e-01  2.20815316e-02]
Sparsity at: 0.0
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2630e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40145791e-01
 -2.89137781e-01  2.19360068e-02]
Sparsity at: 0.0
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40255702e-01
 -2.89337546e-01  2.18214430e-02]
Sparsity at: 0.0
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2451e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40351248e-01
 -2.89548606e-01  2.16988903e-02]
Sparsity at: 0.0
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40459907e-01
 -2.89742559e-01  2.15865131e-02]
Sparsity at: 0.0
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2590e-09 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40573752e-01
 -2.89949954e-01  2.14612093e-02]
Sparsity at: 0.0
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40666318e-01
 -2.90146559e-01  2.13294495e-02]
Sparsity at: 0.0
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40774620e-01
 -2.90373117e-01  2.12002620e-02]
Sparsity at: 0.0
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2173e-09 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.40876842e-01
 -2.90589243e-01  2.10563540e-02]
Sparsity at: 0.0
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3107e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41013992e-01
 -2.90786594e-01  2.09450405e-02]
Sparsity at: 0.0
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41123128e-01
 -2.91018546e-01  2.08151639e-02]
Sparsity at: 0.0
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41233158e-01
 -2.91220725e-01  2.06805244e-02]
Sparsity at: 0.0
Epoch 398/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41353083e-01
 -2.91427165e-01  2.05487702e-02]
Sparsity at: 0.0
Epoch 399/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2372e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41453218e-01
 -2.91651875e-01  2.04149354e-02]
Sparsity at: 0.0
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2153e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41561043e-01
 -2.91878700e-01  2.02855244e-02]
Sparsity at: 0.0
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.5270422661683085
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.6258507425320374
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 1.4723980643949375
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 42s 7ms/step - loss: 2.2630e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41673040e-01
 -2.92106688e-01  2.01588050e-02]
Sparsity at: 0.0
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 2.2809e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41771388e-01
 -2.92283744e-01  2.00307872e-02]
Sparsity at: 0.0
Epoch 403/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2550e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41868007e-01
 -2.92506546e-01  1.98941212e-02]
Sparsity at: 0.0
Epoch 404/500
235/235 [==============================] - 2s 10ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.41951096e-01
 -2.92689353e-01  1.97745040e-02]
Sparsity at: 0.0
Epoch 405/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42058742e-01
 -2.92901099e-01  1.96447633e-02]
Sparsity at: 0.0
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42200899e-01
 -2.93141544e-01  1.95166357e-02]
Sparsity at: 0.0
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2511e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42302287e-01
 -2.93382436e-01  1.93952695e-02]
Sparsity at: 0.0
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42418098e-01
 -2.93599695e-01  1.92537494e-02]
Sparsity at: 0.0
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2531e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42534983e-01
 -2.93819159e-01  1.91261563e-02]
Sparsity at: 0.0
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42642570e-01
 -2.94038624e-01  1.89545490e-02]
Sparsity at: 0.0
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42759395e-01
 -2.94254690e-01  1.88633576e-02]
Sparsity at: 0.0
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2550e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42874789e-01
 -2.94487983e-01  1.87181011e-02]
Sparsity at: 0.0
Epoch 413/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.42967713e-01
 -2.94697315e-01  1.85956415e-02]
Sparsity at: 0.0
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43077803e-01
 -2.94925898e-01  1.84855331e-02]
Sparsity at: 0.0
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2789e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43172932e-01
 -2.95119882e-01  1.83674805e-02]
Sparsity at: 0.0
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2391e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43295598e-01
 -2.95320600e-01  1.82380956e-02]
Sparsity at: 0.0
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43399549e-01
 -2.95548528e-01  1.80909466e-02]
Sparsity at: 0.0
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2570e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43524778e-01
 -2.95752555e-01  1.79642979e-02]
Sparsity at: 0.0
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2630e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43646610e-01
 -2.95953482e-01  1.78360883e-02]
Sparsity at: 0.0
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2471e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43744957e-01
 -2.96161503e-01  1.77066568e-02]
Sparsity at: 0.0
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2332e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43863928e-01
 -2.96393901e-01  1.75607577e-02]
Sparsity at: 0.0
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.43982422e-01
 -2.96626687e-01  1.74317993e-02]
Sparsity at: 0.0
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44111645e-01
 -2.96865016e-01  1.72851663e-02]
Sparsity at: 0.0
Epoch 424/500
235/235 [==============================] - 2s 7ms/step - loss: 2.2332e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44230378e-01
 -2.97109604e-01  1.71498340e-02]
Sparsity at: 0.0
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2272e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44325149e-01
 -2.97351837e-01  1.70032158e-02]
Sparsity at: 0.0
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2729e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44439471e-01
 -2.97595769e-01  1.68572851e-02]
Sparsity at: 0.0
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2908e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44519639e-01
 -2.97793776e-01  1.67543702e-02]
Sparsity at: 0.0
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2590e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44645226e-01
 -2.98026234e-01  1.66223962e-02]
Sparsity at: 0.0
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2888e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44774270e-01
 -2.98236072e-01  1.64820235e-02]
Sparsity at: 0.0
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2630e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44893658e-01
 -2.98430324e-01  1.63302589e-02]
Sparsity at: 0.0
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.44996059e-01
 -2.98661172e-01  1.61828361e-02]
Sparsity at: 0.0
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2372e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.45112824e-01
 -2.98893899e-01  1.60535537e-02]
Sparsity at: 0.0
Epoch 433/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2829e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.45244312e-01
 -2.99134403e-01  1.59059241e-02]
Sparsity at: 0.0
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.45371389e-01
 -2.99344569e-01  1.57548469e-02]
Sparsity at: 0.0
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2074e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.45480764e-01
 -2.99553931e-01  1.56060671e-02]
Sparsity at: 0.0
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.45607901e-01
 -2.99774408e-01  1.54759260e-02]
Sparsity at: 0.0
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.45732355e-01
 -2.99996227e-01  1.53520014e-02]
Sparsity at: 0.0
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.45842922e-01
 -3.00202817e-01  1.52285136e-02]
Sparsity at: 0.0
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1954e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.45941389e-01
 -3.00459027e-01  1.50872329e-02]
Sparsity at: 0.0
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.46087122e-01
 -3.00675988e-01  1.49583369e-02]
Sparsity at: 0.0
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2153e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.46190476e-01
 -3.00919831e-01  1.47944428e-02]
Sparsity at: 0.0
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.46346939e-01
 -3.01154852e-01  1.46322725e-02]
Sparsity at: 0.0
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3246e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9756
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.46472347e-01
 -3.01385343e-01  1.45136509e-02]
Sparsity at: 0.0
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2272e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.46603596e-01
 -3.01629126e-01  1.43880397e-02]
Sparsity at: 0.0
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.46730793e-01
 -3.01870674e-01  1.42418072e-02]
Sparsity at: 0.0
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2431e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.46864784e-01
 -3.02109033e-01  1.40872803e-02]
Sparsity at: 0.0
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.47039127e-01
 -3.02335769e-01  1.39256781e-02]
Sparsity at: 0.0
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2491e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.47155654e-01
 -3.02560091e-01  1.37946298e-02]
Sparsity at: 0.0
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.47258055e-01
 -3.02786618e-01  1.36603639e-02]
Sparsity at: 0.0
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2133e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.47397172e-01
 -3.03030580e-01  1.35149136e-02]
Sparsity at: 0.0
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2332e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.47530627e-01
 -3.03289115e-01  1.33642917e-02]
Sparsity at: 0.0
Epoch 452/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2650e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.47655916e-01
 -3.03539366e-01  1.31863924e-02]
Sparsity at: 0.0
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2272e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.47761178e-01
 -3.03784430e-01  1.30497301e-02]
Sparsity at: 0.0
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3007e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.47892785e-01
 -3.04005861e-01  1.28936414e-02]
Sparsity at: 0.0
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9755
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.48020637e-01
 -3.04228902e-01  1.27583854e-02]
Sparsity at: 0.0
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.48132813e-01
 -3.04452837e-01  1.26176411e-02]
Sparsity at: 0.0
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2550e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.48262155e-01
 -3.04704547e-01  1.24695813e-02]
Sparsity at: 0.0
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.48395550e-01
 -3.04945230e-01  1.23415012e-02]
Sparsity at: 0.0
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2292e-09 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.48519647e-01
 -3.05169433e-01  1.21671464e-02]
Sparsity at: 0.0
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.48657274e-01
 -3.05419743e-01  1.20440805e-02]
Sparsity at: 0.0
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2451e-09 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.48762536e-01
 -3.05681705e-01  1.19062131e-02]
Sparsity at: 0.0
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2968e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.48866189e-01
 -3.05908084e-01  1.17764063e-02]
Sparsity at: 0.0
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1855e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.49006617e-01
 -3.06141645e-01  1.16361463e-02]
Sparsity at: 0.0
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3007e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.49138820e-01
 -3.06387067e-01  1.14782676e-02]
Sparsity at: 0.0
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2809e-09 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.49247301e-01
 -3.06611717e-01  1.13269957e-02]
Sparsity at: 0.0
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.49358165e-01
 -3.06864649e-01  1.11741573e-02]
Sparsity at: 0.0
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2789e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.49463964e-01
 -3.07107210e-01  1.10130813e-02]
Sparsity at: 0.0
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2968e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.49611545e-01
 -3.07388693e-01  1.08463997e-02]
Sparsity at: 0.0
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.49755192e-01
 -3.07629436e-01  1.06958346e-02]
Sparsity at: 0.0
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.49876368e-01
 -3.07882637e-01  1.05686951e-02]
Sparsity at: 0.0
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2650e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.50014651e-01
 -3.08118820e-01  1.04321325e-02]
Sparsity at: 0.0
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.50157940e-01
 -3.08348238e-01  1.02946730e-02]
Sparsity at: 0.0
Epoch 473/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2332e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.50274169e-01
 -3.08610320e-01  1.01395873e-02]
Sparsity at: 0.0
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2590e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.50396001e-01
 -3.08885694e-01  9.96050797e-03]
Sparsity at: 0.0
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2312e-09 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.50530827e-01
 -3.09125006e-01  9.81031545e-03]
Sparsity at: 0.0
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2789e-09 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.50670063e-01
 -3.09412718e-01  9.66217462e-03]
Sparsity at: 0.0
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.50807810e-01
 -3.09622765e-01  9.50858835e-03]
Sparsity at: 0.0
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2233e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.50927675e-01
 -3.09862733e-01  9.35985427e-03]
Sparsity at: 0.0
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.51053977e-01
 -3.10111880e-01  9.20893345e-03]
Sparsity at: 0.0
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2888e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.51170206e-01
 -3.10357779e-01  9.03270021e-03]
Sparsity at: 0.0
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.51299012e-01
 -3.10600460e-01  8.87004752e-03]
Sparsity at: 0.0
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3206e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.51428831e-01
 -3.10844630e-01  8.71321093e-03]
Sparsity at: 0.0
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.51568246e-01
 -3.11082661e-01  8.53290688e-03]
Sparsity at: 0.0
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2491e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.51724827e-01
 -3.11321080e-01  8.37356132e-03]
Sparsity at: 0.0
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.51851189e-01
 -3.11582446e-01  8.21503717e-03]
Sparsity at: 0.0
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.51999962e-01
 -3.11859518e-01  8.07784311e-03]
Sparsity at: 0.0
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2550e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.52133179e-01
 -3.12110484e-01  7.90818781e-03]
Sparsity at: 0.0
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3067e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.52255011e-01
 -3.12348932e-01  7.74430623e-03]
Sparsity at: 0.0
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.52368677e-01
 -3.12595606e-01  7.58849783e-03]
Sparsity at: 0.0
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.52495992e-01
 -3.12877029e-01  7.42098549e-03]
Sparsity at: 0.0
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3067e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.52622890e-01
 -3.13126296e-01  7.27442512e-03]
Sparsity at: 0.0
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9759
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.52781677e-01
 -3.13397199e-01  7.10926624e-03]
Sparsity at: 0.0
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2650e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.52908695e-01
 -3.13661605e-01  6.95057306e-03]
Sparsity at: 0.0
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2570e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.53061283e-01
 -3.13935310e-01  6.78941188e-03]
Sparsity at: 0.0
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.53183889e-01
 -3.14222246e-01  6.63644727e-03]
Sparsity at: 0.0
Epoch 496/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.53332067e-01
 -3.14477414e-01  6.49842480e-03]
Sparsity at: 0.0
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.53476608e-01
 -3.14737469e-01  6.34068158e-03]
Sparsity at: 0.0
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9760
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.53594744e-01
 -3.15025270e-01  6.16831565e-03]
Sparsity at: 0.0
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9757
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.53725040e-01
 -3.15291137e-01  6.01910008e-03]
Sparsity at: 0.0
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9758
[-6.16294481e-02  1.14150345e-02 -6.17124140e-04 ... -9.53865707e-01
 -3.15545589e-01  5.87014109e-03]
Sparsity at: 0.0
Epoch 1/500
235/235 [==============================] - 5s 15ms/step - loss: 0.1403 - accuracy: 0.9782 - val_loss: 0.2178 - val_accuracy: 0.9534
[-4.4356627e-34  2.9253646e-34  8.6999785e-07 ...  2.8114814e-02
  3.4809522e-02 -1.7609550e-02]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9790 - val_loss: 0.2018 - val_accuracy: 0.9599
[-4.4356627e-34  2.9253646e-34  2.0628352e-06 ...  2.2707820e-02
  3.8300749e-02 -1.8742241e-02]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9796 - val_loss: 0.2181 - val_accuracy: 0.9535
[-4.4356627e-34  2.9253646e-34 -2.3647873e-07 ...  2.4266619e-02
  3.1056507e-02 -4.6939841e-03]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.2031 - val_accuracy: 0.9609
[-4.4356627e-34  2.9253646e-34 -1.0235022e-04 ...  2.3896152e-02
  3.2281827e-02 -2.7752947e-03]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9788 - val_loss: 0.1884 - val_accuracy: 0.9639
[-4.4356627e-34  2.9253646e-34  2.4245323e-06 ...  2.5337495e-02
  3.2146811e-02 -8.4898332e-03]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9794 - val_loss: 0.2097 - val_accuracy: 0.9579
[-4.4356627e-34  2.9253646e-34  1.6815411e-09 ...  2.2105722e-02
  3.3353094e-02 -1.1573269e-02]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9792 - val_loss: 0.1940 - val_accuracy: 0.9621
[-4.4356627e-34  2.9253646e-34  3.2540449e-08 ...  2.7064912e-02
  3.3438757e-02 -1.3257946e-02]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9798 - val_loss: 0.1836 - val_accuracy: 0.9661
[-4.4356627e-34  2.9253646e-34 -2.3241686e-05 ...  2.8341416e-02
  3.2972734e-02 -8.8457661e-03]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9780 - val_loss: 0.1793 - val_accuracy: 0.9674
[-4.4356627e-34  2.9253646e-34  3.3285624e-11 ...  2.7563902e-02
  4.0071361e-02 -3.4530694e-03]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1405 - accuracy: 0.9791 - val_loss: 0.1968 - val_accuracy: 0.9605
[-4.4356627e-34  2.9253646e-34 -1.4974354e-07 ...  2.4484839e-02
  3.2037817e-02 -4.9615912e-03]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9795 - val_loss: 0.1985 - val_accuracy: 0.9632
[-4.4356627e-34  2.9253646e-34 -8.4715657e-10 ...  2.4635160e-02
  3.2500427e-02 -8.2618659e-03]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9777 - val_loss: 0.2005 - val_accuracy: 0.9599
[-4.4356627e-34  2.9253646e-34 -3.1456713e-09 ...  2.2555325e-02
  2.6626091e-02 -1.0594313e-02]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.1911 - val_accuracy: 0.9641
[-4.4356627e-34  2.9253646e-34  1.7390824e-04 ...  2.1199387e-02
  3.4701910e-02 -7.0612682e-03]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9803 - val_loss: 0.2010 - val_accuracy: 0.9592
[-4.4356627e-34  2.9253646e-34  5.9900507e-10 ...  1.8665988e-02
  3.5157956e-02 -1.2766198e-02]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9791 - val_loss: 0.1925 - val_accuracy: 0.9646
[-4.4356627e-34  2.9253646e-34  1.0837175e-05 ...  1.1141744e-02
  3.9770748e-02 -2.7370539e-03]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9795 - val_loss: 0.2512 - val_accuracy: 0.9449
[-4.4356627e-34  2.9253646e-34 -9.1011768e-11 ...  1.6691022e-02
  3.9457034e-02 -8.6125014e-03]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9781 - val_loss: 0.2324 - val_accuracy: 0.9502
[-4.4356627e-34  2.9253646e-34  7.2142129e-06 ...  2.1801472e-02
  4.2004917e-02 -2.6586866e-03]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9791 - val_loss: 0.2117 - val_accuracy: 0.9582
[-4.4356627e-34  2.9253646e-34  3.2211209e-11 ...  1.6260084e-02
  3.8023904e-02 -3.6363578e-03]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1364 - accuracy: 0.9794 - val_loss: 0.2253 - val_accuracy: 0.9522
[-4.4356627e-34  2.9253646e-34  2.6021853e-06 ...  1.5645349e-02
  4.0428534e-02 -2.6659528e-03]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9793 - val_loss: 0.2315 - val_accuracy: 0.9531
[-4.4356627e-34  2.9253646e-34 -3.8888073e-11 ...  1.4256310e-02
  3.9871428e-02 -4.1871164e-03]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9791 - val_loss: 0.1968 - val_accuracy: 0.9626
[-4.43566273e-34  2.92536463e-34  1.22077945e-05 ...  6.72502303e-03
  3.83764654e-02 -2.32384587e-03]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9795 - val_loss: 0.2128 - val_accuracy: 0.9580
[-4.4356627e-34  2.9253646e-34  4.8230397e-10 ...  3.7452166e-03
  3.6951277e-02 -8.5197401e-04]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9791 - val_loss: 0.1852 - val_accuracy: 0.9660
[-4.4356627e-34  2.9253646e-34  1.1352503e-07 ...  9.3110343e-03
  3.6662113e-02 -3.5217057e-03]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9795 - val_loss: 0.1988 - val_accuracy: 0.9613
[-4.4356627e-34  2.9253646e-34  7.2233508e-10 ...  1.2345173e-02
  3.8336936e-02 -3.7225680e-03]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9796 - val_loss: 0.2083 - val_accuracy: 0.9611
[-4.4356627e-34  2.9253646e-34 -1.5901655e-11 ...  1.0363245e-02
  4.0690053e-02 -6.2533193e-03]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9789 - val_loss: 0.1918 - val_accuracy: 0.9643
[-4.4356627e-34  2.9253646e-34 -1.6603803e-08 ...  1.2871692e-02
  4.5399975e-02  3.3595660e-03]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1336 - accuracy: 0.9803 - val_loss: 0.2089 - val_accuracy: 0.9603
[-4.4356627e-34  2.9253646e-34  1.1611074e-14 ...  1.2908577e-02
  3.7387349e-02  4.6803150e-03]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1420 - accuracy: 0.9772 - val_loss: 0.1909 - val_accuracy: 0.9652
[-4.4356627e-34  2.9253646e-34 -1.2675883e-07 ...  1.5698574e-02
  4.3851707e-02  5.9758159e-03]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9800 - val_loss: 0.2025 - val_accuracy: 0.9603
[-4.4356627e-34  2.9253646e-34 -1.6535081e-13 ...  2.0728810e-02
  4.0740233e-02  5.8862166e-03]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9779 - val_loss: 0.1802 - val_accuracy: 0.9665
[-4.4356627e-34  2.9253646e-34 -2.1176586e-06 ...  1.6025430e-02
  4.7430277e-02  4.8945788e-03]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9799 - val_loss: 0.2029 - val_accuracy: 0.9588
[-4.4356627e-34  2.9253646e-34  1.2703524e-11 ...  2.0578504e-02
  4.0411767e-02 -6.6473940e-03]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1366 - accuracy: 0.9791 - val_loss: 0.2001 - val_accuracy: 0.9624
[-4.4356627e-34  2.9253646e-34 -4.9994997e-05 ...  1.3571465e-02
  4.1463271e-02 -9.9057890e-03]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9786 - val_loss: 0.2014 - val_accuracy: 0.9621
[-4.4356627e-34  2.9253646e-34 -2.7280800e-10 ...  1.8462239e-02
  3.7958566e-02 -9.2464909e-03]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9801 - val_loss: 0.2072 - val_accuracy: 0.9556
[-4.4356627e-34  2.9253646e-34  2.1029967e-13 ...  1.8352199e-02
  3.6578070e-02 -7.6208659e-03]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9785 - val_loss: 0.2332 - val_accuracy: 0.9513
[-4.4356627e-34  2.9253646e-34 -2.0386757e-08 ...  2.3226952e-02
  3.5327170e-02 -7.9526985e-03]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9792 - val_loss: 0.2093 - val_accuracy: 0.9586
[-4.4356627e-34  2.9253646e-34 -2.6826092e-13 ...  1.5596361e-02
  4.2395931e-02 -6.4118979e-03]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9799 - val_loss: 0.1857 - val_accuracy: 0.9655
[-4.4356627e-34  2.9253646e-34  2.0881648e-06 ...  1.4806369e-02
  4.3227941e-02  2.1685108e-03]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1400 - accuracy: 0.9785 - val_loss: 0.2009 - val_accuracy: 0.9621
[-4.4356627e-34  2.9253646e-34 -1.7623753e-11 ...  5.8548148e-03
  3.5814762e-02 -1.3462368e-03]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1417 - accuracy: 0.9780 - val_loss: 0.1911 - val_accuracy: 0.9656
[-4.4356627e-34  2.9253646e-34  1.7305936e-11 ...  1.0413783e-02
  3.4120601e-02  9.4516268e-03]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1385 - accuracy: 0.9794 - val_loss: 0.1992 - val_accuracy: 0.9608
[-4.4356627e-34  2.9253646e-34  1.2640411e-08 ...  1.3274160e-02
  3.4423355e-02 -3.4465648e-03]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9784 - val_loss: 0.2127 - val_accuracy: 0.9590
[-4.4356627e-34  2.9253646e-34 -9.4728119e-14 ...  1.2558985e-02
  4.1123256e-02  2.3248577e-03]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1388 - accuracy: 0.9791 - val_loss: 0.1810 - val_accuracy: 0.9692
[-4.4356627e-34  2.9253646e-34  2.0730442e-07 ...  1.5148821e-02
  4.1342162e-02 -6.5302886e-03]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9792 - val_loss: 0.2088 - val_accuracy: 0.9620
[-4.4356627e-34  2.9253646e-34  2.2067350e-12 ...  1.8062945e-02
  4.1603591e-02 -5.3234766e-03]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9789 - val_loss: 0.1822 - val_accuracy: 0.9654
[-4.4356627e-34  2.9253646e-34  4.1821153e-05 ...  2.1196369e-02
  3.3439983e-02 -5.2966471e-03]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1394 - accuracy: 0.9777 - val_loss: 0.2045 - val_accuracy: 0.9628
[-4.4356627e-34  2.9253646e-34  1.3934112e-10 ...  1.3416549e-02
  3.7804525e-02  6.3323870e-04]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1354 - accuracy: 0.9799 - val_loss: 0.1983 - val_accuracy: 0.9612
[-4.4356627e-34  2.9253646e-34  1.8034328e-07 ...  1.7215360e-02
  3.8162611e-02 -3.5554436e-03]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9798 - val_loss: 0.1847 - val_accuracy: 0.9644
[-4.4356627e-34  2.9253646e-34 -2.8809382e-09 ...  2.2185195e-02
  3.2359198e-02 -1.4449451e-02]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9793 - val_loss: 0.1859 - val_accuracy: 0.9656
[-4.4356627e-34  2.9253646e-34  6.0652590e-13 ...  1.9747971e-02
  4.4964917e-02 -6.0511660e-03]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1380 - accuracy: 0.9791 - val_loss: 0.2077 - val_accuracy: 0.9611
[-4.4356627e-34  2.9253646e-34  3.5328782e-08 ...  2.0591386e-02
  3.8017310e-02 -2.8537875e-03]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9793 - val_loss: 0.1845 - val_accuracy: 0.9654
[-4.4356627e-34  2.9253646e-34  2.0064465e-13 ...  1.8177766e-02
  3.7906237e-02 -8.3224906e-05]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9798 - val_loss: 0.1738 - val_accuracy: 0.9686
[-4.4356627e-34  0.0000000e+00  7.5561047e-08 ...  0.0000000e+00
  3.8337916e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9806 - val_loss: 0.1939 - val_accuracy: 0.9642
[-4.4356627e-34  0.0000000e+00  7.4753129e-12 ... -0.0000000e+00
  4.0081557e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9793 - val_loss: 0.2033 - val_accuracy: 0.9584
[-4.4356627e-34  0.0000000e+00  4.0164996e-05 ...  0.0000000e+00
  3.8575668e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1352 - accuracy: 0.9801 - val_loss: 0.1854 - val_accuracy: 0.9662
[-4.4356627e-34  0.0000000e+00  1.7040164e-10 ... -0.0000000e+00
  4.1046672e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9805 - val_loss: 0.1780 - val_accuracy: 0.9694
[-4.4356627e-34  0.0000000e+00 -8.9459604e-07 ...  0.0000000e+00
  4.2443641e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9790 - val_loss: 0.1859 - val_accuracy: 0.9655
[-4.4356627e-34  0.0000000e+00 -2.1861919e-09 ...  0.0000000e+00
  4.6921499e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9787 - val_loss: 0.2140 - val_accuracy: 0.9577
[-4.4356627e-34  0.0000000e+00 -1.0070818e-11 ...  0.0000000e+00
  4.6017870e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9790 - val_loss: 0.1978 - val_accuracy: 0.9620
[-4.4356627e-34  0.0000000e+00 -2.0438502e-08 ...  0.0000000e+00
  4.5299400e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9785 - val_loss: 0.2135 - val_accuracy: 0.9593
[-4.4356627e-34  0.0000000e+00 -2.4871468e-13 ... -0.0000000e+00
  4.6698000e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9801 - val_loss: 0.1977 - val_accuracy: 0.9600
[-4.4356627e-34  0.0000000e+00  1.7329231e-07 ...  0.0000000e+00
  4.6961948e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9793 - val_loss: 0.1726 - val_accuracy: 0.9687
[-4.4356627e-34  0.0000000e+00 -1.0099716e-12 ...  0.0000000e+00
  4.4800472e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9791 - val_loss: 0.2034 - val_accuracy: 0.9615
[-4.4356627e-34  0.0000000e+00 -7.9016473e-07 ... -0.0000000e+00
  4.6416786e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1362 - accuracy: 0.9791 - val_loss: 0.2069 - val_accuracy: 0.9575
[-4.4356627e-34  0.0000000e+00  4.2106444e-12 ...  0.0000000e+00
  4.8943881e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1390 - accuracy: 0.9787 - val_loss: 0.1848 - val_accuracy: 0.9658
[-4.4356627e-34  0.0000000e+00 -3.9353017e-06 ...  0.0000000e+00
  4.9843300e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9787 - val_loss: 0.2101 - val_accuracy: 0.9592
[-4.4356627e-34  0.0000000e+00  2.9262030e-11 ...  0.0000000e+00
  4.7083952e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9799 - val_loss: 0.1976 - val_accuracy: 0.9631
[-4.4356627e-34  0.0000000e+00 -8.1391161e-05 ...  0.0000000e+00
  4.9882617e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9792 - val_loss: 0.2004 - val_accuracy: 0.9617
[-4.4356627e-34  0.0000000e+00  5.1441817e-10 ... -0.0000000e+00
  4.8349380e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9793 - val_loss: 0.1986 - val_accuracy: 0.9629
[-4.4356627e-34  0.0000000e+00 -7.1694979e-09 ...  0.0000000e+00
  5.1423583e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9789 - val_loss: 0.1996 - val_accuracy: 0.9610
[-4.4356627e-34  0.0000000e+00 -7.1149597e-10 ...  0.0000000e+00
  4.5033433e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.1829 - val_accuracy: 0.9655
[-4.4356627e-34  0.0000000e+00 -1.0871173e-13 ...  0.0000000e+00
  4.5161065e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9787 - val_loss: 0.1884 - val_accuracy: 0.9642
[-4.4356627e-34  0.0000000e+00 -6.9089694e-08 ...  0.0000000e+00
  4.3527700e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9789 - val_loss: 0.1795 - val_accuracy: 0.9673
[-4.4356627e-34  0.0000000e+00 -4.5490548e-13 ...  0.0000000e+00
  3.9080158e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9791 - val_loss: 0.1872 - val_accuracy: 0.9638
[-4.4356627e-34  0.0000000e+00 -1.7250043e-06 ...  0.0000000e+00
  3.5831150e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9788 - val_loss: 0.1904 - val_accuracy: 0.9661
[-4.4356627e-34  0.0000000e+00  9.6639094e-12 ...  0.0000000e+00
  3.5006415e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9785 - val_loss: 0.1984 - val_accuracy: 0.9609
[-4.4356627e-34  0.0000000e+00  8.5559222e-06 ...  0.0000000e+00
  3.7686534e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9798 - val_loss: 0.2012 - val_accuracy: 0.9601
[-4.4356627e-34  0.0000000e+00 -1.7441773e-10 ...  0.0000000e+00
  4.5726351e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1352 - accuracy: 0.9789 - val_loss: 0.1930 - val_accuracy: 0.9623
[-4.4356627e-34  0.0000000e+00  1.4823360e-08 ...  0.0000000e+00
  4.3249365e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9789 - val_loss: 0.2054 - val_accuracy: 0.9614
[-4.4356627e-34  0.0000000e+00  4.3116963e-09 ...  0.0000000e+00
  5.0600823e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9795 - val_loss: 0.1828 - val_accuracy: 0.9677
[-4.4356627e-34  0.0000000e+00 -8.6048454e-13 ...  0.0000000e+00
  3.9024297e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.1780 - val_accuracy: 0.9666
[-4.4356627e-34  0.0000000e+00 -8.3123908e-10 ... -0.0000000e+00
  3.9982121e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9803 - val_loss: 0.1833 - val_accuracy: 0.9639
[-4.4356627e-34  0.0000000e+00 -3.3148874e-13 ...  0.0000000e+00
  4.0137947e-02  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1354 - accuracy: 0.9796 - val_loss: 0.1972 - val_accuracy: 0.9598
[-4.4356627e-34  0.0000000e+00 -2.8858494e-07 ...  0.0000000e+00
  3.7828699e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.1937 - val_accuracy: 0.9625
[-4.4356627e-34  0.0000000e+00 -2.0175914e-12 ... -0.0000000e+00
  3.3317301e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.1951 - val_accuracy: 0.9650
[-4.4356627e-34  0.0000000e+00  1.6610065e-06 ... -0.0000000e+00
  2.8387634e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1334 - accuracy: 0.9801 - val_loss: 0.1816 - val_accuracy: 0.9654
[-4.4356627e-34  0.0000000e+00 -6.1096527e-12 ...  0.0000000e+00
  2.9365592e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9785 - val_loss: 0.2060 - val_accuracy: 0.9577
[-4.4356627e-34  0.0000000e+00 -3.4340264e-06 ...  0.0000000e+00
  3.0280661e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9801 - val_loss: 0.1979 - val_accuracy: 0.9624
[-4.4356627e-34  0.0000000e+00  8.6926702e-11 ...  0.0000000e+00
  3.0917553e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9787 - val_loss: 0.1758 - val_accuracy: 0.9703
[-4.4356627e-34  0.0000000e+00 -7.4176787e-05 ... -0.0000000e+00
  2.3183962e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9799 - val_loss: 0.1857 - val_accuracy: 0.9653
[-4.4356627e-34  0.0000000e+00 -4.8237903e-10 ...  0.0000000e+00
  2.7763518e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1338 - accuracy: 0.9797 - val_loss: 0.1771 - val_accuracy: 0.9694
[-4.4356627e-34  0.0000000e+00 -4.9796534e-10 ...  0.0000000e+00
  2.7011510e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9796 - val_loss: 0.2320 - val_accuracy: 0.9546
[-4.4356627e-34  0.0000000e+00 -2.3421070e-09 ...  0.0000000e+00
  2.5811791e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9797 - val_loss: 0.1817 - val_accuracy: 0.9675
[-4.4356627e-34  0.0000000e+00 -1.0969568e-12 ... -0.0000000e+00
  2.9459586e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9797 - val_loss: 0.2190 - val_accuracy: 0.9590
[-4.4356627e-34  0.0000000e+00 -1.3856273e-08 ...  0.0000000e+00
  3.4079906e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9790 - val_loss: 0.1913 - val_accuracy: 0.9645
[-4.4356627e-34  0.0000000e+00  1.1063107e-13 ... -0.0000000e+00
  3.2372128e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9787 - val_loss: 0.1736 - val_accuracy: 0.9681
[-4.4356627e-34  0.0000000e+00 -2.0720938e-07 ...  0.0000000e+00
  3.4817919e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9802 - val_loss: 0.1973 - val_accuracy: 0.9607
[-4.4356627e-34  0.0000000e+00  1.3105747e-12 ...  0.0000000e+00
  3.2587487e-02  0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9802 - val_loss: 0.1841 - val_accuracy: 0.9653
[-4.4356627e-34  0.0000000e+00  8.1787135e-07 ...  0.0000000e+00
  2.7937170e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1323 - accuracy: 0.9803 - val_loss: 0.2062 - val_accuracy: 0.9608
[-4.4356627e-34  0.0000000e+00  6.2087054e-13 ... -0.0000000e+00
  2.8586470e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9788 - val_loss: 0.1928 - val_accuracy: 0.9638
[-4.4356627e-34  0.0000000e+00 -3.2267062e-06 ...  0.0000000e+00
  2.6585540e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9793 - val_loss: 0.1819 - val_accuracy: 0.9684
[-4.4356627e-34  0.0000000e+00  4.8087419e-12 ...  0.0000000e+00
  2.4012934e-02 -0.0000000e+00]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9784 - val_loss: 0.1906 - val_accuracy: 0.9630
[ 0.00000000e+00  0.00000000e+00 -1.10901765e-05 ...  0.00000000e+00
  0.00000000e+00 -0.00000000e+00]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9790 - val_loss: 0.1971 - val_accuracy: 0.9619
[ 0.0000000e+00  0.0000000e+00  9.3825336e-11 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9797 - val_loss: 0.1929 - val_accuracy: 0.9639
[ 0.0000000e+00  0.0000000e+00  6.0431394e-06 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9797 - val_loss: 0.1930 - val_accuracy: 0.9622
[ 0.000000e+00  0.000000e+00  2.420294e-10 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9778 - val_loss: 0.1866 - val_accuracy: 0.9640
[ 0.0000000e+00  0.0000000e+00  3.1896767e-13 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1307 - accuracy: 0.9806 - val_loss: 0.1744 - val_accuracy: 0.9669
[ 0.0000000e+00  0.0000000e+00  3.0714983e-08 ... -0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9791 - val_loss: 0.1897 - val_accuracy: 0.9635
[ 0.0000000e+00  0.0000000e+00  3.0954333e-13 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1369 - accuracy: 0.9794 - val_loss: 0.1845 - val_accuracy: 0.9672
[ 0.0000000e+00  0.0000000e+00 -3.1310702e-07 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1332 - accuracy: 0.9798 - val_loss: 0.1953 - val_accuracy: 0.9643
[ 0.000000e+00  0.000000e+00 -8.974476e-12 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1357 - accuracy: 0.9791 - val_loss: 0.1918 - val_accuracy: 0.9637
[ 0.000000e+00  0.000000e+00  2.656984e-05 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9796 - val_loss: 0.1949 - val_accuracy: 0.9620
[ 0.0000000e+00  0.0000000e+00 -1.3925776e-10 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9792 - val_loss: 0.1838 - val_accuracy: 0.9648
[ 0.000000e+00  0.000000e+00 -1.450744e-05 ...  0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9795 - val_loss: 0.1837 - val_accuracy: 0.9643
[ 0.0000000e+00  0.0000000e+00  1.4368234e-09 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1341 - accuracy: 0.9793 - val_loss: 0.1950 - val_accuracy: 0.9625
[ 0.0000000e+00  0.0000000e+00 -6.5859407e-10 ... -0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9796 - val_loss: 0.1944 - val_accuracy: 0.9628
[ 0.000000e+00  0.000000e+00  7.662484e-09 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9796 - val_loss: 0.1838 - val_accuracy: 0.9640
[ 0.0000000e+00  0.0000000e+00 -4.1895174e-13 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9802 - val_loss: 0.2107 - val_accuracy: 0.9574
[ 0.000000e+00  0.000000e+00 -3.666252e-08 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9800 - val_loss: 0.1824 - val_accuracy: 0.9651
[ 0.000000e+00  0.000000e+00 -7.948408e-14 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9791 - val_loss: 0.1905 - val_accuracy: 0.9650
[ 0.0000000e+00  0.0000000e+00 -1.8969446e-07 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9793 - val_loss: 0.1830 - val_accuracy: 0.9659
[ 0.0000000e+00  0.0000000e+00  2.7408983e-13 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1342 - accuracy: 0.9793 - val_loss: 0.1950 - val_accuracy: 0.9617
[ 0.0000000e+00  0.0000000e+00 -2.5255802e-06 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9803 - val_loss: 0.2352 - val_accuracy: 0.9516
[ 0.0000000e+00  0.0000000e+00  2.5782834e-11 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9796 - val_loss: 0.1807 - val_accuracy: 0.9667
[ 0.000000e+00  0.000000e+00 -4.067086e-05 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9795 - val_loss: 0.1920 - val_accuracy: 0.9640
[ 0.0000000e+00  0.0000000e+00 -3.2448738e-11 ... -0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1337 - accuracy: 0.9797 - val_loss: 0.2015 - val_accuracy: 0.9641
[ 0.000000e+00  0.000000e+00 -4.041456e-05 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9796 - val_loss: 0.1783 - val_accuracy: 0.9661
[ 0.0000000e+00  0.0000000e+00 -1.1982384e-09 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9794 - val_loss: 0.1990 - val_accuracy: 0.9599
[ 0.0000000e+00  0.0000000e+00 -2.0103468e-10 ... -0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9801 - val_loss: 0.2071 - val_accuracy: 0.9599
[ 0.00000000e+00  0.00000000e+00 -1.05678115e-08 ...  0.00000000e+00
 -0.00000000e+00 -0.00000000e+00]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9791 - val_loss: 0.2264 - val_accuracy: 0.9522
[ 0.0000000e+00  0.0000000e+00  5.0367284e-13 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9795 - val_loss: 0.1842 - val_accuracy: 0.9654
[ 0.0000000e+00  0.0000000e+00 -5.7416223e-08 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9798 - val_loss: 0.2186 - val_accuracy: 0.9575
[ 0.000000e+00  0.000000e+00 -4.046129e-13 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9796 - val_loss: 0.2148 - val_accuracy: 0.9543
[ 0.0000000e+00  0.0000000e+00  1.5337136e-07 ... -0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1308 - accuracy: 0.9804 - val_loss: 0.1888 - val_accuracy: 0.9644
[ 0.0000000e+00  0.0000000e+00 -1.3391382e-12 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1405 - accuracy: 0.9777 - val_loss: 0.1984 - val_accuracy: 0.9617
[ 0.0000000e+00  0.0000000e+00  1.3954218e-07 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9794 - val_loss: 0.1900 - val_accuracy: 0.9645
[ 0.000000e+00  0.000000e+00 -9.914687e-12 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9789 - val_loss: 0.1847 - val_accuracy: 0.9659
[ 0.00000e+00  0.00000e+00 -4.68079e-06 ... -0.00000e+00 -0.00000e+00
 -0.00000e+00]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9798 - val_loss: 0.2027 - val_accuracy: 0.9616
[ 0.0000000e+00  0.0000000e+00  4.7218417e-11 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9793 - val_loss: 0.2030 - val_accuracy: 0.9571
[ 0.0000000e+00  0.0000000e+00  2.6743915e-05 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9796 - val_loss: 0.1848 - val_accuracy: 0.9664
[ 0.00000000e+00  0.00000000e+00  1.12650125e-10 ...  0.00000000e+00
  0.00000000e+00 -0.00000000e+00]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9785 - val_loss: 0.2062 - val_accuracy: 0.9614
[ 0.000000e+00  0.000000e+00 -6.849979e-05 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1346 - accuracy: 0.9791 - val_loss: 0.2039 - val_accuracy: 0.9595
[ 0.0000000e+00  0.0000000e+00 -3.3240766e-10 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9785 - val_loss: 0.1897 - val_accuracy: 0.9634
[ 0.000000e+00  0.000000e+00 -2.473981e-07 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9803 - val_loss: 0.1956 - val_accuracy: 0.9631
[ 0.000000e+00  0.000000e+00 -2.167746e-09 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9796 - val_loss: 0.2088 - val_accuracy: 0.9585
[ 0.0000000e+00  0.0000000e+00 -2.6023326e-11 ... -0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.2211 - val_accuracy: 0.9530
[ 0.000000e+00  0.000000e+00  5.179608e-09 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1365 - accuracy: 0.9790 - val_loss: 0.1793 - val_accuracy: 0.9674
[ 0.0000000e+00  0.0000000e+00  1.1345071e-13 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9789 - val_loss: 0.1976 - val_accuracy: 0.9592
[ 0.0000000e+00  0.0000000e+00 -7.4160187e-07 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9793 - val_loss: 0.2069 - val_accuracy: 0.9595
[ 0.0000000e+00  0.0000000e+00 -1.6349986e-14 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9790 - val_loss: 0.2184 - val_accuracy: 0.9554
[ 0.0000000e+00  0.0000000e+00 -1.3019195e-05 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1321 - accuracy: 0.9807 - val_loss: 0.2058 - val_accuracy: 0.9586
[ 0.0000000e+00  0.0000000e+00 -8.9659134e-11 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9779 - val_loss: 0.1793 - val_accuracy: 0.9653
[ 0.000000e+00  0.000000e+00  9.122719e-05 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9786 - val_loss: 0.2079 - val_accuracy: 0.9588
[ 0.0000000e+00  0.0000000e+00  1.1002177e-09 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9785 - val_loss: 0.1938 - val_accuracy: 0.9623
[ 0.0000000e+00  0.0000000e+00 -1.9955628e-09 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1369 - accuracy: 0.9785 - val_loss: 0.2126 - val_accuracy: 0.9591
[ 0.0000000e+00  0.0000000e+00 -1.7042057e-10 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9793 - val_loss: 0.2014 - val_accuracy: 0.9613
[ 0.0000000e+00  0.0000000e+00 -3.3773284e-12 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9785 - val_loss: 0.2231 - val_accuracy: 0.9530
[ 0.0000000e+00  0.0000000e+00  2.6485694e-08 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9786 - val_loss: 0.1938 - val_accuracy: 0.9638
[ 0.0000000e+00  0.0000000e+00 -5.0965195e-13 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1362 - accuracy: 0.9787 - val_loss: 0.1957 - val_accuracy: 0.9639
[ 0.000000e+00  0.000000e+00 -3.639437e-08 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9781 - val_loss: 0.1945 - val_accuracy: 0.9643
[ 0.000000e+00  0.000000e+00  6.132617e-13 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9793 - val_loss: 0.2332 - val_accuracy: 0.9516
[ 0.0000000e+00  0.0000000e+00  4.7822334e-07 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9790 - val_loss: 0.1917 - val_accuracy: 0.9632
[ 0.000000e+00  0.000000e+00 -1.966705e-12 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.2156 - val_accuracy: 0.9570
[ 0.000000e+00  0.000000e+00 -1.616819e-05 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9789 - val_loss: 0.2114 - val_accuracy: 0.9561
[ 0.00000e+00  0.00000e+00  5.03321e-11 ... -0.00000e+00  0.00000e+00
  0.00000e+00]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9790 - val_loss: 0.2597 - val_accuracy: 0.9450
[ 0.000000e+00  0.000000e+00 -8.558493e-07 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9777 - val_loss: 0.2103 - val_accuracy: 0.9586
[ 0.000000e+00  0.000000e+00 -6.353012e-10 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9794 - val_loss: 0.2185 - val_accuracy: 0.9569
[ 0.000000e+00  0.000000e+00  3.145237e-13 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9788 - val_loss: 0.1964 - val_accuracy: 0.9628
[ 0.00000e+00  0.00000e+00  3.56048e-08 ...  0.00000e+00  0.00000e+00
 -0.00000e+00]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9783 - val_loss: 0.1938 - val_accuracy: 0.9648
[ 0.0000000e+00  0.0000000e+00  3.1849425e-13 ... -0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9784 - val_loss: 0.1942 - val_accuracy: 0.9617
[ 0.0000000e+00  0.0000000e+00  4.9574453e-08 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1357 - accuracy: 0.9790 - val_loss: 0.2294 - val_accuracy: 0.9517
[ 0.00000e+00  0.00000e+00 -2.91406e-13 ...  0.00000e+00 -0.00000e+00
 -0.00000e+00]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9776 - val_loss: 0.1734 - val_accuracy: 0.9704
[ 0.0000000e+00  0.0000000e+00  1.7178622e-06 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1332 - accuracy: 0.9798 - val_loss: 0.1958 - val_accuracy: 0.9610
[ 0.0000000e+00  0.0000000e+00  4.0700582e-11 ...  0.0000000e+00
 -0.0000000e+00  0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9789 - val_loss: 0.2025 - val_accuracy: 0.9615
[ 0.0000000e+00  0.0000000e+00 -8.1325794e-05 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9795 - val_loss: 0.2253 - val_accuracy: 0.9536
[ 0.0000000e+00  0.0000000e+00 -4.2789614e-10 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9786 - val_loss: 0.2314 - val_accuracy: 0.9528
[ 0.000000e+00  0.000000e+00  6.446089e-10 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1364 - accuracy: 0.9786 - val_loss: 0.1862 - val_accuracy: 0.9652
[ 0.00000e+00  0.00000e+00  2.41013e-10 ... -0.00000e+00 -0.00000e+00
 -0.00000e+00]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9790 - val_loss: 0.2028 - val_accuracy: 0.9605
[ 0.0000000e+00  0.0000000e+00  3.7481926e-14 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9784 - val_loss: 0.2282 - val_accuracy: 0.9540
[ 0.000000e+00  0.000000e+00 -9.490899e-08 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.2031 - val_accuracy: 0.9592
[ 0.0000000e+00  0.0000000e+00 -7.0986484e-13 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1391 - accuracy: 0.9778 - val_loss: 0.2167 - val_accuracy: 0.9587
[ 0.000000e+00  0.000000e+00 -3.865753e-06 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1332 - accuracy: 0.9799 - val_loss: 0.1905 - val_accuracy: 0.9637
[ 0.000000e+00  0.000000e+00 -2.171804e-11 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1348 - accuracy: 0.9787 - val_loss: 0.2044 - val_accuracy: 0.9609
[ 0.0000000e+00  0.0000000e+00 -2.4548866e-05 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9785 - val_loss: 0.1906 - val_accuracy: 0.9654
[ 0.0000000e+00  0.0000000e+00 -3.1039984e-10 ... -0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9798 - val_loss: 0.1802 - val_accuracy: 0.9683
[ 0.0000000e+00  0.0000000e+00 -1.8686228e-07 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9779 - val_loss: 0.1834 - val_accuracy: 0.9693
[ 0.0000000e+00  0.0000000e+00  1.3447674e-09 ...  0.0000000e+00
 -0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9791 - val_loss: 0.1898 - val_accuracy: 0.9630
[ 0.0000000e+00  0.0000000e+00  1.4321574e-11 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9788 - val_loss: 0.2183 - val_accuracy: 0.9529
[ 0.000000e+00  0.000000e+00  1.781405e-08 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9791 - val_loss: 0.2004 - val_accuracy: 0.9584
[ 0.000000e+00  0.000000e+00  1.799275e-13 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9782 - val_loss: 0.2263 - val_accuracy: 0.9522
[ 0.000000e+00  0.000000e+00  6.602303e-08 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9789 - val_loss: 0.1933 - val_accuracy: 0.9645
[ 0.000000e+00  0.000000e+00  1.078543e-12 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9797 - val_loss: 0.1967 - val_accuracy: 0.9625
[ 0.0000000e+00  0.0000000e+00 -7.2546334e-07 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2067 - val_accuracy: 0.9586
[ 0.00000e+00  0.00000e+00 -4.84798e-12 ...  0.00000e+00  0.00000e+00
 -0.00000e+00]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1343 - accuracy: 0.9800 - val_loss: 0.2137 - val_accuracy: 0.9586
[ 0.0000000e+00  0.0000000e+00  8.7397086e-07 ... -0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9784 - val_loss: 0.2025 - val_accuracy: 0.9618
[ 0.000000e+00  0.000000e+00  9.598258e-12 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9791 - val_loss: 0.1935 - val_accuracy: 0.9627
[ 0.000000e+00  0.000000e+00  6.188666e-07 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9803 - val_loss: 0.1917 - val_accuracy: 0.9631
[ 0.000000e+00  0.000000e+00 -4.729945e-11 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.2092 - val_accuracy: 0.9591
[ 0.00000000e+00  0.00000000e+00 -1.10468845e-05 ... -0.00000000e+00
  0.00000000e+00 -0.00000000e+00]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9787 - val_loss: 0.2098 - val_accuracy: 0.9566
[ 0.0000000e+00  0.0000000e+00 -1.6847905e-11 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9808 - val_loss: 0.1943 - val_accuracy: 0.9615
[ 0.0000000e+00  0.0000000e+00  3.5817135e-05 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2142 - val_accuracy: 0.9580
[ 0.0000000e+00  0.0000000e+00 -1.5523263e-10 ...  0.0000000e+00
  0.0000000e+00 -0.0000000e+00]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9777 - val_loss: 0.1769 - val_accuracy: 0.9683
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9778 - val_loss: 0.2163 - val_accuracy: 0.9570
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1398 - accuracy: 0.9777 - val_loss: 0.2232 - val_accuracy: 0.9517
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9787 - val_loss: 0.1947 - val_accuracy: 0.9622
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9779 - val_loss: 0.1877 - val_accuracy: 0.9637
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9789 - val_loss: 0.1892 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9784 - val_loss: 0.2305 - val_accuracy: 0.9521
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9779 - val_loss: 0.2202 - val_accuracy: 0.9561
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9782 - val_loss: 0.2181 - val_accuracy: 0.9582
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9781 - val_loss: 0.2053 - val_accuracy: 0.9611
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1382 - accuracy: 0.9786 - val_loss: 0.2529 - val_accuracy: 0.9473
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.2279 - val_accuracy: 0.9524
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9776 - val_loss: 0.2178 - val_accuracy: 0.9567
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9777 - val_loss: 0.1997 - val_accuracy: 0.9616
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9794 - val_loss: 0.2038 - val_accuracy: 0.9627
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1351 - accuracy: 0.9787 - val_loss: 0.1945 - val_accuracy: 0.9659
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9786 - val_loss: 0.2124 - val_accuracy: 0.9578
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9777 - val_loss: 0.2140 - val_accuracy: 0.9591
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9783 - val_loss: 0.1876 - val_accuracy: 0.9660
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9792 - val_loss: 0.1946 - val_accuracy: 0.9629
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9797 - val_loss: 0.1980 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9767 - val_loss: 0.2060 - val_accuracy: 0.9605
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9794 - val_loss: 0.1948 - val_accuracy: 0.9639
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9791 - val_loss: 0.1953 - val_accuracy: 0.9636
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9783 - val_loss: 0.2118 - val_accuracy: 0.9587
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9778 - val_loss: 0.1980 - val_accuracy: 0.9650
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9795 - val_loss: 0.2045 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9787 - val_loss: 0.2029 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1344 - accuracy: 0.9787 - val_loss: 0.1963 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9787 - val_loss: 0.1879 - val_accuracy: 0.9645
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9784 - val_loss: 0.2016 - val_accuracy: 0.9614
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1369 - accuracy: 0.9783 - val_loss: 0.1990 - val_accuracy: 0.9621
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9767 - val_loss: 0.2106 - val_accuracy: 0.9599
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9794 - val_loss: 0.1917 - val_accuracy: 0.9626
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9779 - val_loss: 0.2095 - val_accuracy: 0.9582
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9798 - val_loss: 0.1843 - val_accuracy: 0.9669
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9786 - val_loss: 0.2177 - val_accuracy: 0.9590
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9790 - val_loss: 0.1882 - val_accuracy: 0.9665
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9789 - val_loss: 0.2071 - val_accuracy: 0.9602
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9774 - val_loss: 0.2195 - val_accuracy: 0.9550
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.1886 - val_accuracy: 0.9648
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9789 - val_loss: 0.1970 - val_accuracy: 0.9600
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9787 - val_loss: 0.2310 - val_accuracy: 0.9538
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9779 - val_loss: 0.2156 - val_accuracy: 0.9568
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9789 - val_loss: 0.2121 - val_accuracy: 0.9580
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1343 - accuracy: 0.9792 - val_loss: 0.2014 - val_accuracy: 0.9618
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9787 - val_loss: 0.2240 - val_accuracy: 0.9528
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.1896 - val_accuracy: 0.9655
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9780 - val_loss: 0.1980 - val_accuracy: 0.9620
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9794 - val_loss: 0.2352 - val_accuracy: 0.9502
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9776 - val_loss: 0.1893 - val_accuracy: 0.9616
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1265 - accuracy: 0.9791 - val_loss: 0.1886 - val_accuracy: 0.9631
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9798 - val_loss: 0.1824 - val_accuracy: 0.9649
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9790 - val_loss: 0.2002 - val_accuracy: 0.9614
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1264 - accuracy: 0.9790 - val_loss: 0.2162 - val_accuracy: 0.9572
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1270 - accuracy: 0.9791 - val_loss: 0.1990 - val_accuracy: 0.9582
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9808 - val_loss: 0.1774 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1206 - accuracy: 0.9803 - val_loss: 0.1787 - val_accuracy: 0.9643
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9804 - val_loss: 0.1979 - val_accuracy: 0.9594
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9815 - val_loss: 0.1787 - val_accuracy: 0.9651
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9804 - val_loss: 0.2051 - val_accuracy: 0.9565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9803 - val_loss: 0.1998 - val_accuracy: 0.9598
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1217 - accuracy: 0.9808 - val_loss: 0.1757 - val_accuracy: 0.9659
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9803 - val_loss: 0.1738 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9802 - val_loss: 0.1955 - val_accuracy: 0.9612
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9811 - val_loss: 0.2095 - val_accuracy: 0.9565
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1196 - accuracy: 0.9803 - val_loss: 0.1839 - val_accuracy: 0.9621
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9801 - val_loss: 0.1799 - val_accuracy: 0.9645
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9807 - val_loss: 0.1903 - val_accuracy: 0.9637
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1177 - accuracy: 0.9814 - val_loss: 0.1787 - val_accuracy: 0.9667
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1163 - accuracy: 0.9812 - val_loss: 0.2090 - val_accuracy: 0.9572
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1214 - accuracy: 0.9801 - val_loss: 0.1951 - val_accuracy: 0.9612
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9817 - val_loss: 0.2143 - val_accuracy: 0.9545
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9792 - val_loss: 0.2043 - val_accuracy: 0.9586
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9802 - val_loss: 0.2010 - val_accuracy: 0.9566
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9808 - val_loss: 0.2186 - val_accuracy: 0.9541
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9802 - val_loss: 0.2045 - val_accuracy: 0.9573
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9802 - val_loss: 0.1858 - val_accuracy: 0.9634
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9810 - val_loss: 0.1798 - val_accuracy: 0.9658
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9804 - val_loss: 0.1822 - val_accuracy: 0.9653
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9804 - val_loss: 0.1958 - val_accuracy: 0.9607
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9809 - val_loss: 0.1917 - val_accuracy: 0.9636
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9812 - val_loss: 0.2145 - val_accuracy: 0.9551
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9800 - val_loss: 0.2253 - val_accuracy: 0.9524
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1171 - accuracy: 0.9815 - val_loss: 0.2032 - val_accuracy: 0.9578
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1223 - accuracy: 0.9800 - val_loss: 0.1794 - val_accuracy: 0.9663
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1173 - accuracy: 0.9811 - val_loss: 0.1808 - val_accuracy: 0.9638
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1194 - accuracy: 0.9805 - val_loss: 0.1850 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9813 - val_loss: 0.1935 - val_accuracy: 0.9608
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1201 - accuracy: 0.9800 - val_loss: 0.2114 - val_accuracy: 0.9561
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1170 - accuracy: 0.9811 - val_loss: 0.1717 - val_accuracy: 0.9675
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1172 - accuracy: 0.9810 - val_loss: 0.1753 - val_accuracy: 0.9662
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9811 - val_loss: 0.2093 - val_accuracy: 0.9544
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1175 - accuracy: 0.9816 - val_loss: 0.1864 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9809 - val_loss: 0.2057 - val_accuracy: 0.9559
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1163 - accuracy: 0.9809 - val_loss: 0.1917 - val_accuracy: 0.9600
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9807 - val_loss: 0.2053 - val_accuracy: 0.9568
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1187 - accuracy: 0.9806 - val_loss: 0.1970 - val_accuracy: 0.9581
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9807 - val_loss: 0.1945 - val_accuracy: 0.9605
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9806 - val_loss: 0.1918 - val_accuracy: 0.9624
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9755 - val_loss: 0.1819 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9786 - val_loss: 0.1700 - val_accuracy: 0.9649
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9791 - val_loss: 0.1811 - val_accuracy: 0.9643
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1146 - accuracy: 0.9792 - val_loss: 0.1944 - val_accuracy: 0.9595
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1126 - accuracy: 0.9803 - val_loss: 0.1998 - val_accuracy: 0.9562
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1088 - accuracy: 0.9807 - val_loss: 0.1729 - val_accuracy: 0.9652
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1091 - accuracy: 0.9804 - val_loss: 0.1837 - val_accuracy: 0.9619
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1116 - accuracy: 0.9795 - val_loss: 0.1897 - val_accuracy: 0.9618
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1082 - accuracy: 0.9809 - val_loss: 0.1729 - val_accuracy: 0.9654
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1064 - accuracy: 0.9816 - val_loss: 0.2077 - val_accuracy: 0.9535
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1059 - accuracy: 0.9813 - val_loss: 0.2058 - val_accuracy: 0.9539
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1062 - accuracy: 0.9811 - val_loss: 0.1853 - val_accuracy: 0.9612
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1055 - accuracy: 0.9810 - val_loss: 0.1774 - val_accuracy: 0.9616
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1070 - accuracy: 0.9806 - val_loss: 0.1835 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1067 - accuracy: 0.9806 - val_loss: 0.1801 - val_accuracy: 0.9609
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9813 - val_loss: 0.1668 - val_accuracy: 0.9652
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1058 - accuracy: 0.9809 - val_loss: 0.2037 - val_accuracy: 0.9579
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9819 - val_loss: 0.1843 - val_accuracy: 0.9604
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9809 - val_loss: 0.2075 - val_accuracy: 0.9555
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9813 - val_loss: 0.1894 - val_accuracy: 0.9589
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1056 - accuracy: 0.9815 - val_loss: 0.1792 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1039 - accuracy: 0.9814 - val_loss: 0.1878 - val_accuracy: 0.9608
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1071 - accuracy: 0.9804 - val_loss: 0.2092 - val_accuracy: 0.9531
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1082 - accuracy: 0.9800 - val_loss: 0.1893 - val_accuracy: 0.9593
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1040 - accuracy: 0.9818 - val_loss: 0.1878 - val_accuracy: 0.9612
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1023 - accuracy: 0.9818 - val_loss: 0.1865 - val_accuracy: 0.9617
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1070 - accuracy: 0.9804 - val_loss: 0.1940 - val_accuracy: 0.9593
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9815 - val_loss: 0.1768 - val_accuracy: 0.9627
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1051 - accuracy: 0.9811 - val_loss: 0.1891 - val_accuracy: 0.9577
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9812 - val_loss: 0.1974 - val_accuracy: 0.9585
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9808 - val_loss: 0.1863 - val_accuracy: 0.9593
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9813 - val_loss: 0.1955 - val_accuracy: 0.9579
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1059 - accuracy: 0.9809 - val_loss: 0.1798 - val_accuracy: 0.9623
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9809 - val_loss: 0.1956 - val_accuracy: 0.9585
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1046 - accuracy: 0.9816 - val_loss: 0.1895 - val_accuracy: 0.9589
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1056 - accuracy: 0.9807 - val_loss: 0.1810 - val_accuracy: 0.9602
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1066 - accuracy: 0.9809 - val_loss: 0.1769 - val_accuracy: 0.9643
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9811 - val_loss: 0.1800 - val_accuracy: 0.9625
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9814 - val_loss: 0.1833 - val_accuracy: 0.9604
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1053 - accuracy: 0.9811 - val_loss: 0.1816 - val_accuracy: 0.9623
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1040 - accuracy: 0.9819 - val_loss: 0.1707 - val_accuracy: 0.9654
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1044 - accuracy: 0.9808 - val_loss: 0.2062 - val_accuracy: 0.9552
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1038 - accuracy: 0.9818 - val_loss: 0.1794 - val_accuracy: 0.9614
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9812 - val_loss: 0.1744 - val_accuracy: 0.9660
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1055 - accuracy: 0.9807 - val_loss: 0.1863 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1055 - accuracy: 0.9807 - val_loss: 0.1822 - val_accuracy: 0.9624
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1032 - accuracy: 0.9816 - val_loss: 0.1714 - val_accuracy: 0.9650
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1037 - accuracy: 0.9814 - val_loss: 0.1795 - val_accuracy: 0.9598
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1028 - accuracy: 0.9814 - val_loss: 0.1780 - val_accuracy: 0.9625
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1027 - accuracy: 0.9817 - val_loss: 0.2028 - val_accuracy: 0.9579
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2009 - accuracy: 0.9611 - val_loss: 0.2082 - val_accuracy: 0.9567
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1457 - accuracy: 0.9724 - val_loss: 0.1913 - val_accuracy: 0.9602
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9743 - val_loss: 0.1906 - val_accuracy: 0.9607
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9748 - val_loss: 0.1865 - val_accuracy: 0.9624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9743 - val_loss: 0.1823 - val_accuracy: 0.9634
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1251 - accuracy: 0.9758 - val_loss: 0.1819 - val_accuracy: 0.9643
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9755 - val_loss: 0.1834 - val_accuracy: 0.9620
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9767 - val_loss: 0.1729 - val_accuracy: 0.9654
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9764 - val_loss: 0.1724 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1225 - accuracy: 0.9758 - val_loss: 0.1789 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1197 - accuracy: 0.9772 - val_loss: 0.1907 - val_accuracy: 0.9594
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9761 - val_loss: 0.1817 - val_accuracy: 0.9617
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9761 - val_loss: 0.1792 - val_accuracy: 0.9636
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9771 - val_loss: 0.1830 - val_accuracy: 0.9617
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1171 - accuracy: 0.9775 - val_loss: 0.1830 - val_accuracy: 0.9617
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9762 - val_loss: 0.1721 - val_accuracy: 0.9632
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1160 - accuracy: 0.9774 - val_loss: 0.1760 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1159 - accuracy: 0.9774 - val_loss: 0.1752 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9766 - val_loss: 0.1695 - val_accuracy: 0.9660
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1155 - accuracy: 0.9776 - val_loss: 0.1730 - val_accuracy: 0.9643
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9765 - val_loss: 0.1782 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1168 - accuracy: 0.9770 - val_loss: 0.1784 - val_accuracy: 0.9645
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9764 - val_loss: 0.1869 - val_accuracy: 0.9619
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9773 - val_loss: 0.1792 - val_accuracy: 0.9626
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9772 - val_loss: 0.1794 - val_accuracy: 0.9642
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1153 - accuracy: 0.9779 - val_loss: 0.1855 - val_accuracy: 0.9598
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9772 - val_loss: 0.1832 - val_accuracy: 0.9621
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1150 - accuracy: 0.9776 - val_loss: 0.1719 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9766 - val_loss: 0.1692 - val_accuracy: 0.9657
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1158 - accuracy: 0.9782 - val_loss: 0.1690 - val_accuracy: 0.9656
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9769 - val_loss: 0.1756 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1150 - accuracy: 0.9775 - val_loss: 0.1868 - val_accuracy: 0.9620
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1156 - accuracy: 0.9778 - val_loss: 0.1783 - val_accuracy: 0.9618
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1149 - accuracy: 0.9774 - val_loss: 0.1822 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1157 - accuracy: 0.9769 - val_loss: 0.1727 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9772 - val_loss: 0.1744 - val_accuracy: 0.9656
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1159 - accuracy: 0.9772 - val_loss: 0.1791 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1137 - accuracy: 0.9780 - val_loss: 0.1823 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1168 - accuracy: 0.9765 - val_loss: 0.1726 - val_accuracy: 0.9634
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1164 - accuracy: 0.9770 - val_loss: 0.1847 - val_accuracy: 0.9614
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1150 - accuracy: 0.9779 - val_loss: 0.1756 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9770 - val_loss: 0.1755 - val_accuracy: 0.9636
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1155 - accuracy: 0.9771 - val_loss: 0.1810 - val_accuracy: 0.9634
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9779 - val_loss: 0.1736 - val_accuracy: 0.9647
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1137 - accuracy: 0.9784 - val_loss: 0.1819 - val_accuracy: 0.9620
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1156 - accuracy: 0.9776 - val_loss: 0.1742 - val_accuracy: 0.9626
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1141 - accuracy: 0.9778 - val_loss: 0.1705 - val_accuracy: 0.9649
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1154 - accuracy: 0.9773 - val_loss: 0.1712 - val_accuracy: 0.9653
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9762 - val_loss: 0.1878 - val_accuracy: 0.9619
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1157 - accuracy: 0.9773 - val_loss: 0.1732 - val_accuracy: 0.9624
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9736 - val_loss: 0.1699 - val_accuracy: 0.9651
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9769 - val_loss: 0.1803 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1148 - accuracy: 0.9767 - val_loss: 0.1741 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1152 - accuracy: 0.9763 - val_loss: 0.1744 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1137 - accuracy: 0.9765 - val_loss: 0.1802 - val_accuracy: 0.9616
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9768 - val_loss: 0.1721 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1136 - accuracy: 0.9765 - val_loss: 0.1726 - val_accuracy: 0.9639
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1131 - accuracy: 0.9771 - val_loss: 0.1826 - val_accuracy: 0.9607
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1120 - accuracy: 0.9771 - val_loss: 0.1743 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1129 - accuracy: 0.9764 - val_loss: 0.1632 - val_accuracy: 0.9656
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1122 - accuracy: 0.9769 - val_loss: 0.1715 - val_accuracy: 0.9639
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1122 - accuracy: 0.9772 - val_loss: 0.1849 - val_accuracy: 0.9603
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1125 - accuracy: 0.9770 - val_loss: 0.1674 - val_accuracy: 0.9627
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1123 - accuracy: 0.9765 - val_loss: 0.1756 - val_accuracy: 0.9626
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1109 - accuracy: 0.9770 - val_loss: 0.1766 - val_accuracy: 0.9620
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1114 - accuracy: 0.9770 - val_loss: 0.1801 - val_accuracy: 0.9611
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1096 - accuracy: 0.9779 - val_loss: 0.1776 - val_accuracy: 0.9614
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1106 - accuracy: 0.9777 - val_loss: 0.1658 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1108 - accuracy: 0.9767 - val_loss: 0.1748 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1102 - accuracy: 0.9776 - val_loss: 0.1671 - val_accuracy: 0.9632
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1102 - accuracy: 0.9776 - val_loss: 0.1678 - val_accuracy: 0.9661
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1097 - accuracy: 0.9773 - val_loss: 0.1683 - val_accuracy: 0.9643
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1104 - accuracy: 0.9769 - val_loss: 0.1786 - val_accuracy: 0.9632
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1096 - accuracy: 0.9777 - val_loss: 0.1863 - val_accuracy: 0.9586
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1100 - accuracy: 0.9771 - val_loss: 0.1667 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1099 - accuracy: 0.9768 - val_loss: 0.1692 - val_accuracy: 0.9637
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1108 - accuracy: 0.9767 - val_loss: 0.1776 - val_accuracy: 0.9612
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1094 - accuracy: 0.9770 - val_loss: 0.1758 - val_accuracy: 0.9627
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1096 - accuracy: 0.9774 - val_loss: 0.1717 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1096 - accuracy: 0.9772 - val_loss: 0.1671 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1101 - accuracy: 0.9769 - val_loss: 0.1686 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1093 - accuracy: 0.9773 - val_loss: 0.1765 - val_accuracy: 0.9634
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1100 - accuracy: 0.9772 - val_loss: 0.1667 - val_accuracy: 0.9657
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1090 - accuracy: 0.9774 - val_loss: 0.1720 - val_accuracy: 0.9645
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1091 - accuracy: 0.9776 - val_loss: 0.1760 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1095 - accuracy: 0.9772 - val_loss: 0.1662 - val_accuracy: 0.9651
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1095 - accuracy: 0.9770 - val_loss: 0.1749 - val_accuracy: 0.9625
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1097 - accuracy: 0.9775 - val_loss: 0.1704 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1105 - accuracy: 0.9768 - val_loss: 0.1750 - val_accuracy: 0.9633
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1093 - accuracy: 0.9772 - val_loss: 0.1748 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1092 - accuracy: 0.9771 - val_loss: 0.1829 - val_accuracy: 0.9603
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1090 - accuracy: 0.9777 - val_loss: 0.1802 - val_accuracy: 0.9609
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1089 - accuracy: 0.9775 - val_loss: 0.1720 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1092 - accuracy: 0.9771 - val_loss: 0.1682 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1098 - accuracy: 0.9773 - val_loss: 0.1703 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1089 - accuracy: 0.9776 - val_loss: 0.1673 - val_accuracy: 0.9640
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1085 - accuracy: 0.9776 - val_loss: 0.1645 - val_accuracy: 0.9655
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1098 - accuracy: 0.9765 - val_loss: 0.1669 - val_accuracy: 0.9634
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1091 - accuracy: 0.9770 - val_loss: 0.1730 - val_accuracy: 0.9633
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1083 - accuracy: 0.9774 - val_loss: 0.1729 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1097 - accuracy: 0.9768 - val_loss: 0.1700 - val_accuracy: 0.9639
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1085 - accuracy: 0.9777 - val_loss: 0.1708 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1105 - accuracy: 0.9768 - val_loss: 0.1714 - val_accuracy: 0.9637
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1089 - accuracy: 0.9779 - val_loss: 0.1717 - val_accuracy: 0.9623
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1088 - accuracy: 0.9774 - val_loss: 0.1802 - val_accuracy: 0.9624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1082 - accuracy: 0.9771 - val_loss: 0.1754 - val_accuracy: 0.9625
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1103 - accuracy: 0.9768 - val_loss: 0.1724 - val_accuracy: 0.9636
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1106 - accuracy: 0.9763 - val_loss: 0.1655 - val_accuracy: 0.9645
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1097 - accuracy: 0.9775 - val_loss: 0.1621 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1088 - accuracy: 0.9769 - val_loss: 0.1770 - val_accuracy: 0.9615
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1094 - accuracy: 0.9779 - val_loss: 0.1598 - val_accuracy: 0.9656
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1093 - accuracy: 0.9775 - val_loss: 0.1592 - val_accuracy: 0.9659
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1089 - accuracy: 0.9773 - val_loss: 0.1668 - val_accuracy: 0.9642
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1087 - accuracy: 0.9773 - val_loss: 0.1791 - val_accuracy: 0.9639
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1094 - accuracy: 0.9770 - val_loss: 0.1834 - val_accuracy: 0.9604
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1087 - accuracy: 0.9772 - val_loss: 0.1736 - val_accuracy: 0.9622
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1086 - accuracy: 0.9772 - val_loss: 0.1776 - val_accuracy: 0.9618
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1089 - accuracy: 0.9775 - val_loss: 0.1708 - val_accuracy: 0.9621
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1084 - accuracy: 0.9772 - val_loss: 0.1722 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1087 - accuracy: 0.9775 - val_loss: 0.1714 - val_accuracy: 0.9632
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1099 - accuracy: 0.9770 - val_loss: 0.1685 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1095 - accuracy: 0.9765 - val_loss: 0.1749 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1086 - accuracy: 0.9772 - val_loss: 0.1639 - val_accuracy: 0.9654
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1084 - accuracy: 0.9774 - val_loss: 0.1727 - val_accuracy: 0.9619
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1082 - accuracy: 0.9773 - val_loss: 0.1760 - val_accuracy: 0.9627
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1087 - accuracy: 0.9772 - val_loss: 0.1704 - val_accuracy: 0.9640
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1086 - accuracy: 0.9779 - val_loss: 0.1692 - val_accuracy: 0.9632
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1081 - accuracy: 0.9777 - val_loss: 0.1685 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1088 - accuracy: 0.9770 - val_loss: 0.1702 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1103 - accuracy: 0.9768 - val_loss: 0.1688 - val_accuracy: 0.9632
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1079 - accuracy: 0.9779 - val_loss: 0.1677 - val_accuracy: 0.9636
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1078 - accuracy: 0.9771 - val_loss: 0.1607 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1088 - accuracy: 0.9769 - val_loss: 0.1693 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1077 - accuracy: 0.9777 - val_loss: 0.1655 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1091 - accuracy: 0.9769 - val_loss: 0.1654 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1078 - accuracy: 0.9774 - val_loss: 0.1662 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1084 - accuracy: 0.9775 - val_loss: 0.1661 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1089 - accuracy: 0.9773 - val_loss: 0.1613 - val_accuracy: 0.9661
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1081 - accuracy: 0.9768 - val_loss: 0.1665 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1079 - accuracy: 0.9774 - val_loss: 0.1668 - val_accuracy: 0.9636
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1081 - accuracy: 0.9777 - val_loss: 0.1657 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1078 - accuracy: 0.9772 - val_loss: 0.1693 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1085 - accuracy: 0.9776 - val_loss: 0.1690 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1086 - accuracy: 0.9770 - val_loss: 0.1662 - val_accuracy: 0.9654
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1080 - accuracy: 0.9778 - val_loss: 0.1663 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1072 - accuracy: 0.9781 - val_loss: 0.1678 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1094 - accuracy: 0.9764 - val_loss: 0.1645 - val_accuracy: 0.9642
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1069 - accuracy: 0.9774 - val_loss: 0.1633 - val_accuracy: 0.9653
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1071 - accuracy: 0.9772 - val_loss: 0.1630 - val_accuracy: 0.9661
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1083 - accuracy: 0.9772 - val_loss: 0.1695 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 5s 15ms/step - loss: 9.3331e-04 - accuracy: 0.9997 - val_loss: 0.0963 - val_accuracy: 0.9812
[-0.          0.         -0.         ...  0.5728107  -0.6194763
 -0.34995005]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5382e-04 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.5811358  -0.62145585
 -0.34729576]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1270e-04 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9831
[-0.         0.        -0.        ...  0.5845508 -0.6308096 -0.3416493]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1033e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.58650583 -0.6309471
 -0.34129578]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9506e-05 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.58817655 -0.63367796
 -0.35052896]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3892e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9830
[-0.          0.         -0.         ...  0.5890956  -0.63567436
 -0.3518097 ]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6770e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9834
[-0.          0.         -0.         ...  0.59242564 -0.63557327
 -0.35123745]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1067e-05 - accuracy: 1.0000 - val_loss: 0.0959 - val_accuracy: 0.9831
[-0.          0.         -0.         ...  0.5933231  -0.64107764
 -0.3517232 ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7829e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9835
[-0.          0.         -0.         ...  0.5950826  -0.6455001
 -0.35310227]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9340e-05 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9836
[-0.          0.         -0.         ...  0.5969851  -0.64611626
 -0.35453779]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1802e-04 - accuracy: 0.9999 - val_loss: 0.1043 - val_accuracy: 0.9826
[-0.          0.         -0.         ...  0.59762824 -0.6596632
 -0.35390502]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6295e-04 - accuracy: 0.9999 - val_loss: 0.1089 - val_accuracy: 0.9827
[-0.          0.         -0.         ...  0.60045296 -0.69052464
 -0.32379308]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6196e-04 - accuracy: 0.9999 - val_loss: 0.1081 - val_accuracy: 0.9828
[-0.          0.         -0.         ...  0.63626266 -0.6899871
 -0.34524125]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2674e-04 - accuracy: 0.9999 - val_loss: 0.1055 - val_accuracy: 0.9831
[-0.          0.         -0.         ...  0.63799417 -0.68720376
 -0.34359086]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3679e-04 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.64237183 -0.68737996
 -0.3410983 ]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6625e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9834
[-0.          0.         -0.         ...  0.6461495  -0.68922085
 -0.3433997 ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3966e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9832
[-0.          0.         -0.         ...  0.6486699  -0.69084466
 -0.3469219 ]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8556e-04 - accuracy: 0.9999 - val_loss: 0.1090 - val_accuracy: 0.9820
[-0.          0.         -0.         ...  0.6470774  -0.69216007
 -0.33084062]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 6.5657e-04 - accuracy: 0.9998 - val_loss: 0.1073 - val_accuracy: 0.9830
[-0.          0.         -0.         ...  0.64972854 -0.6898631
 -0.32484606]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2469e-04 - accuracy: 0.9999 - val_loss: 0.1132 - val_accuracy: 0.9824
[-0.          0.         -0.         ...  0.62412965 -0.69792885
 -0.30385524]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4485e-04 - accuracy: 0.9998 - val_loss: 0.1153 - val_accuracy: 0.9818
[-0.          0.         -0.         ...  0.6229628  -0.6654983
 -0.31234008]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8763e-04 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9822
[-0.          0.         -0.         ...  0.6175275  -0.6579139
 -0.31474936]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2513e-05 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9818
[-0.          0.         -0.         ...  0.6234867  -0.63990015
 -0.3232531 ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3872e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9822
[-0.          0.         -0.         ...  0.6274907  -0.64659023
 -0.32716078]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3270e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9826
[-0.          0.         -0.         ...  0.62874603 -0.6493433
 -0.32712048]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3714e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9812
[-0.          0.         -0.         ...  0.63365585 -0.65055966
 -0.32116568]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7069e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9813
[-0.          0.         -0.         ...  0.6328648  -0.65292984
 -0.3230161 ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1656e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9814
[-0.          0.         -0.         ...  0.6346627  -0.6539204
 -0.32331395]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0874e-06 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9815
[-0.          0.         -0.         ...  0.6369615  -0.65541726
 -0.3234316 ]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 8.9655e-06 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9816
[-0.          0.         -0.         ...  0.6373124  -0.65784353
 -0.32275504]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 7.9628e-06 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9815
[-0.          0.         -0.         ...  0.6411442  -0.65870833
 -0.32401726]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 6.6336e-06 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.6473526  -0.66045934
 -0.32407165]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3290e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9819
[-0.          0.         -0.         ...  0.6489468  -0.66235393
 -0.3240567 ]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 5.7615e-06 - accuracy: 1.0000 - val_loss: 0.1115 - val_accuracy: 0.9819
[-0.          0.         -0.         ...  0.64948905 -0.6630712
 -0.32466218]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4540e-06 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9815
[-0.          0.         -0.         ...  0.65234184 -0.663413
 -0.32477307]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1417e-06 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9820
[-0.          0.         -0.         ...  0.6529727  -0.66734636
 -0.32475352]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0414e-06 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9821
[-0.         0.        -0.        ...  0.6502373 -0.6688133 -0.3254238]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9960e-06 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9822
[-0.          0.         -0.         ...  0.65443945 -0.6701251
 -0.32661685]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6559e-06 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9823
[-0.          0.         -0.         ...  0.65405536 -0.67231345
 -0.32769272]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1687e-06 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9823
[-0.          0.         -0.         ...  0.6547837  -0.675713
 -0.32839793]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2624e-06 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9826
[-0.          0.         -0.         ...  0.6563494  -0.6817796
 -0.32847393]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2849e-06 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9828
[-0.          0.         -0.         ...  0.6584201  -0.6855066
 -0.32805812]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3630e-06 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9832
[-0.          0.         -0.         ...  0.66052485 -0.686105
 -0.32879087]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2724e-06 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.65127695 -0.6895687
 -0.3293585 ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0209e-06 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9827
[-0.          0.         -0.         ...  0.6537033  -0.69137114
 -0.3300522 ]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7297e-06 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.6557985  -0.6974854
 -0.33050433]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5521e-06 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9831
[-0.         0.        -0.        ...  0.6577344 -0.7002139 -0.3307969]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6236e-06 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9831
[-0.          0.         -0.         ...  0.66088337 -0.6997571
 -0.33187795]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3420e-06 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.6629594  -0.7033369
 -0.33326378]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3051e-06 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9835
[-0.          0.         -0.         ...  0.66472346 -0.70540833
 -0.33328238]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9987 - val_loss: 0.1142 - val_accuracy: 0.9809
[-0.          0.         -0.         ...  0.67267823 -0.6694673
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2343e-04 - accuracy: 0.9999 - val_loss: 0.1156 - val_accuracy: 0.9814
[-0.         0.        -0.        ...  0.6750742 -0.6694366 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5477e-04 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9815
[-0.          0.         -0.         ...  0.67589384 -0.6586552
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 8.8033e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9813
[-0.          0.         -0.         ...  0.67959446 -0.664264
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 7.0351e-05 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9814
[-0.          0.         -0.         ...  0.68111336 -0.6574598
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4314e-05 - accuracy: 1.0000 - val_loss: 0.1115 - val_accuracy: 0.9814
[-0.          0.         -0.         ...  0.6823232  -0.65832376
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3096e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9813
[-0.          0.         -0.         ...  0.6837406  -0.65799636
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0015e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9812
[-0.          0.         -0.         ...  0.6841942  -0.65819967
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0865e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9816
[-0.          0.         -0.         ...  0.6842039  -0.65945446
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5436e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.6856578  -0.65961194
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9722e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9819
[-0.         0.        -0.        ...  0.6870233 -0.6593791 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7636e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9820
[-0.          0.         -0.         ...  0.68911314 -0.65971917
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2463e-05 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9816
[-0.          0.         -0.         ...  0.6898468  -0.65928704
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0311e-05 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9818
[-0.         0.        -0.        ...  0.6919321 -0.6597065 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5163e-05 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.69464403 -0.6592978
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5711e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9818
[-0.         0.        -0.        ...  0.6958347 -0.6609568 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2783e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9821
[-0.         0.        -0.        ...  0.6976866 -0.6617891  0.       ]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0779e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9820
[-0.          0.         -0.         ...  0.7008152  -0.66302764
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1982e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9817
[-0.         0.        -0.        ...  0.7011633 -0.6635848 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5149e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9816
[-0.          0.         -0.         ...  0.7069478  -0.66461915
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0693e-05 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.70389295 -0.66573805
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0642e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9816
[-0.         0.        -0.        ...  0.7057545 -0.6640868  0.       ]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5360e-06 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9818
[-0.          0.         -0.         ...  0.7102162  -0.66545075
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7784e-06 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9817
[-0.         0.        -0.        ...  0.7119753 -0.665771  -0.       ]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7769e-06 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9826
[-0.          0.         -0.         ...  0.7098874  -0.66466635
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2641e-06 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9828
[-0.          0.         -0.         ...  0.71202105 -0.6655856
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4936e-06 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9825
[-0.          0.         -0.         ...  0.71641064 -0.66721
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6450e-06 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9824
[-0.          0.         -0.         ...  0.7192422  -0.66830343
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2055e-06 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9824
[-0.          0.         -0.         ...  0.7219333  -0.67022395
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9112e-06 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9823
[-0.          0.         -0.         ...  0.72621113 -0.67057467
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7637e-06 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9826
[-0.         0.        -0.        ...  0.727075  -0.6721101 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5869e-06 - accuracy: 1.0000 - val_loss: 0.1179 - val_accuracy: 0.9824
[-0.          0.         -0.         ...  0.72855395 -0.6740664
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7235e-06 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9824
[-0.         0.        -0.        ...  0.7290966 -0.6759711 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3406e-06 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9826
[-0.          0.         -0.         ...  0.7085638  -0.67781353
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0892e-04 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.70539594 -0.6756918
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0508e-04 - accuracy: 0.9998 - val_loss: 0.1336 - val_accuracy: 0.9806
[-0.         0.        -0.        ...  0.7289927 -0.7011832 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 9.1676e-04 - accuracy: 0.9997 - val_loss: 0.1304 - val_accuracy: 0.9818
[-0.          0.         -0.         ...  0.69481236 -0.68962526
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2219e-04 - accuracy: 0.9998 - val_loss: 0.1232 - val_accuracy: 0.9831
[-0.         0.        -0.        ...  0.7086477 -0.6901053  0.       ]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7720e-05 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9824
[-0.          0.         -0.         ...  0.71027124 -0.68777096
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7224e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9822
[-0.         0.        -0.        ...  0.7110911 -0.6880556 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1619e-05 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9821
[-0.          0.         -0.         ...  0.71161515 -0.68817335
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5976e-05 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9824
[-0.         0.        -0.        ...  0.7118119 -0.6930628 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3374e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9828
[-0.          0.         -0.         ...  0.71268284 -0.6952215
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 9.4212e-06 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9829
[-0.         0.        -0.        ...  0.7130383 -0.6965197 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0812e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9827
[-0.         0.        -0.        ...  0.7131864 -0.6953783 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6503e-04 - accuracy: 0.9999 - val_loss: 0.1279 - val_accuracy: 0.9828
[-0.          0.         -0.         ...  0.7134659  -0.69130635
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1213e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9831
[-0.          0.         -0.         ...  0.711992   -0.69202596
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1584e-06 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9832
[-0.          0.         -0.         ...  0.71246517 -0.69141304
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 6.5545e-06 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9834
[-0.          0.         -0.         ...  0.7131963  -0.69123703
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1739e-06 - accuracy: 1.0000 - val_loss: 0.1247 - val_accuracy: 0.9836
[-0.          0.         -0.         ...  0.71325    -0.69116396
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0104 - accuracy: 0.9967 - val_loss: 0.1108 - val_accuracy: 0.9806
[-0.         0.        -0.        ...  0.6110119 -0.6858295  0.       ]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1105 - val_accuracy: 0.9808
[-0.          0.         -0.         ...  0.63798857 -0.6979264
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1219e-04 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9812
[-0.         0.        -0.        ...  0.6407899 -0.6906702  0.       ]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6631e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9813
[-0.         0.        -0.        ...  0.6435331 -0.6927668 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9895e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9821
[-0.          0.         -0.         ...  0.64305925 -0.69544995
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7461e-04 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9812
[-0.         0.        -0.        ...  0.640739  -0.6956936  0.       ]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7881e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.64870965 -0.6990817
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3145e-04 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.65189624 -0.6992605
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5564e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9813
[-0.          0.         -0.         ...  0.65277624 -0.7000053
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2340e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9819
[-0.          0.         -0.         ...  0.65494007 -0.6991881
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2995e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9819
[-0.          0.         -0.         ...  0.65609974 -0.6998431
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 4s 15ms/step - loss: 8.2249e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9818
[-0.          0.         -0.         ...  0.65814567 -0.69843817
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 9.2097e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9820
[-0.          0.         -0.         ...  0.6591217  -0.70076704
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 6.5924e-05 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9824
[-0.         0.        -0.        ...  0.6605608 -0.7008054 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 6.8858e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9821
[-0.         0.        -0.        ...  0.663057  -0.7001511  0.       ]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2744e-05 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.6676221  -0.70617723
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 4s 17ms/step - loss: 4.6379e-05 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9818
[-0.          0.         -0.         ...  0.67018235 -0.7089968
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7506e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.67002004 -0.70976394
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7285e-05 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9820
[-0.          0.         -0.         ...  0.6723043  -0.71164054
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2465e-05 - accuracy: 1.0000 - val_loss: 0.1118 - val_accuracy: 0.9824
[-0.          0.         -0.         ...  0.675022   -0.71514684
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5052e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9819
[-0.          0.         -0.         ...  0.6752753  -0.72023225
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0463e-05 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.67813927 -0.7205321
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4964e-05 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9825
[-0.         0.        -0.        ...  0.6758883 -0.7230654  0.       ]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0061e-05 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9826
[-0.         0.        -0.        ...  0.6784352 -0.7260054  0.       ]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7300e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9825
[-0.          0.         -0.         ...  0.68057126 -0.7288986
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 6.6649e-05 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9822
[-0.          0.         -0.         ...  0.68047976 -0.73600656
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9053e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9824
[-0.          0.         -0.         ...  0.6839424  -0.74509305
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9285e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9827
[-0.          0.         -0.         ...  0.68527794 -0.7477193
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6842e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.6905687  -0.74836195
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8267e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9827
[-0.         0.        -0.        ...  0.6902968 -0.7543926  0.       ]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9052e-05 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9838
[-0.          0.         -0.         ...  0.6981326  -0.75526774
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2835e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9837
[-0.         0.        -0.        ...  0.6737219 -0.7577034  0.       ]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2843e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9839
[-0.         0.        -0.        ...  0.6510969 -0.7578433  0.       ]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6188e-04 - accuracy: 0.9999 - val_loss: 0.1189 - val_accuracy: 0.9835
[-0.         0.        -0.        ...  0.6972773 -0.7576598  0.       ]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7531e-04 - accuracy: 0.9999 - val_loss: 0.1187 - val_accuracy: 0.9821
[-0.          0.         -0.         ...  0.6902221  -0.76433235
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5578e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9826
[-0.          0.         -0.         ...  0.6930773  -0.77579236
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6565e-05 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9825
[-0.         0.        -0.        ...  0.6931349 -0.7786544  0.       ]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1491e-05 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9826
[-0.          0.         -0.         ...  0.69264024 -0.76636523
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 9.8180e-06 - accuracy: 1.0000 - val_loss: 0.1191 - val_accuracy: 0.9827
[-0.          0.         -0.         ...  0.6935656  -0.77079767
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5495e-05 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9829
[-0.         0.        -0.        ...  0.6879964 -0.7699719 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1827e-04 - accuracy: 0.9999 - val_loss: 0.1248 - val_accuracy: 0.9824
[-0.          0.         -0.         ...  0.7046541  -0.81172335
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7369e-04 - accuracy: 1.0000 - val_loss: 0.1251 - val_accuracy: 0.9827
[-0.          0.         -0.         ...  0.70606863 -0.83346945
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6858e-05 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9826
[-0.         0.        -0.        ...  0.7075756 -0.8355491 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2029e-05 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9829
[-0.          0.         -0.         ...  0.70732874 -0.831987
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5877e-06 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9830
[-0.          0.         -0.         ...  0.7074176  -0.83183855
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4195e-06 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9827
[-0.          0.         -0.         ...  0.7074956  -0.83091474
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5892e-06 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9827
[-0.         0.        -0.        ...  0.7075618 -0.8297385 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2573e-06 - accuracy: 1.0000 - val_loss: 0.1269 - val_accuracy: 0.9827
[-0.         0.        -0.        ...  0.7080372 -0.8293254 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5151e-06 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9826
[-0.         0.        -0.        ...  0.7085081 -0.8294451 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1128e-06 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9828
[-0.          0.         -0.         ...  0.7086039  -0.83034563
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0252 - accuracy: 0.9921 - val_loss: 0.1123 - val_accuracy: 0.9785
[-0.         0.        -0.        ... -0.        -0.7440849 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 0.1080 - val_accuracy: 0.9790
[-0.         0.        -0.        ...  0.        -0.7526541 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9998 - val_loss: 0.1076 - val_accuracy: 0.9789
[-0.          0.         -0.         ...  0.         -0.75774723
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1078 - val_accuracy: 0.9798
[-0.          0.         -0.         ...  0.         -0.75869465
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 9.8900e-04 - accuracy: 0.9999 - val_loss: 0.1062 - val_accuracy: 0.9806
[-0.         0.        -0.        ...  0.        -0.7609396 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5973e-04 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9806
[-0.         0.        -0.        ... -0.        -0.7665332  0.       ]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4631e-04 - accuracy: 0.9999 - val_loss: 0.1062 - val_accuracy: 0.9805
[-0.         0.        -0.        ...  0.        -0.7592011  0.       ]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1691e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9814
[-0.          0.         -0.         ...  0.         -0.76015586
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1296e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9804
[-0.         0.        -0.        ...  0.        -0.7656703 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2932e-04 - accuracy: 0.9999 - val_loss: 0.1090 - val_accuracy: 0.9803
[-0.         0.        -0.        ... -0.        -0.7622859 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3926e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9808
[-0.          0.         -0.         ...  0.         -0.77538466
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1746e-04 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9810
[-0.         0.        -0.        ...  0.        -0.7781318 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6398e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9808
[-0.          0.         -0.         ...  0.         -0.78141093
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3711e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9810
[-0.          0.         -0.         ...  0.         -0.79104614
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7049e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9806
[-0.          0.         -0.         ...  0.         -0.79307246
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1104e-04 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9808
[-0.        0.       -0.       ...  0.       -0.793305 -0.      ]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 4s 16ms/step - loss: 1.6608e-04 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9816
[-0.          0.         -0.         ...  0.         -0.79998714
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 4s 16ms/step - loss: 1.4595e-04 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9815
[-0.          0.         -0.         ...  0.         -0.80366963
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4174e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9816
[-0.         0.        -0.        ...  0.        -0.8007117 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2445e-04 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9813
[-0.         0.        -0.        ...  0.        -0.8061623 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8029e-04 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9816
[-0.         0.        -0.        ...  0.        -0.8125732 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2205e-04 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9812
[-0.          0.         -0.         ...  0.         -0.80843836
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2125e-04 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9821
[-0.        0.       -0.       ...  0.       -0.811196 -0.      ]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5808e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9822
[-0.          0.         -0.         ... -0.         -0.80934453
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 8.9900e-05 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9821
[-0.         0.        -0.        ...  0.        -0.8203639 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2783e-05 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9828
[-0.         0.        -0.        ... -0.        -0.8211397  0.       ]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 5.9989e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9825
[-0.         0.        -0.        ... -0.        -0.8268195 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 15ms/step - loss: 5.5061e-05 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9825
[-0.         0.        -0.        ...  0.        -0.8345771 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7776e-05 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9824
[-0.         0.        -0.        ... -0.        -0.8287348 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0984e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9827
[-0.         0.        -0.        ... -0.        -0.8321112  0.       ]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4910e-05 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9822
[-0.          0.         -0.         ... -0.         -0.83348036
  0.        ]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4088e-05 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9820
[-0.          0.         -0.         ...  0.         -0.83803266
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8119e-05 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9816
[-0.         0.        -0.        ... -0.        -0.8416033 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 8.3554e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9814
[-0.         0.        -0.        ...  0.        -0.8419356 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0285e-05 - accuracy: 1.0000 - val_loss: 0.1234 - val_accuracy: 0.9826
[-0.         0.        -0.        ...  0.        -0.8441631  0.       ]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3261e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9817
[-0.         0.        -0.        ... -0.        -0.8416663 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7940e-05 - accuracy: 1.0000 - val_loss: 0.1254 - val_accuracy: 0.9816
[-0.         0.        -0.        ...  0.        -0.8414333  0.       ]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6141e-04 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9821
[-0.         0.        -0.        ... -0.        -0.8852608 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8587e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9822
[-0.          0.         -0.         ...  0.         -0.88370085
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1298e-04 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9818
[-0.         0.        -0.        ... -0.        -0.8710609 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8512e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9816
[-0.          0.         -0.         ... -0.         -0.88074225
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7639e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9814
[-0.         0.        -0.        ... -0.        -0.8799711 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0734e-05 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9821
[-0.         0.        -0.        ...  0.        -0.8837086 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 9.4109e-05 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9811
[-0.         0.        -0.        ... -0.        -0.8808844 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2703e-05 - accuracy: 1.0000 - val_loss: 0.1303 - val_accuracy: 0.9815
[-0.          0.         -0.         ... -0.         -0.87856007
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8782e-05 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9817
[-0.          0.         -0.         ...  0.         -0.88340646
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8361e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9818
[-0.          0.         -0.         ...  0.         -0.88370496
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5962e-05 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9815
[-0.         0.        -0.        ...  0.        -0.8791857 -0.       ]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1569e-05 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9814
[-0.          0.         -0.         ...  0.         -0.88159984
 -0.        ]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7513e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9816
[-0.         0.        -0.        ... -0.        -0.8855052  0.       ]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0740 - accuracy: 0.9809 - val_loss: 0.1357 - val_accuracy: 0.9722
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0228 - accuracy: 0.9925 - val_loss: 0.1260 - val_accuracy: 0.9753
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0141 - accuracy: 0.9957 - val_loss: 0.1226 - val_accuracy: 0.9756
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0103 - accuracy: 0.9969 - val_loss: 0.1192 - val_accuracy: 0.9761
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9979 - val_loss: 0.1180 - val_accuracy: 0.9767
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9982 - val_loss: 0.1174 - val_accuracy: 0.9769
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9988 - val_loss: 0.1172 - val_accuracy: 0.9772
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0044 - accuracy: 0.9992 - val_loss: 0.1161 - val_accuracy: 0.9767
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 0.1173 - val_accuracy: 0.9771
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.1178 - val_accuracy: 0.9774
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1178 - val_accuracy: 0.9778
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9998 - val_loss: 0.1184 - val_accuracy: 0.9779
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9998 - val_loss: 0.1179 - val_accuracy: 0.9782
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0018 - accuracy: 0.9998 - val_loss: 0.1189 - val_accuracy: 0.9782
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.1196 - val_accuracy: 0.9787
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1218 - val_accuracy: 0.9783
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1218 - val_accuracy: 0.9785
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9792
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 9.6738e-04 - accuracy: 1.0000 - val_loss: 0.1251 - val_accuracy: 0.9784
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5441e-04 - accuracy: 0.9999 - val_loss: 0.1244 - val_accuracy: 0.9787
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 8.9439e-04 - accuracy: 1.0000 - val_loss: 0.1274 - val_accuracy: 0.9788
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7263e-04 - accuracy: 1.0000 - val_loss: 0.1279 - val_accuracy: 0.9784
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1095e-04 - accuracy: 1.0000 - val_loss: 0.1282 - val_accuracy: 0.9784
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7242e-04 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9781
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 6.5615e-04 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9791
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0292e-04 - accuracy: 1.0000 - val_loss: 0.1291 - val_accuracy: 0.9783
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8570e-04 - accuracy: 1.0000 - val_loss: 0.1280 - val_accuracy: 0.9790
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6339e-04 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9781
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4515e-04 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9793
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8250e-04 - accuracy: 1.0000 - val_loss: 0.1307 - val_accuracy: 0.9795
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2899e-04 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9787
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2006e-04 - accuracy: 0.9999 - val_loss: 0.1339 - val_accuracy: 0.9788
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8067e-04 - accuracy: 1.0000 - val_loss: 0.1350 - val_accuracy: 0.9785
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8182e-04 - accuracy: 0.9999 - val_loss: 0.1373 - val_accuracy: 0.9789
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2219e-04 - accuracy: 1.0000 - val_loss: 0.1365 - val_accuracy: 0.9788
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1484e-04 - accuracy: 1.0000 - val_loss: 0.1358 - val_accuracy: 0.9787
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6711e-04 - accuracy: 1.0000 - val_loss: 0.1383 - val_accuracy: 0.9790
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2025e-04 - accuracy: 1.0000 - val_loss: 0.1401 - val_accuracy: 0.9788
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1301e-04 - accuracy: 0.9999 - val_loss: 0.1430 - val_accuracy: 0.9781
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7922e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9787
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7816e-04 - accuracy: 1.0000 - val_loss: 0.1425 - val_accuracy: 0.9790
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5838e-04 - accuracy: 1.0000 - val_loss: 0.1417 - val_accuracy: 0.9789
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3195e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9788
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3744e-04 - accuracy: 1.0000 - val_loss: 0.1408 - val_accuracy: 0.9789
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8176e-04 - accuracy: 1.0000 - val_loss: 0.1436 - val_accuracy: 0.9789
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3314e-04 - accuracy: 1.0000 - val_loss: 0.1438 - val_accuracy: 0.9789
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0997e-04 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9782
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5966e-04 - accuracy: 1.0000 - val_loss: 0.1488 - val_accuracy: 0.9786
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0773e-04 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9788
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6043e-04 - accuracy: 0.9999 - val_loss: 0.1515 - val_accuracy: 0.9785
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1821 - accuracy: 0.9564 - val_loss: 0.1846 - val_accuracy: 0.9614
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0854 - accuracy: 0.9748 - val_loss: 0.1615 - val_accuracy: 0.9659
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0674 - accuracy: 0.9790 - val_loss: 0.1510 - val_accuracy: 0.9657
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0576 - accuracy: 0.9818 - val_loss: 0.1450 - val_accuracy: 0.9678
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0524 - accuracy: 0.9834 - val_loss: 0.1405 - val_accuracy: 0.9683
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0467 - accuracy: 0.9848 - val_loss: 0.1373 - val_accuracy: 0.9687
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0428 - accuracy: 0.9864 - val_loss: 0.1355 - val_accuracy: 0.9696
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0397 - accuracy: 0.9872 - val_loss: 0.1337 - val_accuracy: 0.9697
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0368 - accuracy: 0.9880 - val_loss: 0.1328 - val_accuracy: 0.9702
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0345 - accuracy: 0.9886 - val_loss: 0.1313 - val_accuracy: 0.9712
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0320 - accuracy: 0.9898 - val_loss: 0.1299 - val_accuracy: 0.9708
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0306 - accuracy: 0.9899 - val_loss: 0.1291 - val_accuracy: 0.9711
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0286 - accuracy: 0.9907 - val_loss: 0.1292 - val_accuracy: 0.9710
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0265 - accuracy: 0.9916 - val_loss: 0.1286 - val_accuracy: 0.9713
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0254 - accuracy: 0.9918 - val_loss: 0.1284 - val_accuracy: 0.9709
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0240 - accuracy: 0.9927 - val_loss: 0.1288 - val_accuracy: 0.9714
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0222 - accuracy: 0.9932 - val_loss: 0.1284 - val_accuracy: 0.9718
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0216 - accuracy: 0.9936 - val_loss: 0.1290 - val_accuracy: 0.9726
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0203 - accuracy: 0.9940 - val_loss: 0.1298 - val_accuracy: 0.9721
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0196 - accuracy: 0.9944 - val_loss: 0.1305 - val_accuracy: 0.9725
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0182 - accuracy: 0.9948 - val_loss: 0.1308 - val_accuracy: 0.9723
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0170 - accuracy: 0.9952 - val_loss: 0.1315 - val_accuracy: 0.9728
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0166 - accuracy: 0.9954 - val_loss: 0.1316 - val_accuracy: 0.9725
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0156 - accuracy: 0.9955 - val_loss: 0.1340 - val_accuracy: 0.9724
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 0.1333 - val_accuracy: 0.9725
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0145 - accuracy: 0.9961 - val_loss: 0.1342 - val_accuracy: 0.9728
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0133 - accuracy: 0.9966 - val_loss: 0.1350 - val_accuracy: 0.9722
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0128 - accuracy: 0.9966 - val_loss: 0.1365 - val_accuracy: 0.9721
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0120 - accuracy: 0.9974 - val_loss: 0.1374 - val_accuracy: 0.9725
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0113 - accuracy: 0.9973 - val_loss: 0.1391 - val_accuracy: 0.9719
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0109 - accuracy: 0.9974 - val_loss: 0.1380 - val_accuracy: 0.9726
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0106 - accuracy: 0.9973 - val_loss: 0.1403 - val_accuracy: 0.9731
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9977 - val_loss: 0.1422 - val_accuracy: 0.9728
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0096 - accuracy: 0.9978 - val_loss: 0.1422 - val_accuracy: 0.9734
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 3s 12ms/step - loss: 0.0087 - accuracy: 0.9981 - val_loss: 0.1446 - val_accuracy: 0.9724
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0086 - accuracy: 0.9982 - val_loss: 0.1462 - val_accuracy: 0.9726
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 12ms/step - loss: 0.0081 - accuracy: 0.9984 - val_loss: 0.1463 - val_accuracy: 0.9724
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9981 - val_loss: 0.1470 - val_accuracy: 0.9729
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0076 - accuracy: 0.9983 - val_loss: 0.1487 - val_accuracy: 0.9730
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0071 - accuracy: 0.9985 - val_loss: 0.1502 - val_accuracy: 0.9732
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9988 - val_loss: 0.1505 - val_accuracy: 0.9730
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0064 - accuracy: 0.9988 - val_loss: 0.1528 - val_accuracy: 0.9728
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0059 - accuracy: 0.9990 - val_loss: 0.1550 - val_accuracy: 0.9728
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0056 - accuracy: 0.9989 - val_loss: 0.1547 - val_accuracy: 0.9724
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0057 - accuracy: 0.9990 - val_loss: 0.1572 - val_accuracy: 0.9714
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9989 - val_loss: 0.1588 - val_accuracy: 0.9716
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0050 - accuracy: 0.9993 - val_loss: 0.1611 - val_accuracy: 0.9720
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0049 - accuracy: 0.9992 - val_loss: 0.1613 - val_accuracy: 0.9720
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0045 - accuracy: 0.9992 - val_loss: 0.1648 - val_accuracy: 0.9719
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.1653 - val_accuracy: 0.9720
[-0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 14ms/step - loss: 0.6125 - accuracy: 0.8541 - val_loss: 0.4022 - val_accuracy: 0.8906
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.3103 - accuracy: 0.9083 - val_loss: 0.3055 - val_accuracy: 0.9151
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2572 - accuracy: 0.9220 - val_loss: 0.2715 - val_accuracy: 0.9238
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2317 - accuracy: 0.9297 - val_loss: 0.2528 - val_accuracy: 0.9277
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2174 - accuracy: 0.9332 - val_loss: 0.2404 - val_accuracy: 0.9310
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2076 - accuracy: 0.9363 - val_loss: 0.2311 - val_accuracy: 0.9343
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1987 - accuracy: 0.9388 - val_loss: 0.2242 - val_accuracy: 0.9372
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1918 - accuracy: 0.9411 - val_loss: 0.2181 - val_accuracy: 0.9395
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1864 - accuracy: 0.9425 - val_loss: 0.2130 - val_accuracy: 0.9407
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1806 - accuracy: 0.9444 - val_loss: 0.2088 - val_accuracy: 0.9424
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1758 - accuracy: 0.9459 - val_loss: 0.2056 - val_accuracy: 0.9429
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1719 - accuracy: 0.9467 - val_loss: 0.2030 - val_accuracy: 0.9441
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1684 - accuracy: 0.9481 - val_loss: 0.2001 - val_accuracy: 0.9450
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1641 - accuracy: 0.9492 - val_loss: 0.1979 - val_accuracy: 0.9455
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1608 - accuracy: 0.9499 - val_loss: 0.1960 - val_accuracy: 0.9458
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1593 - accuracy: 0.9511 - val_loss: 0.1941 - val_accuracy: 0.9469
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1563 - accuracy: 0.9520 - val_loss: 0.1923 - val_accuracy: 0.9473
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1545 - accuracy: 0.9525 - val_loss: 0.1911 - val_accuracy: 0.9472
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1520 - accuracy: 0.9528 - val_loss: 0.1896 - val_accuracy: 0.9474
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1495 - accuracy: 0.9538 - val_loss: 0.1881 - val_accuracy: 0.9486
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1482 - accuracy: 0.9544 - val_loss: 0.1872 - val_accuracy: 0.9494
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1469 - accuracy: 0.9545 - val_loss: 0.1866 - val_accuracy: 0.9498
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1449 - accuracy: 0.9560 - val_loss: 0.1853 - val_accuracy: 0.9500
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1432 - accuracy: 0.9561 - val_loss: 0.1842 - val_accuracy: 0.9503
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1419 - accuracy: 0.9565 - val_loss: 0.1833 - val_accuracy: 0.9509
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1405 - accuracy: 0.9570 - val_loss: 0.1824 - val_accuracy: 0.9504
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1391 - accuracy: 0.9572 - val_loss: 0.1822 - val_accuracy: 0.9506
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1376 - accuracy: 0.9576 - val_loss: 0.1817 - val_accuracy: 0.9510
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1378 - accuracy: 0.9578 - val_loss: 0.1811 - val_accuracy: 0.9514
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9584 - val_loss: 0.1806 - val_accuracy: 0.9514
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9585 - val_loss: 0.1803 - val_accuracy: 0.9516
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1336 - accuracy: 0.9586 - val_loss: 0.1800 - val_accuracy: 0.9511
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9590 - val_loss: 0.1800 - val_accuracy: 0.9513
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9599 - val_loss: 0.1792 - val_accuracy: 0.9512
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9596 - val_loss: 0.1795 - val_accuracy: 0.9511
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9598 - val_loss: 0.1791 - val_accuracy: 0.9508
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9597 - val_loss: 0.1790 - val_accuracy: 0.9514
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1282 - accuracy: 0.9604 - val_loss: 0.1788 - val_accuracy: 0.9508
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9607 - val_loss: 0.1785 - val_accuracy: 0.9508
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1263 - accuracy: 0.9614 - val_loss: 0.1781 - val_accuracy: 0.9510
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1259 - accuracy: 0.9614 - val_loss: 0.1781 - val_accuracy: 0.9506
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1244 - accuracy: 0.9617 - val_loss: 0.1776 - val_accuracy: 0.9502
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9623 - val_loss: 0.1773 - val_accuracy: 0.9502
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9621 - val_loss: 0.1773 - val_accuracy: 0.9505
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9625 - val_loss: 0.1771 - val_accuracy: 0.9510
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1212 - accuracy: 0.9630 - val_loss: 0.1772 - val_accuracy: 0.9512
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1206 - accuracy: 0.9627 - val_loss: 0.1770 - val_accuracy: 0.9511
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1197 - accuracy: 0.9634 - val_loss: 0.1770 - val_accuracy: 0.9513
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1189 - accuracy: 0.9635 - val_loss: 0.1769 - val_accuracy: 0.9517
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9630 - val_loss: 0.1769 - val_accuracy: 0.9513
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 4s 16ms/step - loss: 0.7342 - accuracy: 0.7664 - val_loss: 0.6443 - val_accuracy: 0.8018
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 3s 15ms/step - loss: 0.6177 - accuracy: 0.8041 - val_loss: 0.6057 - val_accuracy: 0.8130
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 4s 16ms/step - loss: 0.5922 - accuracy: 0.8116 - val_loss: 0.5868 - val_accuracy: 0.8170
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 3s 15ms/step - loss: 0.5757 - accuracy: 0.8165 - val_loss: 0.5735 - val_accuracy: 0.8259
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 3s 13ms/step - loss: 0.5635 - accuracy: 0.8221 - val_loss: 0.5641 - val_accuracy: 0.8295
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5555 - accuracy: 0.8238 - val_loss: 0.5571 - val_accuracy: 0.8325
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5478 - accuracy: 0.8267 - val_loss: 0.5513 - val_accuracy: 0.8341
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 15ms/step - loss: 0.5417 - accuracy: 0.8286 - val_loss: 0.5465 - val_accuracy: 0.8359
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5360 - accuracy: 0.8305 - val_loss: 0.5420 - val_accuracy: 0.8377
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5314 - accuracy: 0.8321 - val_loss: 0.5376 - val_accuracy: 0.8400
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 4s 15ms/step - loss: 0.5269 - accuracy: 0.8334 - val_loss: 0.5341 - val_accuracy: 0.8410
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 3s 15ms/step - loss: 0.5218 - accuracy: 0.8356 - val_loss: 0.5308 - val_accuracy: 0.8423
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 15ms/step - loss: 0.5201 - accuracy: 0.8366 - val_loss: 0.5279 - val_accuracy: 0.8424
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 15ms/step - loss: 0.5139 - accuracy: 0.8376 - val_loss: 0.5248 - val_accuracy: 0.8423
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5115 - accuracy: 0.8393 - val_loss: 0.5211 - val_accuracy: 0.8434
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 4s 16ms/step - loss: 0.5066 - accuracy: 0.8412 - val_loss: 0.5172 - val_accuracy: 0.8466
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5031 - accuracy: 0.8426 - val_loss: 0.5140 - val_accuracy: 0.8471
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 4s 15ms/step - loss: 0.5001 - accuracy: 0.8430 - val_loss: 0.5114 - val_accuracy: 0.8497
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4969 - accuracy: 0.8446 - val_loss: 0.5087 - val_accuracy: 0.8496
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4946 - accuracy: 0.8445 - val_loss: 0.5056 - val_accuracy: 0.8517
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4906 - accuracy: 0.8462 - val_loss: 0.5017 - val_accuracy: 0.8519
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4863 - accuracy: 0.8486 - val_loss: 0.4976 - val_accuracy: 0.8524
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4805 - accuracy: 0.8505 - val_loss: 0.4931 - val_accuracy: 0.8538
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4749 - accuracy: 0.8519 - val_loss: 0.4873 - val_accuracy: 0.8571
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4710 - accuracy: 0.8535 - val_loss: 0.4837 - val_accuracy: 0.8580
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4666 - accuracy: 0.8547 - val_loss: 0.4811 - val_accuracy: 0.8582
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4650 - accuracy: 0.8559 - val_loss: 0.4801 - val_accuracy: 0.8586
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4643 - accuracy: 0.8552 - val_loss: 0.4788 - val_accuracy: 0.8582
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4624 - accuracy: 0.8553 - val_loss: 0.4777 - val_accuracy: 0.8580
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4611 - accuracy: 0.8569 - val_loss: 0.4780 - val_accuracy: 0.8589
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4597 - accuracy: 0.8570 - val_loss: 0.4753 - val_accuracy: 0.8584
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4583 - accuracy: 0.8573 - val_loss: 0.4746 - val_accuracy: 0.8596
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4578 - accuracy: 0.8568 - val_loss: 0.4739 - val_accuracy: 0.8592
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4573 - accuracy: 0.8570 - val_loss: 0.4732 - val_accuracy: 0.8599
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 13ms/step - loss: 0.4566 - accuracy: 0.8574 - val_loss: 0.4721 - val_accuracy: 0.8601
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4562 - accuracy: 0.8579 - val_loss: 0.4711 - val_accuracy: 0.8602
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4550 - accuracy: 0.8573 - val_loss: 0.4722 - val_accuracy: 0.8606
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4543 - accuracy: 0.8579 - val_loss: 0.4711 - val_accuracy: 0.8612
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4539 - accuracy: 0.8585 - val_loss: 0.4710 - val_accuracy: 0.8611
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4531 - accuracy: 0.8587 - val_loss: 0.4702 - val_accuracy: 0.8600
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4522 - accuracy: 0.8590 - val_loss: 0.4689 - val_accuracy: 0.8618
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4519 - accuracy: 0.8590 - val_loss: 0.4680 - val_accuracy: 0.8613
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4509 - accuracy: 0.8587 - val_loss: 0.4685 - val_accuracy: 0.8614
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4506 - accuracy: 0.8599 - val_loss: 0.4676 - val_accuracy: 0.8608
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4501 - accuracy: 0.8594 - val_loss: 0.4672 - val_accuracy: 0.8612
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4491 - accuracy: 0.8601 - val_loss: 0.4659 - val_accuracy: 0.8594
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4494 - accuracy: 0.8600 - val_loss: 0.4676 - val_accuracy: 0.8612
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 4s 17ms/step - loss: 0.4495 - accuracy: 0.8595 - val_loss: 0.4684 - val_accuracy: 0.8616
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4487 - accuracy: 0.8607 - val_loss: 0.4661 - val_accuracy: 0.8607
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4492 - accuracy: 0.8593 - val_loss: 0.4654 - val_accuracy: 0.8612
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0034 - accuracy: 0.6751 - val_loss: 0.9745 - val_accuracy: 0.6446
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 3s 15ms/step - loss: 0.9290 - accuracy: 0.6858 - val_loss: 0.9438 - val_accuracy: 0.6497
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 4s 16ms/step - loss: 0.9165 - accuracy: 0.6895 - val_loss: 0.9324 - val_accuracy: 0.6510
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9125 - accuracy: 0.6906 - val_loss: 0.9235 - val_accuracy: 0.6933
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9091 - accuracy: 0.6931 - val_loss: 0.9208 - val_accuracy: 0.6942
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9070 - accuracy: 0.6932 - val_loss: 0.9181 - val_accuracy: 0.6949
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9045 - accuracy: 0.6935 - val_loss: 0.9146 - val_accuracy: 0.6962
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 15ms/step - loss: 0.9037 - accuracy: 0.6952 - val_loss: 0.9116 - val_accuracy: 0.6939
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9008 - accuracy: 0.6957 - val_loss: 0.9096 - val_accuracy: 0.6952
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8992 - accuracy: 0.6963 - val_loss: 0.9081 - val_accuracy: 0.6953
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8975 - accuracy: 0.6958 - val_loss: 0.9078 - val_accuracy: 0.6974
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8964 - accuracy: 0.6976 - val_loss: 0.9054 - val_accuracy: 0.6986
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8949 - accuracy: 0.6982 - val_loss: 0.9047 - val_accuracy: 0.6981
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8946 - accuracy: 0.6991 - val_loss: 0.9038 - val_accuracy: 0.6989
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8934 - accuracy: 0.6981 - val_loss: 0.9019 - val_accuracy: 0.6994
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8927 - accuracy: 0.6992 - val_loss: 0.9018 - val_accuracy: 0.6994
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8923 - accuracy: 0.6996 - val_loss: 0.9011 - val_accuracy: 0.7003
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8907 - accuracy: 0.7000 - val_loss: 0.9003 - val_accuracy: 0.7009
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8898 - accuracy: 0.7004 - val_loss: 0.8995 - val_accuracy: 0.7015
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8892 - accuracy: 0.7008 - val_loss: 0.8981 - val_accuracy: 0.7024
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8884 - accuracy: 0.7005 - val_loss: 0.9000 - val_accuracy: 0.7018
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8879 - accuracy: 0.7013 - val_loss: 0.8975 - val_accuracy: 0.7024
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8864 - accuracy: 0.7016 - val_loss: 0.8976 - val_accuracy: 0.7017
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8869 - accuracy: 0.7013 - val_loss: 0.8964 - val_accuracy: 0.7027
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8871 - accuracy: 0.7020 - val_loss: 0.8963 - val_accuracy: 0.7024
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8842 - accuracy: 0.7010 - val_loss: 0.8947 - val_accuracy: 0.7039
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8841 - accuracy: 0.7017 - val_loss: 0.8930 - val_accuracy: 0.7045
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8837 - accuracy: 0.7019 - val_loss: 0.8932 - val_accuracy: 0.7042
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8823 - accuracy: 0.7029 - val_loss: 0.8930 - val_accuracy: 0.7045
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8819 - accuracy: 0.7022 - val_loss: 0.8930 - val_accuracy: 0.7049
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8818 - accuracy: 0.7024 - val_loss: 0.8927 - val_accuracy: 0.7054
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8811 - accuracy: 0.7025 - val_loss: 0.8917 - val_accuracy: 0.7050
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8812 - accuracy: 0.7016 - val_loss: 0.8922 - val_accuracy: 0.7050
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8805 - accuracy: 0.7022 - val_loss: 0.8928 - val_accuracy: 0.7046
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8798 - accuracy: 0.7036 - val_loss: 0.8910 - val_accuracy: 0.7057
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8806 - accuracy: 0.7029 - val_loss: 0.8912 - val_accuracy: 0.7053
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8800 - accuracy: 0.7018 - val_loss: 0.8905 - val_accuracy: 0.7031
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8806 - accuracy: 0.7025 - val_loss: 0.8900 - val_accuracy: 0.7031
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8788 - accuracy: 0.7031 - val_loss: 0.8904 - val_accuracy: 0.7061
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8794 - accuracy: 0.7013 - val_loss: 0.8905 - val_accuracy: 0.7063
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8785 - accuracy: 0.7034 - val_loss: 0.8901 - val_accuracy: 0.7061
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8792 - accuracy: 0.7028 - val_loss: 0.8901 - val_accuracy: 0.7065
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8781 - accuracy: 0.7027 - val_loss: 0.8898 - val_accuracy: 0.7061
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8787 - accuracy: 0.7016 - val_loss: 0.8896 - val_accuracy: 0.7064
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8777 - accuracy: 0.7028 - val_loss: 0.8893 - val_accuracy: 0.7062
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8790 - accuracy: 0.7030 - val_loss: 0.8888 - val_accuracy: 0.7066
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8786 - accuracy: 0.7028 - val_loss: 0.8898 - val_accuracy: 0.7066
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8779 - accuracy: 0.7033 - val_loss: 0.8899 - val_accuracy: 0.7069
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8781 - accuracy: 0.7014 - val_loss: 0.8890 - val_accuracy: 0.7041
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 4s 17ms/step - loss: 0.8779 - accuracy: 0.7032 - val_loss: 0.8890 - val_accuracy: 0.7057
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8773 - accuracy: 0.7027 - val_loss: 0.8896 - val_accuracy: 0.7064
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8769 - accuracy: 0.7031 - val_loss: 0.8888 - val_accuracy: 0.7066
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8772 - accuracy: 0.7034 - val_loss: 0.8887 - val_accuracy: 0.7068
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8768 - accuracy: 0.7028 - val_loss: 0.8895 - val_accuracy: 0.7063
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8767 - accuracy: 0.7030 - val_loss: 0.8888 - val_accuracy: 0.7064
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8776 - accuracy: 0.7034 - val_loss: 0.8883 - val_accuracy: 0.7067
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8768 - accuracy: 0.7030 - val_loss: 0.8892 - val_accuracy: 0.7058
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8772 - accuracy: 0.7021 - val_loss: 0.8880 - val_accuracy: 0.7035
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8771 - accuracy: 0.7036 - val_loss: 0.8892 - val_accuracy: 0.7059
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8770 - accuracy: 0.7035 - val_loss: 0.8885 - val_accuracy: 0.7060
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8758 - accuracy: 0.7034 - val_loss: 0.8885 - val_accuracy: 0.7059
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8762 - accuracy: 0.7034 - val_loss: 0.8882 - val_accuracy: 0.7056
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8765 - accuracy: 0.7030 - val_loss: 0.8884 - val_accuracy: 0.7063
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8751 - accuracy: 0.7036 - val_loss: 0.8880 - val_accuracy: 0.7059
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 4s 17ms/step - loss: 0.8763 - accuracy: 0.7033 - val_loss: 0.8896 - val_accuracy: 0.7055
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8756 - accuracy: 0.7025 - val_loss: 0.8870 - val_accuracy: 0.7033
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8757 - accuracy: 0.7037 - val_loss: 0.8877 - val_accuracy: 0.7057
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 4s 16ms/step - loss: 0.8763 - accuracy: 0.7035 - val_loss: 0.8875 - val_accuracy: 0.7033
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8766 - accuracy: 0.7041 - val_loss: 0.8881 - val_accuracy: 0.7055
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8753 - accuracy: 0.7039 - val_loss: 0.8885 - val_accuracy: 0.7052
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8758 - accuracy: 0.7029 - val_loss: 0.8885 - val_accuracy: 0.7061
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8747 - accuracy: 0.7042 - val_loss: 0.8887 - val_accuracy: 0.7058
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8756 - accuracy: 0.7025 - val_loss: 0.8893 - val_accuracy: 0.7049
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8758 - accuracy: 0.7033 - val_loss: 0.8882 - val_accuracy: 0.7048
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8757 - accuracy: 0.7034 - val_loss: 0.8881 - val_accuracy: 0.7053
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8762 - accuracy: 0.7027 - val_loss: 0.8872 - val_accuracy: 0.7054
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8751 - accuracy: 0.7030 - val_loss: 0.8884 - val_accuracy: 0.7058
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8762 - accuracy: 0.7028 - val_loss: 0.8866 - val_accuracy: 0.7025
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8741 - accuracy: 0.7032 - val_loss: 0.8883 - val_accuracy: 0.7061
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8749 - accuracy: 0.7039 - val_loss: 0.8872 - val_accuracy: 0.7059
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8741 - accuracy: 0.7042 - val_loss: 0.8868 - val_accuracy: 0.7061
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8746 - accuracy: 0.7035 - val_loss: 0.8884 - val_accuracy: 0.7060
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8753 - accuracy: 0.7035 - val_loss: 0.8874 - val_accuracy: 0.7058
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8739 - accuracy: 0.7035 - val_loss: 0.8873 - val_accuracy: 0.7060
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8748 - accuracy: 0.7036 - val_loss: 0.8878 - val_accuracy: 0.7056
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8754 - accuracy: 0.7028 - val_loss: 0.8875 - val_accuracy: 0.7061
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8744 - accuracy: 0.7032 - val_loss: 0.8870 - val_accuracy: 0.7063
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8754 - accuracy: 0.7028 - val_loss: 0.8873 - val_accuracy: 0.7056
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8745 - accuracy: 0.7040 - val_loss: 0.8866 - val_accuracy: 0.7063
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8745 - accuracy: 0.7043 - val_loss: 0.8863 - val_accuracy: 0.7032
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8742 - accuracy: 0.7048 - val_loss: 0.8876 - val_accuracy: 0.7057
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8746 - accuracy: 0.7032 - val_loss: 0.8864 - val_accuracy: 0.7057
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8744 - accuracy: 0.7033 - val_loss: 0.8863 - val_accuracy: 0.7060
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8749 - accuracy: 0.7032 - val_loss: 0.8854 - val_accuracy: 0.7031
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8738 - accuracy: 0.7043 - val_loss: 0.8868 - val_accuracy: 0.7064
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8739 - accuracy: 0.7040 - val_loss: 0.8856 - val_accuracy: 0.7034
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8744 - accuracy: 0.7042 - val_loss: 0.8862 - val_accuracy: 0.7064
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8743 - accuracy: 0.7041 - val_loss: 0.8864 - val_accuracy: 0.7058
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8744 - accuracy: 0.7046 - val_loss: 0.8851 - val_accuracy: 0.7064
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8740 - accuracy: 0.7038 - val_loss: 0.8855 - val_accuracy: 0.7058
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 3s 9ms/step - loss: 0.8520 - accuracy: 0.9004 - val_loss: 0.8256 - val_accuracy: 0.9053
[ 0.          0.          0.         ... -0.11999476  0.09713747
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8428 - accuracy: 0.9017 - val_loss: 0.8238 - val_accuracy: 0.9061
[ 0.          0.          0.         ... -0.11510681  0.09732954
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8417 - accuracy: 0.9017 - val_loss: 0.8237 - val_accuracy: 0.9062
[ 0.          0.          0.         ... -0.11250631  0.09858902
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8410 - accuracy: 0.9016 - val_loss: 0.8230 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.11115648  0.09971751
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8410 - accuracy: 0.9015 - val_loss: 0.8226 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.11069585  0.10087128
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9013 - val_loss: 0.8225 - val_accuracy: 0.9062
[ 0.          0.          0.         ... -0.11003719  0.10171769
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9016 - val_loss: 0.8231 - val_accuracy: 0.9056
[ 0.          0.          0.         ... -0.11021721  0.10235994
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8402 - accuracy: 0.9018 - val_loss: 0.8223 - val_accuracy: 0.9067
[ 0.          0.          0.         ... -0.11026673  0.10327499
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8403 - accuracy: 0.9016 - val_loss: 0.8222 - val_accuracy: 0.9065
[ 0.          0.          0.         ... -0.11059546  0.10384838
  0.        ]
Sparsity at: 0.4999497049356223
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8402 - accuracy: 0.9015 - val_loss: 0.8220 - val_accuracy: 0.9068
[ 0.          0.          0.         ... -0.11047424  0.10421267
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9016 - val_loss: 0.8221 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.11097842  0.10494617
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8399 - accuracy: 0.9017 - val_loss: 0.8219 - val_accuracy: 0.9064
[ 0.          0.          0.         ... -0.11090565  0.10508011
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9014 - val_loss: 0.8222 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.11091372  0.10521565
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9016 - val_loss: 0.8217 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.11115445  0.10520121
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9015 - val_loss: 0.8218 - val_accuracy: 0.9062
[ 0.          0.          0.         ... -0.11103234  0.10562972
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9017 - val_loss: 0.8217 - val_accuracy: 0.9065
[ 0.          0.          0.         ... -0.11115702  0.10545632
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9018 - val_loss: 0.8220 - val_accuracy: 0.9062
[ 0.          0.          0.         ... -0.11081094  0.10569409
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8397 - accuracy: 0.9016 - val_loss: 0.8221 - val_accuracy: 0.9061
[ 0.          0.          0.         ... -0.11115512  0.10595326
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8397 - accuracy: 0.9013 - val_loss: 0.8216 - val_accuracy: 0.9065
[ 0.          0.          0.         ... -0.1115019   0.10596164
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9020 - val_loss: 0.8219 - val_accuracy: 0.9059
[ 0.          0.          0.         ... -0.11132853  0.10617508
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8398 - accuracy: 0.9018 - val_loss: 0.8222 - val_accuracy: 0.9061
[ 0.          0.          0.         ... -0.11114341  0.1062773
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8399 - accuracy: 0.9014 - val_loss: 0.8222 - val_accuracy: 0.9061
[ 0.          0.          0.         ... -0.11112142  0.10616571
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8398 - accuracy: 0.9018 - val_loss: 0.8217 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.111249    0.10646705
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8397 - accuracy: 0.9016 - val_loss: 0.8218 - val_accuracy: 0.9058
[ 0.         0.         0.        ... -0.1113296  0.1065885  0.       ]
Sparsity at: 0.4999497049356223
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8398 - accuracy: 0.9013 - val_loss: 0.8211 - val_accuracy: 0.9067
[ 0.          0.          0.         ... -0.11155467  0.1064709
  0.        ]
Sparsity at: 0.4999497049356223
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9017 - val_loss: 0.8215 - val_accuracy: 0.9064
[ 0.          0.          0.         ... -0.11126692  0.10668615
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8398 - accuracy: 0.9014 - val_loss: 0.8215 - val_accuracy: 0.9067
[ 0.          0.          0.         ... -0.11128236  0.10695814
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8397 - accuracy: 0.9014 - val_loss: 0.8215 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.11132324  0.10694671
  0.        ]
Sparsity at: 0.4999497049356223
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8397 - accuracy: 0.9015 - val_loss: 0.8213 - val_accuracy: 0.9069
[ 0.          0.          0.         ... -0.11114836  0.10676926
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9018 - val_loss: 0.8214 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.11114299  0.10683499
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8395 - accuracy: 0.9014 - val_loss: 0.8217 - val_accuracy: 0.9059
[ 0.          0.          0.         ... -0.11142986  0.10724993
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9015 - val_loss: 0.8217 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.11111596  0.1068156
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9018 - val_loss: 0.8212 - val_accuracy: 0.9065
[ 0.          0.          0.         ... -0.111177    0.10685202
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9018 - val_loss: 0.8213 - val_accuracy: 0.9061
[ 0.          0.          0.         ... -0.1112776   0.10690131
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 35/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9016 - val_loss: 0.8217 - val_accuracy: 0.9057
[ 0.          0.          0.         ... -0.11080883  0.10680526
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9017 - val_loss: 0.8220 - val_accuracy: 0.9064
[ 0.          0.          0.         ... -0.11056922  0.10705034
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9015 - val_loss: 0.8218 - val_accuracy: 0.9060
[ 0.          0.          0.         ... -0.11078779  0.10706759
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9015 - val_loss: 0.8216 - val_accuracy: 0.9062
[ 0.          0.          0.         ... -0.11128186  0.1073451
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8392 - accuracy: 0.9018 - val_loss: 0.8218 - val_accuracy: 0.9061
[ 0.          0.          0.         ... -0.11110184  0.1074997
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8397 - accuracy: 0.9014 - val_loss: 0.8217 - val_accuracy: 0.9064
[ 0.          0.          0.         ... -0.11089122  0.10716727
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9016 - val_loss: 0.8215 - val_accuracy: 0.9062
[ 0.          0.          0.         ... -0.11071569  0.10693131
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 42/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9018 - val_loss: 0.8215 - val_accuracy: 0.9065
[ 0.          0.          0.         ... -0.11094666  0.10705218
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9014 - val_loss: 0.8216 - val_accuracy: 0.9064
[ 0.          0.          0.         ... -0.11079706  0.1070861
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8217 - val_accuracy: 0.9063
[ 0.          0.          0.         ... -0.11087454  0.10747892
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9014 - val_loss: 0.8219 - val_accuracy: 0.9060
[ 0.          0.          0.         ... -0.1107095   0.10741054
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9018 - val_loss: 0.8217 - val_accuracy: 0.9060
[ 0.          0.          0.         ... -0.11062576  0.1072721
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8393 - accuracy: 0.9018 - val_loss: 0.8217 - val_accuracy: 0.9059
[ 0.          0.          0.         ... -0.11075323  0.10719405
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9014 - val_loss: 0.8212 - val_accuracy: 0.9064
[ 0.          0.          0.         ... -0.11079308  0.10741761
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9016 - val_loss: 0.8212 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.11067689  0.10727011
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9014 - val_loss: 0.8214 - val_accuracy: 0.9066
[ 0.          0.          0.         ... -0.11076469  0.10725372
 -0.        ]
Sparsity at: 0.4999497049356223
Epoch 51/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8702 - accuracy: 0.9013 - val_loss: 0.8455 - val_accuracy: 0.9073
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 52/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8630 - accuracy: 0.9021 - val_loss: 0.8438 - val_accuracy: 0.9073
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8620 - accuracy: 0.9018 - val_loss: 0.8431 - val_accuracy: 0.9081
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 54/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.9021 - val_loss: 0.8432 - val_accuracy: 0.9071
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.6458221566523605
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.9019 - val_loss: 0.8429 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8612 - accuracy: 0.9020 - val_loss: 0.8426 - val_accuracy: 0.9076
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.6458221566523605
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9020 - val_loss: 0.8427 - val_accuracy: 0.9071
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8612 - accuracy: 0.9019 - val_loss: 0.8426 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 59/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9021 - val_loss: 0.8428 - val_accuracy: 0.9068
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9016 - val_loss: 0.8424 - val_accuracy: 0.9072
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 61/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8610 - accuracy: 0.9019 - val_loss: 0.8424 - val_accuracy: 0.9071
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9020 - val_loss: 0.8425 - val_accuracy: 0.9072
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9017 - val_loss: 0.8427 - val_accuracy: 0.9064
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.6458221566523605
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8609 - accuracy: 0.9019 - val_loss: 0.8425 - val_accuracy: 0.9071
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9020 - val_loss: 0.8422 - val_accuracy: 0.9074
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9019 - val_loss: 0.8424 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.6458221566523605
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9019 - val_loss: 0.8425 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9019 - val_loss: 0.8422 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9018 - val_loss: 0.8424 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9019 - val_loss: 0.8422 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9018 - val_loss: 0.8422 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9020 - val_loss: 0.8425 - val_accuracy: 0.9064
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9020 - val_loss: 0.8422 - val_accuracy: 0.9068
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8607 - accuracy: 0.9022 - val_loss: 0.8423 - val_accuracy: 0.9066
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9021 - val_loss: 0.8422 - val_accuracy: 0.9066
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9023 - val_loss: 0.8424 - val_accuracy: 0.9066
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 77/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8608 - accuracy: 0.9020 - val_loss: 0.8423 - val_accuracy: 0.9072
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9018 - val_loss: 0.8422 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9019 - val_loss: 0.8421 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.6458221566523605
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9022 - val_loss: 0.8421 - val_accuracy: 0.9066
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 81/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8606 - accuracy: 0.9021 - val_loss: 0.8423 - val_accuracy: 0.9067
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9018 - val_loss: 0.8421 - val_accuracy: 0.9071
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9019 - val_loss: 0.8422 - val_accuracy: 0.9074
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9018 - val_loss: 0.8425 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9020 - val_loss: 0.8419 - val_accuracy: 0.9071
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9021 - val_loss: 0.8422 - val_accuracy: 0.9066
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9021 - val_loss: 0.8423 - val_accuracy: 0.9066
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9019 - val_loss: 0.8422 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9018 - val_loss: 0.8427 - val_accuracy: 0.9068
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9018 - val_loss: 0.8420 - val_accuracy: 0.9066
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.6458221566523605
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9020 - val_loss: 0.8420 - val_accuracy: 0.9068
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9021 - val_loss: 0.8421 - val_accuracy: 0.9067
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 93/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8606 - accuracy: 0.9019 - val_loss: 0.8421 - val_accuracy: 0.9069
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9018 - val_loss: 0.8422 - val_accuracy: 0.9068
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9018 - val_loss: 0.8423 - val_accuracy: 0.9064
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.6458221566523605
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9021 - val_loss: 0.8424 - val_accuracy: 0.9068
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8604 - accuracy: 0.9019 - val_loss: 0.8420 - val_accuracy: 0.9066
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9020 - val_loss: 0.8422 - val_accuracy: 0.9065
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 99/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8605 - accuracy: 0.9019 - val_loss: 0.8419 - val_accuracy: 0.9070
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 100/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8606 - accuracy: 0.9018 - val_loss: 0.8423 - val_accuracy: 0.9064
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.6458221566523605
Epoch 101/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8955 - accuracy: 0.9003 - val_loss: 0.8750 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 102/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8894 - accuracy: 0.9017 - val_loss: 0.8738 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8889 - accuracy: 0.9019 - val_loss: 0.8739 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8734 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8884 - accuracy: 0.9020 - val_loss: 0.8733 - val_accuracy: 0.9034
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8882 - accuracy: 0.9017 - val_loss: 0.8734 - val_accuracy: 0.9029
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8881 - accuracy: 0.9019 - val_loss: 0.8732 - val_accuracy: 0.9028
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8881 - accuracy: 0.9014 - val_loss: 0.8730 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8880 - accuracy: 0.9016 - val_loss: 0.8729 - val_accuracy: 0.9028
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9015 - val_loss: 0.8728 - val_accuracy: 0.9028
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9016 - val_loss: 0.8730 - val_accuracy: 0.9033
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9015 - val_loss: 0.8731 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9014 - val_loss: 0.8729 - val_accuracy: 0.9029
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9017 - val_loss: 0.8729 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9014 - val_loss: 0.8728 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8878 - accuracy: 0.9017 - val_loss: 0.8728 - val_accuracy: 0.9033
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8730 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 118/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8729 - val_accuracy: 0.9029
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8877 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8878 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8730 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8725 - val_accuracy: 0.9029
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9018 - val_loss: 0.8729 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9016 - val_loss: 0.8726 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8727 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9016 - val_loss: 0.8728 - val_accuracy: 0.9034
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9014 - val_loss: 0.8727 - val_accuracy: 0.9036
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9033
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8728 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9018 - val_loss: 0.8725 - val_accuracy: 0.9033
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9020 - val_loss: 0.8727 - val_accuracy: 0.9035
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9019 - val_loss: 0.8726 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8874 - accuracy: 0.9016 - val_loss: 0.8724 - val_accuracy: 0.9033
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9035
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9033
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9018 - val_loss: 0.8725 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9018 - val_loss: 0.8725 - val_accuracy: 0.9033
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8728 - val_accuracy: 0.9029
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9015 - val_loss: 0.8724 - val_accuracy: 0.9033
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8721 - val_accuracy: 0.9032
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9018 - val_loss: 0.8725 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9016 - val_loss: 0.8726 - val_accuracy: 0.9029
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.7594219420600858
Epoch 151/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9558 - accuracy: 0.8981 - val_loss: 0.9275 - val_accuracy: 0.9014
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 152/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9429 - accuracy: 0.9000 - val_loss: 0.9254 - val_accuracy: 0.9020
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9412 - accuracy: 0.9004 - val_loss: 0.9244 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9403 - accuracy: 0.9003 - val_loss: 0.9238 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9398 - accuracy: 0.9007 - val_loss: 0.9233 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9393 - accuracy: 0.9007 - val_loss: 0.9230 - val_accuracy: 0.9028
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9389 - accuracy: 0.9007 - val_loss: 0.9227 - val_accuracy: 0.9027
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9387 - accuracy: 0.9008 - val_loss: 0.9227 - val_accuracy: 0.9027
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9385 - accuracy: 0.9006 - val_loss: 0.9223 - val_accuracy: 0.9026
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9383 - accuracy: 0.9006 - val_loss: 0.9221 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9382 - accuracy: 0.9006 - val_loss: 0.9222 - val_accuracy: 0.9031
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9381 - accuracy: 0.9005 - val_loss: 0.9219 - val_accuracy: 0.9030
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9380 - accuracy: 0.9007 - val_loss: 0.9220 - val_accuracy: 0.9028
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 164/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9379 - accuracy: 0.9006 - val_loss: 0.9219 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9379 - accuracy: 0.9005 - val_loss: 0.9217 - val_accuracy: 0.9028
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9378 - accuracy: 0.9004 - val_loss: 0.9217 - val_accuracy: 0.9028
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9378 - accuracy: 0.9005 - val_loss: 0.9217 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9007 - val_loss: 0.9217 - val_accuracy: 0.9027
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9006 - val_loss: 0.9216 - val_accuracy: 0.9026
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9007 - val_loss: 0.9216 - val_accuracy: 0.9027
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9006 - val_loss: 0.9216 - val_accuracy: 0.9028
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9006 - val_loss: 0.9215 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9008 - val_loss: 0.9216 - val_accuracy: 0.9022
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8447726663090128
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9216 - val_accuracy: 0.9027
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9007 - val_loss: 0.9216 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9216 - val_accuracy: 0.9024
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8447726663090128
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9009 - val_loss: 0.9215 - val_accuracy: 0.9026
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9007 - val_loss: 0.9215 - val_accuracy: 0.9023
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9006 - val_loss: 0.9216 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9006 - val_loss: 0.9215 - val_accuracy: 0.9024
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8447726663090128
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9215 - val_accuracy: 0.9024
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8447726663090128
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9213 - val_accuracy: 0.9022
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8447726663090128
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9022
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9006 - val_loss: 0.9214 - val_accuracy: 0.9025
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8447726663090128
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9007 - val_loss: 0.9213 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9008 - val_loss: 0.9215 - val_accuracy: 0.9021
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8447726663090128
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9008 - val_loss: 0.9214 - val_accuracy: 0.9023
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9215 - val_accuracy: 0.9023
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9006 - val_loss: 0.9215 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9008 - val_loss: 0.9214 - val_accuracy: 0.9021
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9376 - accuracy: 0.9008 - val_loss: 0.9214 - val_accuracy: 0.9023
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9213 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9213 - val_accuracy: 0.9025
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9008 - val_loss: 0.9213 - val_accuracy: 0.9024
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8447726663090128
Epoch 201/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1231 - accuracy: 0.8529 - val_loss: 1.0860 - val_accuracy: 0.8721
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 202/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0939 - accuracy: 0.8738 - val_loss: 1.0787 - val_accuracy: 0.8735
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0901 - accuracy: 0.8760 - val_loss: 1.0759 - val_accuracy: 0.8759
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0883 - accuracy: 0.8767 - val_loss: 1.0745 - val_accuracy: 0.8771
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 205/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0874 - accuracy: 0.8773 - val_loss: 1.0737 - val_accuracy: 0.8778
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 206/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0868 - accuracy: 0.8776 - val_loss: 1.0731 - val_accuracy: 0.8780
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0863 - accuracy: 0.8777 - val_loss: 1.0726 - val_accuracy: 0.8783
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0860 - accuracy: 0.8779 - val_loss: 1.0723 - val_accuracy: 0.8780
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0857 - accuracy: 0.8783 - val_loss: 1.0721 - val_accuracy: 0.8787
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0854 - accuracy: 0.8783 - val_loss: 1.0717 - val_accuracy: 0.8787
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0852 - accuracy: 0.8783 - val_loss: 1.0715 - val_accuracy: 0.8790
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0851 - accuracy: 0.8785 - val_loss: 1.0713 - val_accuracy: 0.8793
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0849 - accuracy: 0.8784 - val_loss: 1.0713 - val_accuracy: 0.8796
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0847 - accuracy: 0.8788 - val_loss: 1.0710 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0846 - accuracy: 0.8786 - val_loss: 1.0709 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0846 - accuracy: 0.8785 - val_loss: 1.0708 - val_accuracy: 0.8796
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0845 - accuracy: 0.8785 - val_loss: 1.0706 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0843 - accuracy: 0.8786 - val_loss: 1.0706 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0843 - accuracy: 0.8787 - val_loss: 1.0705 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0843 - accuracy: 0.8785 - val_loss: 1.0703 - val_accuracy: 0.8796
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0842 - accuracy: 0.8787 - val_loss: 1.0702 - val_accuracy: 0.8796
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0842 - accuracy: 0.8787 - val_loss: 1.0704 - val_accuracy: 0.8799
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0842 - accuracy: 0.8788 - val_loss: 1.0703 - val_accuracy: 0.8797
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0841 - accuracy: 0.8788 - val_loss: 1.0703 - val_accuracy: 0.8798
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8788 - val_loss: 1.0703 - val_accuracy: 0.8797
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 226/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0841 - accuracy: 0.8788 - val_loss: 1.0703 - val_accuracy: 0.8796
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8787 - val_loss: 1.0703 - val_accuracy: 0.8798
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 228/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8788 - val_loss: 1.0701 - val_accuracy: 0.8791
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8788 - val_loss: 1.0702 - val_accuracy: 0.8794
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8787 - val_loss: 1.0701 - val_accuracy: 0.8797
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 231/500
235/235 [==============================] - 2s 10ms/step - loss: 1.0839 - accuracy: 0.8786 - val_loss: 1.0702 - val_accuracy: 0.8791
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 232/500
235/235 [==============================] - 2s 10ms/step - loss: 1.0839 - accuracy: 0.8788 - val_loss: 1.0701 - val_accuracy: 0.8797
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 233/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0839 - accuracy: 0.8788 - val_loss: 1.0702 - val_accuracy: 0.8797
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 234/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8786 - val_loss: 1.0701 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8789 - val_loss: 1.0701 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8787 - val_loss: 1.0700 - val_accuracy: 0.8796
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8787 - val_loss: 1.0699 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 238/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0839 - accuracy: 0.8786 - val_loss: 1.0701 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 239/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0839 - accuracy: 0.8788 - val_loss: 1.0701 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8789 - val_loss: 1.0700 - val_accuracy: 0.8798
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 241/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8786 - val_loss: 1.0701 - val_accuracy: 0.8798
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8790 - val_loss: 1.0700 - val_accuracy: 0.8794
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0838 - accuracy: 0.8786 - val_loss: 1.0700 - val_accuracy: 0.8797
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8787 - val_loss: 1.0700 - val_accuracy: 0.8796
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8789 - val_loss: 1.0701 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0838 - accuracy: 0.8789 - val_loss: 1.0700 - val_accuracy: 0.8793
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8789 - val_loss: 1.0700 - val_accuracy: 0.8791
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8787 - val_loss: 1.0700 - val_accuracy: 0.8799
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0838 - accuracy: 0.8786 - val_loss: 1.0700 - val_accuracy: 0.8792
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8788 - val_loss: 1.0700 - val_accuracy: 0.8795
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059482296137339
Epoch 251/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2216 - accuracy: 0.8592 - val_loss: 1.1620 - val_accuracy: 0.8758
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 252/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1800 - accuracy: 0.8737 - val_loss: 1.1565 - val_accuracy: 0.8787
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1766 - accuracy: 0.8749 - val_loss: 1.1546 - val_accuracy: 0.8801
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1752 - accuracy: 0.8752 - val_loss: 1.1535 - val_accuracy: 0.8817
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1744 - accuracy: 0.8752 - val_loss: 1.1526 - val_accuracy: 0.8819
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1736 - accuracy: 0.8752 - val_loss: 1.1517 - val_accuracy: 0.8823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1729 - accuracy: 0.8753 - val_loss: 1.1510 - val_accuracy: 0.8822
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1724 - accuracy: 0.8754 - val_loss: 1.1505 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1722 - accuracy: 0.8753 - val_loss: 1.1503 - val_accuracy: 0.8824
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1720 - accuracy: 0.8754 - val_loss: 1.1500 - val_accuracy: 0.8829
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1718 - accuracy: 0.8753 - val_loss: 1.1499 - val_accuracy: 0.8828
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1717 - accuracy: 0.8752 - val_loss: 1.1498 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1717 - accuracy: 0.8750 - val_loss: 1.1497 - val_accuracy: 0.8828
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1716 - accuracy: 0.8751 - val_loss: 1.1496 - val_accuracy: 0.8827
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1715 - accuracy: 0.8752 - val_loss: 1.1496 - val_accuracy: 0.8829
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1715 - accuracy: 0.8751 - val_loss: 1.1495 - val_accuracy: 0.8824
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1714 - accuracy: 0.8752 - val_loss: 1.1495 - val_accuracy: 0.8825
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 268/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1714 - accuracy: 0.8751 - val_loss: 1.1494 - val_accuracy: 0.8825
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 269/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1714 - accuracy: 0.8751 - val_loss: 1.1494 - val_accuracy: 0.8827
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1714 - accuracy: 0.8751 - val_loss: 1.1494 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 271/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8752 - val_loss: 1.1493 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1493 - val_accuracy: 0.8827
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1494 - val_accuracy: 0.8823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8824
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8822
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8824
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1493 - val_accuracy: 0.8823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8753 - val_loss: 1.1493 - val_accuracy: 0.8825
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1493 - val_accuracy: 0.8824
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8820
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1712 - accuracy: 0.8750 - val_loss: 1.1493 - val_accuracy: 0.8823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 282/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1492 - val_accuracy: 0.8824
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8824
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469722371244635
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8825
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1492 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1492 - val_accuracy: 0.8824
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469722371244635
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8749 - val_loss: 1.1492 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8749 - val_loss: 1.1492 - val_accuracy: 0.8824
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469722371244635
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1492 - val_accuracy: 0.8825
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469722371244635
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8752 - val_loss: 1.1492 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1492 - val_accuracy: 0.8827
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8750 - val_loss: 1.1492 - val_accuracy: 0.8825
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469722371244635
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8750 - val_loss: 1.1491 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8750 - val_loss: 1.1491 - val_accuracy: 0.8824
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1711 - accuracy: 0.8750 - val_loss: 1.1490 - val_accuracy: 0.8826
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1710 - accuracy: 0.8750 - val_loss: 1.1489 - val_accuracy: 0.8823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 298/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1710 - accuracy: 0.8748 - val_loss: 1.1489 - val_accuracy: 0.8823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1709 - accuracy: 0.8749 - val_loss: 1.1488 - val_accuracy: 0.8820
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1709 - accuracy: 0.8750 - val_loss: 1.1487 - val_accuracy: 0.8823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469722371244635
Epoch 301/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5324 - accuracy: 0.6888 - val_loss: 1.4737 - val_accuracy: 0.6974
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718347639484979
Epoch 302/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4811 - accuracy: 0.6967 - val_loss: 1.4623 - val_accuracy: 0.6987
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4755 - accuracy: 0.6970 - val_loss: 1.4595 - val_accuracy: 0.6988
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4737 - accuracy: 0.6950 - val_loss: 1.4583 - val_accuracy: 0.6989
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718347639484979
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4729 - accuracy: 0.6945 - val_loss: 1.4577 - val_accuracy: 0.6985
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4726 - accuracy: 0.6953 - val_loss: 1.4574 - val_accuracy: 0.6991
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4723 - accuracy: 0.6948 - val_loss: 1.4571 - val_accuracy: 0.6991
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4721 - accuracy: 0.6949 - val_loss: 1.4570 - val_accuracy: 0.6989
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4720 - accuracy: 0.6949 - val_loss: 1.4568 - val_accuracy: 0.6990
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4719 - accuracy: 0.6950 - val_loss: 1.4567 - val_accuracy: 0.6989
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4718 - accuracy: 0.6953 - val_loss: 1.4566 - val_accuracy: 0.6990
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4718 - accuracy: 0.6955 - val_loss: 1.4566 - val_accuracy: 0.6989
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4717 - accuracy: 0.6949 - val_loss: 1.4565 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4717 - accuracy: 0.6951 - val_loss: 1.4564 - val_accuracy: 0.6989
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 315/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4716 - accuracy: 0.6951 - val_loss: 1.4564 - val_accuracy: 0.6989
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4716 - accuracy: 0.6950 - val_loss: 1.4563 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4716 - accuracy: 0.6952 - val_loss: 1.4563 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4716 - accuracy: 0.6950 - val_loss: 1.4563 - val_accuracy: 0.6995
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6952 - val_loss: 1.4563 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6957 - val_loss: 1.4562 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6956 - val_loss: 1.4562 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6953 - val_loss: 1.4562 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6953 - val_loss: 1.4562 - val_accuracy: 0.6995
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6950 - val_loss: 1.4562 - val_accuracy: 0.6995
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6951 - val_loss: 1.4562 - val_accuracy: 0.6996
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6949 - val_loss: 1.4562 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6952 - val_loss: 1.4561 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6949 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 331/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6951 - val_loss: 1.4561 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6953 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6955 - val_loss: 1.4561 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6953 - val_loss: 1.4561 - val_accuracy: 0.6995
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4714 - accuracy: 0.6954 - val_loss: 1.4561 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 339/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6956 - val_loss: 1.4561 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6949 - val_loss: 1.4560 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6952 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 343/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4714 - accuracy: 0.6951 - val_loss: 1.4561 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6992
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6951 - val_loss: 1.4560 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6949 - val_loss: 1.4561 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6948 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6947 - val_loss: 1.4561 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6949 - val_loss: 1.4561 - val_accuracy: 0.6993
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718347639484979
Epoch 351/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7748 - accuracy: 0.5517 - val_loss: 1.7227 - val_accuracy: 0.5577
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 352/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7197 - accuracy: 0.5532 - val_loss: 1.7108 - val_accuracy: 0.5407
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7140 - accuracy: 0.5449 - val_loss: 1.7082 - val_accuracy: 0.5410
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7124 - accuracy: 0.5429 - val_loss: 1.7072 - val_accuracy: 0.5412
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.5431 - val_loss: 1.7068 - val_accuracy: 0.5417
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7114 - accuracy: 0.5433 - val_loss: 1.7065 - val_accuracy: 0.5421
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7112 - accuracy: 0.5433 - val_loss: 1.7063 - val_accuracy: 0.5422
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7110 - accuracy: 0.5435 - val_loss: 1.7062 - val_accuracy: 0.5425
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7110 - accuracy: 0.5435 - val_loss: 1.7061 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.5436 - val_loss: 1.7060 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.5437 - val_loss: 1.7060 - val_accuracy: 0.5425
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.5437 - val_loss: 1.7059 - val_accuracy: 0.5427
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7107 - accuracy: 0.5436 - val_loss: 1.7059 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7107 - accuracy: 0.5436 - val_loss: 1.7058 - val_accuracy: 0.5427
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7107 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5430
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7107 - accuracy: 0.5437 - val_loss: 1.7058 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5430
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5430
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5430
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5427
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5430
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5431
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5431
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5431
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5440 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5440 - val_loss: 1.7058 - val_accuracy: 0.5430
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 386/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5440 - val_loss: 1.7058 - val_accuracy: 0.5431
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 388/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5437 - val_loss: 1.7057 - val_accuracy: 0.5430
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7057 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9843414699570815
Epoch 401/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8909 - accuracy: 0.4116 - val_loss: 1.8557 - val_accuracy: 0.4074
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9891362660944206
Epoch 402/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8576 - accuracy: 0.4144 - val_loss: 1.8469 - val_accuracy: 0.4565
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9891362660944206
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8537 - accuracy: 0.4536 - val_loss: 1.8450 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 404/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8529 - accuracy: 0.4533 - val_loss: 1.8443 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8526 - accuracy: 0.4534 - val_loss: 1.8441 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8524 - accuracy: 0.4533 - val_loss: 1.8439 - val_accuracy: 0.4563
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8523 - accuracy: 0.4533 - val_loss: 1.8438 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8523 - accuracy: 0.4534 - val_loss: 1.8437 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8522 - accuracy: 0.4533 - val_loss: 1.8437 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8522 - accuracy: 0.4532 - val_loss: 1.8436 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8436 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8436 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 419/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 423/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4563
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4533 - val_loss: 1.8435 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4559
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 433/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4563
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4563
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 452/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4533 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 458/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4559
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 465/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4563
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 478/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 479/500
235/235 [==============================] - 2s 10ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4533 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4559
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 496/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9891362660944206
Epoch 1/500
235/235 [==============================] - 4s 9ms/step - loss: 0.0028 - accuracy: 0.9990 - val_loss: 0.2508 - val_accuracy: 0.9731
[-0.        0.       -0.       ... -0.880246  0.       -0.      ]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5839e-04 - accuracy: 1.0000 - val_loss: 0.2459 - val_accuracy: 0.9748
[-0.          0.         -0.         ... -0.88338655  0.
 -0.        ]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6287e-05 - accuracy: 1.0000 - val_loss: 0.2439 - val_accuracy: 0.9750
[-0.          0.         -0.         ... -0.88420767 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0862e-05 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9753
[-0.          0.         -0.         ... -0.88462055 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6462e-05 - accuracy: 1.0000 - val_loss: 0.2430 - val_accuracy: 0.9753
[-0.          0.         -0.         ... -0.88488364 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3971e-05 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9753
[-0.          0.         -0.         ... -0.88519484 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2152e-05 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9751
[-0.         0.        -0.        ... -0.8855181 -0.        -0.       ]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0740e-05 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9752
[-0.         0.        -0.        ... -0.8858649 -0.        -0.       ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 9.6042e-06 - accuracy: 1.0000 - val_loss: 0.2421 - val_accuracy: 0.9753
[-0.         0.        -0.        ... -0.8862164 -0.        -0.       ]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 8.6602e-06 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9753
[-0.          0.         -0.         ... -0.88663924 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8596e-06 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8870965 -0.        -0.       ]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 7.1681e-06 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9755
[-0.          0.         -0.         ... -0.88758665 -0.
 -0.        ]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5653e-06 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9755
[-0.         0.        -0.        ... -0.8880845 -0.        -0.       ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0327e-06 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8886213 -0.        -0.       ]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5587e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8892315 -0.        -0.       ]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1293e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8898362 -0.        -0.       ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7440e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8904798 -0.        -0.       ]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3919e-06 - accuracy: 1.0000 - val_loss: 0.2410 - val_accuracy: 0.9754
[-0.        0.       -0.       ... -0.891201 -0.       -0.      ]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0704e-06 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8919127 -0.        -0.       ]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7760e-06 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8927047  0.        -0.       ]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5044e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9755
[-0.         0.        -0.        ... -0.8934853 -0.        -0.       ]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2548e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9755
[-0.          0.         -0.         ... -0.89430565  0.
 -0.        ]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0236e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8951802  0.        -0.       ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8083e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.8961327  0.        -0.       ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6101e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9755
[-0.         0.        -0.        ... -0.8970646  0.        -0.       ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4256e-06 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9755
[-0.         0.        -0.        ... -0.8980432  0.        -0.       ]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2550e-06 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9755
[-0.         0.        -0.        ... -0.8991141  0.        -0.       ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0949e-06 - accuracy: 1.0000 - val_loss: 0.2410 - val_accuracy: 0.9757
[-0.        0.       -0.       ... -0.900172  0.       -0.      ]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9475e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9757
[-0.         0.        -0.        ... -0.9013244  0.        -0.       ]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8090e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9757
[-0.          0.         -0.         ... -0.90249956  0.
 -0.        ]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6800e-06 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9758
[-0.          0.         -0.         ... -0.90368205  0.
 -0.        ]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5605e-06 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9755
[-0.         0.        -0.        ... -0.9049617  0.        -0.       ]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4485e-06 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9755
[-0.          0.         -0.         ... -0.90627617  0.
 -0.        ]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3443e-06 - accuracy: 1.0000 - val_loss: 0.2420 - val_accuracy: 0.9755
[-0.         0.        -0.        ... -0.9076231  0.        -0.       ]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2475e-06 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9755
[-0.         0.        -0.        ... -0.9090028  0.        -0.       ]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1572e-06 - accuracy: 1.0000 - val_loss: 0.2426 - val_accuracy: 0.9757
[-0.         0.        -0.        ... -0.9104391  0.        -0.       ]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0728e-06 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9758
[-0.          0.         -0.         ... -0.91196615  0.
 -0.        ]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 9.9442e-07 - accuracy: 1.0000 - val_loss: 0.2432 - val_accuracy: 0.9763
[-0.         0.        -0.        ... -0.9135096  0.        -0.       ]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 2s 9ms/step - loss: 9.2161e-07 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9762
[-0.         0.        -0.        ... -0.9151253  0.        -0.       ]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 8.5330e-07 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9761
[-0.         0.        -0.        ... -0.9168575  0.        -0.       ]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8998e-07 - accuracy: 1.0000 - val_loss: 0.2444 - val_accuracy: 0.9760
[-0.          0.         -0.         ... -0.91864526  0.
 -0.        ]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3102e-07 - accuracy: 1.0000 - val_loss: 0.2449 - val_accuracy: 0.9760
[-0.         0.        -0.        ... -0.9204894  0.        -0.       ]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7575e-07 - accuracy: 1.0000 - val_loss: 0.2454 - val_accuracy: 0.9761
[-0.         0.        -0.        ... -0.9223955  0.        -0.       ]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2511e-07 - accuracy: 1.0000 - val_loss: 0.2459 - val_accuracy: 0.9761
[-0.          0.         -0.         ... -0.92433524  0.
  0.        ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7775e-07 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9761
[-0.         0.        -0.        ... -0.9263523  0.         0.       ]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3357e-07 - accuracy: 1.0000 - val_loss: 0.2470 - val_accuracy: 0.9761
[-0.         0.        -0.        ... -0.9284324  0.         0.       ]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9279e-07 - accuracy: 1.0000 - val_loss: 0.2476 - val_accuracy: 0.9761
[-0.         0.        -0.        ... -0.9305882  0.        -0.       ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5467e-07 - accuracy: 1.0000 - val_loss: 0.2481 - val_accuracy: 0.9761
[-0.         0.        -0.        ... -0.9328444  0.         0.       ]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1975e-07 - accuracy: 1.0000 - val_loss: 0.2488 - val_accuracy: 0.9761
[-0.         0.        -0.        ... -0.9351129  0.         0.       ]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8673e-07 - accuracy: 1.0000 - val_loss: 0.2494 - val_accuracy: 0.9761
[-0.          0.         -0.         ... -0.93748915  0.
  0.        ]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0220 - accuracy: 0.9937 - val_loss: 0.2050 - val_accuracy: 0.9721
[-0.         0.        -0.        ... -0.9197833  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 52/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0031 - accuracy: 0.9989 - val_loss: 0.1972 - val_accuracy: 0.9746
[-0.          0.         -0.         ... -0.92911404  0.
 -0.        ]
Sparsity at: 0.6458724517167382
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 6.4271e-04 - accuracy: 0.9999 - val_loss: 0.1923 - val_accuracy: 0.9743
[-0.          0.         -0.         ... -0.93192124  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 54/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7807e-04 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9742
[-0.         0.        -0.        ... -0.9324415  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9726e-04 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9743
[-0.          0.         -0.         ... -0.93300086  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7012e-04 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9744
[-0.          0.         -0.         ... -0.93357474  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5169e-04 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9744
[-0.          0.         -0.         ... -0.93418986  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3714e-04 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9744
[-0.         0.        -0.        ... -0.9348854  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 59/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2514e-04 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9747
[-0.          0.         -0.         ... -0.93563855  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1471e-04 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9746
[-0.         0.        -0.        ... -0.9364736  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0559e-04 - accuracy: 1.0000 - val_loss: 0.1940 - val_accuracy: 0.9746
[-0.         0.        -0.        ... -0.9373931  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7388e-05 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -0.9383922  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0047e-05 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -0.9394359  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3303e-05 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9750
[-0.          0.         -0.         ... -0.94056064  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7102e-05 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9751
[-0.          0.         -0.         ... -0.94178057  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 7.1415e-05 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9752
[-0.         0.        -0.        ... -0.9430806  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6151e-05 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9752
[-0.          0.         -0.         ... -0.94446164  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1270e-05 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9751
[-0.         0.        -0.        ... -0.9459038  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6698e-05 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9753
[-0.          0.         -0.         ... -0.94743764  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2444e-05 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9753
[-0.          0.         -0.         ... -0.94902414  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8513e-05 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9753
[-0.       0.      -0.      ... -0.95073  0.       0.     ]
Sparsity at: 0.6458724517167382
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4804e-05 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9753
[-0.         0.        -0.        ... -0.9524606  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1370e-05 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9753
[-0.          0.         -0.         ... -0.95430547  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8158e-05 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9754
[-0.         0.        -0.        ... -0.9562299  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5170e-05 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9754
[-0.          0.         -0.         ... -0.95825326  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2361e-05 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9755
[-0.          0.         -0.         ... -0.96038073  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 77/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9773e-05 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9752
[-0.          0.         -0.         ... -0.96263814  0.
 -0.        ]
Sparsity at: 0.6458724517167382
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7366e-05 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9752
[-0.         0.        -0.        ... -0.9649859  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5115e-05 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9751
[-0.          0.         -0.         ... -0.96739435  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3035e-05 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9750
[-0.         0.        -0.        ... -0.9699126  0.         0.       ]
Sparsity at: 0.6458724517167382
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1112e-05 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9749
[-0.          0.         -0.         ... -0.97255224  0.
  0.        ]
Sparsity at: 0.6458724517167382
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9329e-05 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9750
[-0.         0.        -0.        ... -0.9753028  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7664e-05 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9751
[-0.          0.         -0.         ... -0.97817564  0.
 -0.        ]
Sparsity at: 0.6458724517167382
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6130e-05 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9750
[-0.         0.        -0.        ... -0.9811863  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4737e-05 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9749
[-0.         0.        -0.        ... -0.9843151  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3429e-05 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9749
[-0.          0.         -0.         ... -0.98756605  0.
 -0.        ]
Sparsity at: 0.6458724517167382
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2235e-05 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9749
[-0.          0.         -0.         ... -0.99092084  0.
 -0.        ]
Sparsity at: 0.6458724517167382
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1129e-05 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9749
[-0.        0.       -0.       ... -0.994429  0.       -0.      ]
Sparsity at: 0.6458724517167382
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0126e-05 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9751
[-0.          0.         -0.         ... -0.99803513  0.
 -0.        ]
Sparsity at: 0.6458724517167382
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 9.1957e-06 - accuracy: 1.0000 - val_loss: 0.2154 - val_accuracy: 0.9751
[-0.         0.        -0.        ... -1.0017804  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3395e-06 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 0.9751
[-0.         0.        -0.        ... -1.0055803  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 7.5572e-06 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9750
[-0.         0.        -0.        ... -1.0094881  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8534e-06 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9750
[-0.         0.        -0.        ... -1.0135279  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1955e-06 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9749
[-0.         0.        -0.        ... -1.0176321  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6008e-06 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9748
[-0.         0.        -0.        ... -1.0218481  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0619e-06 - accuracy: 1.0000 - val_loss: 0.2229 - val_accuracy: 0.9749
[-0.         0.        -0.        ... -1.0262133  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5736e-06 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9749
[-0.         0.        -0.        ... -1.0305617  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 98/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1190e-06 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 0.9752
[-0.         0.        -0.        ... -1.0350757  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7181e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9752
[-0.         0.        -0.        ... -1.0395976  0.        -0.       ]
Sparsity at: 0.6458724517167382
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3489e-06 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 0.9752
[-0.        0.       -0.       ... -1.044248  0.       -0.      ]
Sparsity at: 0.6458724517167382
Epoch 101/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0431 - accuracy: 0.9875 - val_loss: 0.1635 - val_accuracy: 0.9720
[-0.         0.        -0.        ... -1.0806397  0.        -0.       ]
Sparsity at: 0.759438707081545
Epoch 102/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0104 - accuracy: 0.9965 - val_loss: 0.1573 - val_accuracy: 0.9738
[-0.         0.        -0.        ... -1.0674495  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9987 - val_loss: 0.1561 - val_accuracy: 0.9739
[-0.        0.       -0.       ... -1.067485  0.        0.      ]
Sparsity at: 0.759438707081545
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9996 - val_loss: 0.1568 - val_accuracy: 0.9741
[-0.         0.        -0.        ... -1.0717487  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.1573 - val_accuracy: 0.9742
[-0.         0.        -0.        ... -1.0771368  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 0.9999 - val_loss: 0.1583 - val_accuracy: 0.9742
[-0.         0.        -0.        ... -1.0827911  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1593 - val_accuracy: 0.9742
[-0.         0.        -0.        ... -1.0886633  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1601 - val_accuracy: 0.9743
[-0.         0.        -0.        ... -1.0946239  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1608 - val_accuracy: 0.9742
[-0.        0.       -0.       ... -1.100825  0.        0.      ]
Sparsity at: 0.759438707081545
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1616 - val_accuracy: 0.9743
[-0.         0.        -0.        ... -1.1069539  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5225e-04 - accuracy: 1.0000 - val_loss: 0.1623 - val_accuracy: 0.9744
[-0.         0.        -0.        ... -1.1135459  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 8.5302e-04 - accuracy: 1.0000 - val_loss: 0.1632 - val_accuracy: 0.9743
[-0.         0.        -0.        ... -1.1199894  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 7.6585e-04 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9742
[-0.         0.        -0.        ... -1.1266072  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 6.9055e-04 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -1.1335133  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2432e-04 - accuracy: 1.0000 - val_loss: 0.1659 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -1.1405433  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6487e-04 - accuracy: 1.0000 - val_loss: 0.1669 - val_accuracy: 0.9746
[-0.         0.        -0.        ... -1.1476506  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1332e-04 - accuracy: 1.0000 - val_loss: 0.1678 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.1551005  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6562e-04 - accuracy: 1.0000 - val_loss: 0.1687 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.1628269  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2392e-04 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.1707535  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8579e-04 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9744
[-0.         0.        -0.        ... -1.1787006  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5043e-04 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9744
[-0.         0.        -0.        ... -1.1869619  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1863e-04 - accuracy: 1.0000 - val_loss: 0.1729 - val_accuracy: 0.9743
[-0.         0.        -0.        ... -1.1952964  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9000e-04 - accuracy: 1.0000 - val_loss: 0.1740 - val_accuracy: 0.9743
[-0.         0.        -0.        ... -1.2040031  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6367e-04 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.2128332  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4042e-04 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.2215818  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1740e-04 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9742
[-0.         0.        -0.        ... -1.2306157  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9742e-04 - accuracy: 1.0000 - val_loss: 0.1789 - val_accuracy: 0.9743
[-0.         0.        -0.        ... -1.2397698  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7974e-04 - accuracy: 1.0000 - val_loss: 0.1802 - val_accuracy: 0.9744
[-0.         0.        -0.        ... -1.2489645  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6289e-04 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.2582648  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4785e-04 - accuracy: 1.0000 - val_loss: 0.1829 - val_accuracy: 0.9746
[-0.         0.        -0.        ... -1.2677523  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3371e-04 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9746
[-0.         0.        -0.        ... -1.2773597  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2098e-04 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9742
[-0.         0.        -0.        ... -1.2868885  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0975e-04 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9744
[-0.         0.        -0.        ... -1.2965337  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 9.9275e-05 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9744
[-0.        0.       -0.       ... -1.306352  0.        0.      ]
Sparsity at: 0.759438707081545
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 8.9800e-05 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.3162463  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 8.1073e-05 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9746
[-0.         0.        -0.        ... -1.3262224  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3143e-05 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -1.3364494  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 138/500
235/235 [==============================] - 2s 9ms/step - loss: 6.6010e-05 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -1.3465014  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9599e-05 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -1.3567601  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3671e-05 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9748
[-0.         0.        -0.        ... -1.3667188  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8388e-05 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -1.3769611  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3653e-05 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -1.3872551  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9302e-05 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9747
[-0.         0.        -0.        ... -1.3972656  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5376e-05 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.4078622  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1764e-05 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.4180739  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8552e-05 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9744
[-0.         0.        -0.        ... -1.4283988  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5641e-05 - accuracy: 1.0000 - val_loss: 0.2113 - val_accuracy: 0.9744
[-0.         0.        -0.        ... -1.4384574  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3114e-05 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9746
[-0.         0.        -0.        ... -1.4489262  0.        -0.       ]
Sparsity at: 0.759438707081545
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0759e-05 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.4591879  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8644e-05 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9745
[-0.         0.        -0.        ... -1.4693817  0.         0.       ]
Sparsity at: 0.759438707081545
Epoch 151/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0921 - accuracy: 0.9755 - val_loss: 0.1920 - val_accuracy: 0.9658
[-0.         0.        -0.        ... -1.3002248 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 152/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0358 - accuracy: 0.9883 - val_loss: 0.1806 - val_accuracy: 0.9672
[-0.         0.        -0.        ... -1.2823541 -0.        -0.       ]
Sparsity at: 0.8448229613733905
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0259 - accuracy: 0.9911 - val_loss: 0.1750 - val_accuracy: 0.9680
[-0.         0.        -0.        ... -1.2756494 -0.        -0.       ]
Sparsity at: 0.8448229613733905
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0206 - accuracy: 0.9931 - val_loss: 0.1719 - val_accuracy: 0.9686
[-0.         0.        -0.        ... -1.2714455 -0.        -0.       ]
Sparsity at: 0.8448229613733905
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0172 - accuracy: 0.9944 - val_loss: 0.1697 - val_accuracy: 0.9689
[-0.         0.        -0.        ... -1.2680866 -0.        -0.       ]
Sparsity at: 0.8448229613733905
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0147 - accuracy: 0.9955 - val_loss: 0.1683 - val_accuracy: 0.9691
[-0.         0.        -0.        ... -1.2659676 -0.        -0.       ]
Sparsity at: 0.8448229613733905
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0128 - accuracy: 0.9963 - val_loss: 0.1672 - val_accuracy: 0.9697
[-0.         0.        -0.        ... -1.2656113 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0113 - accuracy: 0.9969 - val_loss: 0.1666 - val_accuracy: 0.9700
[-0.         0.        -0.        ... -1.2666739 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0101 - accuracy: 0.9974 - val_loss: 0.1662 - val_accuracy: 0.9700
[-0.         0.        -0.        ... -1.2690679 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0091 - accuracy: 0.9980 - val_loss: 0.1660 - val_accuracy: 0.9699
[-0.         0.        -0.        ... -1.2734616 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0083 - accuracy: 0.9983 - val_loss: 0.1661 - val_accuracy: 0.9700
[-0.         0.        -0.        ... -1.2793527 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0076 - accuracy: 0.9986 - val_loss: 0.1662 - val_accuracy: 0.9706
[-0.         0.        -0.        ... -1.2858511 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0069 - accuracy: 0.9989 - val_loss: 0.1665 - val_accuracy: 0.9705
[-0.         0.        -0.        ... -1.2927115 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0064 - accuracy: 0.9991 - val_loss: 0.1669 - val_accuracy: 0.9703
[-0.         0.        -0.        ... -1.3002685 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0059 - accuracy: 0.9993 - val_loss: 0.1673 - val_accuracy: 0.9705
[-0.         0.        -0.        ... -1.3080535 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0054 - accuracy: 0.9995 - val_loss: 0.1679 - val_accuracy: 0.9707
[-0.        0.       -0.       ... -1.315829 -0.        0.      ]
Sparsity at: 0.8448229613733905
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0050 - accuracy: 0.9997 - val_loss: 0.1686 - val_accuracy: 0.9706
[-0.         0.        -0.        ... -1.3236148 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9997 - val_loss: 0.1693 - val_accuracy: 0.9704
[-0.         0.        -0.        ... -1.3319994 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9998 - val_loss: 0.1702 - val_accuracy: 0.9702
[-0.         0.        -0.        ... -1.3405224 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0040 - accuracy: 0.9999 - val_loss: 0.1710 - val_accuracy: 0.9702
[-0.         0.        -0.        ... -1.3489258 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0037 - accuracy: 0.9999 - val_loss: 0.1719 - val_accuracy: 0.9705
[-0.         0.        -0.        ... -1.3582197 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9999 - val_loss: 0.1729 - val_accuracy: 0.9704
[-0.         0.        -0.        ... -1.3671787 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 0.9999 - val_loss: 0.1739 - val_accuracy: 0.9705
[-0.         0.        -0.        ... -1.3767401 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0030 - accuracy: 0.9999 - val_loss: 0.1750 - val_accuracy: 0.9706
[-0.         0.        -0.        ... -1.3861424 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.1762 - val_accuracy: 0.9706
[-0.         0.        -0.        ... -1.3957092 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.1774 - val_accuracy: 0.9705
[-0.        0.       -0.       ... -1.405351 -0.        0.      ]
Sparsity at: 0.8448229613733905
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9704
[-0.         0.        -0.        ... -1.4152862 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1798 - val_accuracy: 0.9701
[-0.         0.        -0.        ... -1.4253176 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9703
[-0.        0.       -0.       ... -1.435876 -0.        0.      ]
Sparsity at: 0.8448229613733905
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1827 - val_accuracy: 0.9702
[-0.         0.        -0.        ... -1.4465294 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 181/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9703
[-0.        0.       -0.       ... -1.457261 -0.        0.      ]
Sparsity at: 0.8448229613733905
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.9705
[-0.         0.        -0.        ... -1.4676903 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9705
[-0.         0.        -0.        ... -1.4788667 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9707
[-0.         0.        -0.        ... -1.4904815 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9707
[-0.         0.        -0.        ... -1.5023797 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9706
[-0.        0.       -0.       ... -1.514085 -0.        0.      ]
Sparsity at: 0.8448229613733905
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9708
[-0.         0.        -0.        ... -1.5256187 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1949 - val_accuracy: 0.9707
[-0.         0.        -0.        ... -1.5371948 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9706
[-0.        0.       -0.       ... -1.549159 -0.        0.      ]
Sparsity at: 0.8448229613733905
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9708
[-0.         0.        -0.        ... -1.5611707 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3864e-04 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9706
[-0.         0.        -0.        ... -1.5731853 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 8.7673e-04 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9705
[-0.        0.       -0.       ... -1.585135 -0.        0.      ]
Sparsity at: 0.8448229613733905
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2159e-04 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9705
[-0.        0.       -0.       ... -1.597745 -0.        0.      ]
Sparsity at: 0.8448229613733905
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 7.6858e-04 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9704
[-0.         0.        -0.        ... -1.6100304 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 7.1607e-04 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9705
[-0.         0.        -0.        ... -1.6226298 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6920e-04 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9703
[-0.         0.        -0.        ... -1.6350399 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2534e-04 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9703
[-0.         0.        -0.        ... -1.6470389 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 5.8364e-04 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9704
[-0.         0.        -0.        ... -1.6594774 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 5.4626e-04 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9703
[-0.         0.        -0.        ... -1.6723495 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0987e-04 - accuracy: 1.0000 - val_loss: 0.2159 - val_accuracy: 0.9703
[-0.         0.        -0.        ... -1.6844566 -0.         0.       ]
Sparsity at: 0.8448229613733905
Epoch 201/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1797 - accuracy: 0.9490 - val_loss: 0.1993 - val_accuracy: 0.9515
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 202/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1006 - accuracy: 0.9682 - val_loss: 0.1783 - val_accuracy: 0.9553
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0850 - accuracy: 0.9722 - val_loss: 0.1676 - val_accuracy: 0.9572
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0764 - accuracy: 0.9750 - val_loss: 0.1606 - val_accuracy: 0.9587
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0705 - accuracy: 0.9767 - val_loss: 0.1556 - val_accuracy: 0.9599
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 206/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0661 - accuracy: 0.9783 - val_loss: 0.1517 - val_accuracy: 0.9612
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0626 - accuracy: 0.9796 - val_loss: 0.1488 - val_accuracy: 0.9614
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0597 - accuracy: 0.9806 - val_loss: 0.1464 - val_accuracy: 0.9615
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0573 - accuracy: 0.9814 - val_loss: 0.1443 - val_accuracy: 0.9620
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0551 - accuracy: 0.9819 - val_loss: 0.1426 - val_accuracy: 0.9621
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0533 - accuracy: 0.9824 - val_loss: 0.1412 - val_accuracy: 0.9623
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0516 - accuracy: 0.9830 - val_loss: 0.1400 - val_accuracy: 0.9625
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0501 - accuracy: 0.9834 - val_loss: 0.1389 - val_accuracy: 0.9629
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0487 - accuracy: 0.9840 - val_loss: 0.1381 - val_accuracy: 0.9633
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0475 - accuracy: 0.9844 - val_loss: 0.1373 - val_accuracy: 0.9638
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0464 - accuracy: 0.9848 - val_loss: 0.1367 - val_accuracy: 0.9637
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0453 - accuracy: 0.9851 - val_loss: 0.1361 - val_accuracy: 0.9643
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0443 - accuracy: 0.9855 - val_loss: 0.1356 - val_accuracy: 0.9644
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 219/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0434 - accuracy: 0.9859 - val_loss: 0.1352 - val_accuracy: 0.9648
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 220/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0426 - accuracy: 0.9861 - val_loss: 0.1349 - val_accuracy: 0.9648
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0418 - accuracy: 0.9864 - val_loss: 0.1347 - val_accuracy: 0.9652
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0410 - accuracy: 0.9867 - val_loss: 0.1345 - val_accuracy: 0.9652
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0403 - accuracy: 0.9870 - val_loss: 0.1344 - val_accuracy: 0.9650
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0396 - accuracy: 0.9872 - val_loss: 0.1343 - val_accuracy: 0.9648
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0390 - accuracy: 0.9873 - val_loss: 0.1343 - val_accuracy: 0.9650
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0384 - accuracy: 0.9877 - val_loss: 0.1344 - val_accuracy: 0.9651
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0378 - accuracy: 0.9878 - val_loss: 0.1344 - val_accuracy: 0.9652
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0372 - accuracy: 0.9880 - val_loss: 0.1345 - val_accuracy: 0.9653
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 229/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0367 - accuracy: 0.9882 - val_loss: 0.1347 - val_accuracy: 0.9652
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0362 - accuracy: 0.9884 - val_loss: 0.1348 - val_accuracy: 0.9653
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0357 - accuracy: 0.9885 - val_loss: 0.1351 - val_accuracy: 0.9652
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0352 - accuracy: 0.9890 - val_loss: 0.1353 - val_accuracy: 0.9654
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0348 - accuracy: 0.9891 - val_loss: 0.1355 - val_accuracy: 0.9656
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 234/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0344 - accuracy: 0.9893 - val_loss: 0.1359 - val_accuracy: 0.9656
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0339 - accuracy: 0.9894 - val_loss: 0.1362 - val_accuracy: 0.9656
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0335 - accuracy: 0.9897 - val_loss: 0.1365 - val_accuracy: 0.9657
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0331 - accuracy: 0.9898 - val_loss: 0.1368 - val_accuracy: 0.9658
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0327 - accuracy: 0.9900 - val_loss: 0.1372 - val_accuracy: 0.9659
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0324 - accuracy: 0.9901 - val_loss: 0.1376 - val_accuracy: 0.9660
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0320 - accuracy: 0.9902 - val_loss: 0.1380 - val_accuracy: 0.9663
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 241/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0317 - accuracy: 0.9903 - val_loss: 0.1385 - val_accuracy: 0.9663
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0313 - accuracy: 0.9905 - val_loss: 0.1388 - val_accuracy: 0.9662
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0310 - accuracy: 0.9906 - val_loss: 0.1393 - val_accuracy: 0.9662
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0307 - accuracy: 0.9908 - val_loss: 0.1398 - val_accuracy: 0.9662
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0304 - accuracy: 0.9908 - val_loss: 0.1402 - val_accuracy: 0.9661
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0301 - accuracy: 0.9909 - val_loss: 0.1407 - val_accuracy: 0.9661
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0298 - accuracy: 0.9911 - val_loss: 0.1412 - val_accuracy: 0.9659
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0295 - accuracy: 0.9912 - val_loss: 0.1416 - val_accuracy: 0.9657
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0292 - accuracy: 0.9913 - val_loss: 0.1422 - val_accuracy: 0.9657
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0289 - accuracy: 0.9914 - val_loss: 0.1427 - val_accuracy: 0.9656
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 251/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4640 - accuracy: 0.8569 - val_loss: 0.3334 - val_accuracy: 0.8991
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 252/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2711 - accuracy: 0.9116 - val_loss: 0.2809 - val_accuracy: 0.9156
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2373 - accuracy: 0.9226 - val_loss: 0.2586 - val_accuracy: 0.9203
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2195 - accuracy: 0.9280 - val_loss: 0.2453 - val_accuracy: 0.9253
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2078 - accuracy: 0.9323 - val_loss: 0.2361 - val_accuracy: 0.9278
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1991 - accuracy: 0.9352 - val_loss: 0.2292 - val_accuracy: 0.9292
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1924 - accuracy: 0.9372 - val_loss: 0.2237 - val_accuracy: 0.9312
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1870 - accuracy: 0.9393 - val_loss: 0.2193 - val_accuracy: 0.9325
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1825 - accuracy: 0.9408 - val_loss: 0.2156 - val_accuracy: 0.9331
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1786 - accuracy: 0.9419 - val_loss: 0.2124 - val_accuracy: 0.9339
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1753 - accuracy: 0.9434 - val_loss: 0.2096 - val_accuracy: 0.9351
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1724 - accuracy: 0.9444 - val_loss: 0.2072 - val_accuracy: 0.9355
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1698 - accuracy: 0.9452 - val_loss: 0.2050 - val_accuracy: 0.9364
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1676 - accuracy: 0.9459 - val_loss: 0.2031 - val_accuracy: 0.9381
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1655 - accuracy: 0.9467 - val_loss: 0.2014 - val_accuracy: 0.9390
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1637 - accuracy: 0.9475 - val_loss: 0.1998 - val_accuracy: 0.9392
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1620 - accuracy: 0.9482 - val_loss: 0.1984 - val_accuracy: 0.9391
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 268/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1605 - accuracy: 0.9488 - val_loss: 0.1971 - val_accuracy: 0.9394
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 269/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1590 - accuracy: 0.9489 - val_loss: 0.1959 - val_accuracy: 0.9398
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1577 - accuracy: 0.9495 - val_loss: 0.1949 - val_accuracy: 0.9400
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 271/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1565 - accuracy: 0.9497 - val_loss: 0.1939 - val_accuracy: 0.9402
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1554 - accuracy: 0.9501 - val_loss: 0.1930 - val_accuracy: 0.9401
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1544 - accuracy: 0.9503 - val_loss: 0.1922 - val_accuracy: 0.9409
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1533 - accuracy: 0.9509 - val_loss: 0.1914 - val_accuracy: 0.9410
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.1907 - val_accuracy: 0.9412
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1516 - accuracy: 0.9513 - val_loss: 0.1901 - val_accuracy: 0.9414
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1507 - accuracy: 0.9517 - val_loss: 0.1895 - val_accuracy: 0.9415
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 278/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1499 - accuracy: 0.9519 - val_loss: 0.1890 - val_accuracy: 0.9415
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1492 - accuracy: 0.9522 - val_loss: 0.1885 - val_accuracy: 0.9418
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1485 - accuracy: 0.9524 - val_loss: 0.1880 - val_accuracy: 0.9419
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1478 - accuracy: 0.9526 - val_loss: 0.1876 - val_accuracy: 0.9422
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1472 - accuracy: 0.9527 - val_loss: 0.1872 - val_accuracy: 0.9427
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1466 - accuracy: 0.9529 - val_loss: 0.1868 - val_accuracy: 0.9430
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1460 - accuracy: 0.9530 - val_loss: 0.1864 - val_accuracy: 0.9433
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1455 - accuracy: 0.9532 - val_loss: 0.1861 - val_accuracy: 0.9435
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1449 - accuracy: 0.9538 - val_loss: 0.1858 - val_accuracy: 0.9435
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1444 - accuracy: 0.9539 - val_loss: 0.1855 - val_accuracy: 0.9436
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1439 - accuracy: 0.9542 - val_loss: 0.1853 - val_accuracy: 0.9439
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1434 - accuracy: 0.9544 - val_loss: 0.1851 - val_accuracy: 0.9439
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1430 - accuracy: 0.9544 - val_loss: 0.1848 - val_accuracy: 0.9434
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1426 - accuracy: 0.9546 - val_loss: 0.1846 - val_accuracy: 0.9437
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1422 - accuracy: 0.9546 - val_loss: 0.1845 - val_accuracy: 0.9439
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1417 - accuracy: 0.9548 - val_loss: 0.1843 - val_accuracy: 0.9442
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1413 - accuracy: 0.9549 - val_loss: 0.1841 - val_accuracy: 0.9444
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1410 - accuracy: 0.9550 - val_loss: 0.1840 - val_accuracy: 0.9448
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1406 - accuracy: 0.9552 - val_loss: 0.1838 - val_accuracy: 0.9447
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1402 - accuracy: 0.9553 - val_loss: 0.1837 - val_accuracy: 0.9448
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 298/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1399 - accuracy: 0.9554 - val_loss: 0.1835 - val_accuracy: 0.9449
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1395 - accuracy: 0.9555 - val_loss: 0.1834 - val_accuracy: 0.9447
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1392 - accuracy: 0.9556 - val_loss: 0.1833 - val_accuracy: 0.9449
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 301/500
235/235 [==============================] - 2s 8ms/step - loss: 0.7708 - accuracy: 0.7487 - val_loss: 0.6593 - val_accuracy: 0.8015
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 302/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6307 - accuracy: 0.8051 - val_loss: 0.6132 - val_accuracy: 0.8166
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5977 - accuracy: 0.8188 - val_loss: 0.5929 - val_accuracy: 0.8244
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5806 - accuracy: 0.8247 - val_loss: 0.5803 - val_accuracy: 0.8277
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5687 - accuracy: 0.8275 - val_loss: 0.5709 - val_accuracy: 0.8307
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5595 - accuracy: 0.8300 - val_loss: 0.5634 - val_accuracy: 0.8324
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5517 - accuracy: 0.8321 - val_loss: 0.5570 - val_accuracy: 0.8339
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5451 - accuracy: 0.8340 - val_loss: 0.5514 - val_accuracy: 0.8352
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5393 - accuracy: 0.8353 - val_loss: 0.5466 - val_accuracy: 0.8363
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5342 - accuracy: 0.8365 - val_loss: 0.5424 - val_accuracy: 0.8374
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5299 - accuracy: 0.8375 - val_loss: 0.5388 - val_accuracy: 0.8382
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5261 - accuracy: 0.8384 - val_loss: 0.5357 - val_accuracy: 0.8396
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5228 - accuracy: 0.8391 - val_loss: 0.5328 - val_accuracy: 0.8400
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5199 - accuracy: 0.8395 - val_loss: 0.5302 - val_accuracy: 0.8406
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5173 - accuracy: 0.8403 - val_loss: 0.5279 - val_accuracy: 0.8414
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5149 - accuracy: 0.8408 - val_loss: 0.5258 - val_accuracy: 0.8422
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5128 - accuracy: 0.8414 - val_loss: 0.5239 - val_accuracy: 0.8422
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5108 - accuracy: 0.8416 - val_loss: 0.5221 - val_accuracy: 0.8427
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5090 - accuracy: 0.8423 - val_loss: 0.5205 - val_accuracy: 0.8425
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5073 - accuracy: 0.8428 - val_loss: 0.5190 - val_accuracy: 0.8424
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5057 - accuracy: 0.8433 - val_loss: 0.5177 - val_accuracy: 0.8433
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5042 - accuracy: 0.8434 - val_loss: 0.5164 - val_accuracy: 0.8437
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5028 - accuracy: 0.8439 - val_loss: 0.5152 - val_accuracy: 0.8445
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5015 - accuracy: 0.8442 - val_loss: 0.5141 - val_accuracy: 0.8450
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5003 - accuracy: 0.8444 - val_loss: 0.5131 - val_accuracy: 0.8449
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4991 - accuracy: 0.8449 - val_loss: 0.5121 - val_accuracy: 0.8452
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4980 - accuracy: 0.8455 - val_loss: 0.5112 - val_accuracy: 0.8456
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4969 - accuracy: 0.8458 - val_loss: 0.5103 - val_accuracy: 0.8463
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4960 - accuracy: 0.8461 - val_loss: 0.5095 - val_accuracy: 0.8462
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4950 - accuracy: 0.8465 - val_loss: 0.5087 - val_accuracy: 0.8468
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4941 - accuracy: 0.8465 - val_loss: 0.5080 - val_accuracy: 0.8469
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 332/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4932 - accuracy: 0.8472 - val_loss: 0.5073 - val_accuracy: 0.8471
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4924 - accuracy: 0.8475 - val_loss: 0.5067 - val_accuracy: 0.8471
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 334/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4916 - accuracy: 0.8480 - val_loss: 0.5061 - val_accuracy: 0.8479
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4908 - accuracy: 0.8481 - val_loss: 0.5055 - val_accuracy: 0.8482
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4901 - accuracy: 0.8483 - val_loss: 0.5049 - val_accuracy: 0.8490
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4894 - accuracy: 0.8488 - val_loss: 0.5044 - val_accuracy: 0.8492
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4887 - accuracy: 0.8490 - val_loss: 0.5040 - val_accuracy: 0.8495
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 339/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4880 - accuracy: 0.8493 - val_loss: 0.5035 - val_accuracy: 0.8497
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4874 - accuracy: 0.8495 - val_loss: 0.5031 - val_accuracy: 0.8496
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4868 - accuracy: 0.8496 - val_loss: 0.5026 - val_accuracy: 0.8499
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4862 - accuracy: 0.8498 - val_loss: 0.5023 - val_accuracy: 0.8503
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4856 - accuracy: 0.8497 - val_loss: 0.5019 - val_accuracy: 0.8509
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4851 - accuracy: 0.8497 - val_loss: 0.5015 - val_accuracy: 0.8508
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4846 - accuracy: 0.8503 - val_loss: 0.5011 - val_accuracy: 0.8510
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4840 - accuracy: 0.8505 - val_loss: 0.5008 - val_accuracy: 0.8514
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4835 - accuracy: 0.8504 - val_loss: 0.5005 - val_accuracy: 0.8515
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4830 - accuracy: 0.8503 - val_loss: 0.5001 - val_accuracy: 0.8513
[-0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4826 - accuracy: 0.8504 - val_loss: 0.4999 - val_accuracy: 0.8512
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4821 - accuracy: 0.8508 - val_loss: 0.4996 - val_accuracy: 0.8515
[-0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9718515289699571
Epoch 351/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6082 - accuracy: 0.4311 - val_loss: 1.5056 - val_accuracy: 0.4505
[-0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 352/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4894 - accuracy: 0.4552 - val_loss: 1.4884 - val_accuracy: 0.4435
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4762 - accuracy: 0.4550 - val_loss: 1.4795 - val_accuracy: 0.4572
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4681 - accuracy: 0.4604 - val_loss: 1.4729 - val_accuracy: 0.4613
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4619 - accuracy: 0.4629 - val_loss: 1.4676 - val_accuracy: 0.4638
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4571 - accuracy: 0.4653 - val_loss: 1.4633 - val_accuracy: 0.4667
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4531 - accuracy: 0.4671 - val_loss: 1.4597 - val_accuracy: 0.4697
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4498 - accuracy: 0.4685 - val_loss: 1.4568 - val_accuracy: 0.4712
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4472 - accuracy: 0.4695 - val_loss: 1.4545 - val_accuracy: 0.4713
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4450 - accuracy: 0.4701 - val_loss: 1.4526 - val_accuracy: 0.4727
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4432 - accuracy: 0.4707 - val_loss: 1.4510 - val_accuracy: 0.4733
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4415 - accuracy: 0.4711 - val_loss: 1.4495 - val_accuracy: 0.4739
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4400 - accuracy: 0.4714 - val_loss: 1.4481 - val_accuracy: 0.4742
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4386 - accuracy: 0.4718 - val_loss: 1.4468 - val_accuracy: 0.4745
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4372 - accuracy: 0.4721 - val_loss: 1.4456 - val_accuracy: 0.4748
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4359 - accuracy: 0.4726 - val_loss: 1.4443 - val_accuracy: 0.4751
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4347 - accuracy: 0.4724 - val_loss: 1.4431 - val_accuracy: 0.4754
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4334 - accuracy: 0.4725 - val_loss: 1.4418 - val_accuracy: 0.4764
[-0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4322 - accuracy: 0.4731 - val_loss: 1.4406 - val_accuracy: 0.4768
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4310 - accuracy: 0.4734 - val_loss: 1.4394 - val_accuracy: 0.4775
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4299 - accuracy: 0.4739 - val_loss: 1.4383 - val_accuracy: 0.4775
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4287 - accuracy: 0.4743 - val_loss: 1.4370 - val_accuracy: 0.4777
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4274 - accuracy: 0.4752 - val_loss: 1.4356 - val_accuracy: 0.4786
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4259 - accuracy: 0.4760 - val_loss: 1.4338 - val_accuracy: 0.4792
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4239 - accuracy: 0.4768 - val_loss: 1.4313 - val_accuracy: 0.4799
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4214 - accuracy: 0.4777 - val_loss: 1.4281 - val_accuracy: 0.4794
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4187 - accuracy: 0.4776 - val_loss: 1.4250 - val_accuracy: 0.4798
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4163 - accuracy: 0.4778 - val_loss: 1.4227 - val_accuracy: 0.4798
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4145 - accuracy: 0.4775 - val_loss: 1.4212 - val_accuracy: 0.4807
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4132 - accuracy: 0.4774 - val_loss: 1.4202 - val_accuracy: 0.4811
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4123 - accuracy: 0.4774 - val_loss: 1.4195 - val_accuracy: 0.4810
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4115 - accuracy: 0.4776 - val_loss: 1.4188 - val_accuracy: 0.4805
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4108 - accuracy: 0.4778 - val_loss: 1.4183 - val_accuracy: 0.4807
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4102 - accuracy: 0.4782 - val_loss: 1.4178 - val_accuracy: 0.4804
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 385/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4096 - accuracy: 0.4785 - val_loss: 1.4173 - val_accuracy: 0.4803
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4091 - accuracy: 0.4784 - val_loss: 1.4168 - val_accuracy: 0.4801
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 387/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4085 - accuracy: 0.4787 - val_loss: 1.4163 - val_accuracy: 0.4803
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4081 - accuracy: 0.4788 - val_loss: 1.4158 - val_accuracy: 0.4786
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4076 - accuracy: 0.4788 - val_loss: 1.4154 - val_accuracy: 0.4793
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4071 - accuracy: 0.4791 - val_loss: 1.4149 - val_accuracy: 0.4794
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4066 - accuracy: 0.4794 - val_loss: 1.4145 - val_accuracy: 0.4800
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4062 - accuracy: 0.4794 - val_loss: 1.4141 - val_accuracy: 0.4800
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 393/500
235/235 [==============================] - 2s 7ms/step - loss: 1.4057 - accuracy: 0.4798 - val_loss: 1.4137 - val_accuracy: 0.4799
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4053 - accuracy: 0.4799 - val_loss: 1.4133 - val_accuracy: 0.4799
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4048 - accuracy: 0.4805 - val_loss: 1.4129 - val_accuracy: 0.4799
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4044 - accuracy: 0.4805 - val_loss: 1.4125 - val_accuracy: 0.4803
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4039 - accuracy: 0.4810 - val_loss: 1.4121 - val_accuracy: 0.4803
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 398/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4035 - accuracy: 0.4810 - val_loss: 1.4116 - val_accuracy: 0.4809
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 399/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4030 - accuracy: 0.4815 - val_loss: 1.4112 - val_accuracy: 0.4807
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4026 - accuracy: 0.4817 - val_loss: 1.4108 - val_accuracy: 0.4810
[-0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 401/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8316 - accuracy: 0.3411 - val_loss: 1.7511 - val_accuracy: 0.3464
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 402/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7366 - accuracy: 0.3586 - val_loss: 1.7362 - val_accuracy: 0.3565
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7299 - accuracy: 0.3627 - val_loss: 1.7316 - val_accuracy: 0.3573
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 404/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7275 - accuracy: 0.3630 - val_loss: 1.7294 - val_accuracy: 0.3578
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 405/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7261 - accuracy: 0.3630 - val_loss: 1.7281 - val_accuracy: 0.3580
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7252 - accuracy: 0.3631 - val_loss: 1.7272 - val_accuracy: 0.3586
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7244 - accuracy: 0.3630 - val_loss: 1.7265 - val_accuracy: 0.3586
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7238 - accuracy: 0.3630 - val_loss: 1.7259 - val_accuracy: 0.3587
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7234 - accuracy: 0.3631 - val_loss: 1.7254 - val_accuracy: 0.3586
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7229 - accuracy: 0.3632 - val_loss: 1.7250 - val_accuracy: 0.3588
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7225 - accuracy: 0.3633 - val_loss: 1.7247 - val_accuracy: 0.3591
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7221 - accuracy: 0.3635 - val_loss: 1.7244 - val_accuracy: 0.3592
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7217 - accuracy: 0.3636 - val_loss: 1.7241 - val_accuracy: 0.3599
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7214 - accuracy: 0.3636 - val_loss: 1.7237 - val_accuracy: 0.3601
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7210 - accuracy: 0.3638 - val_loss: 1.7235 - val_accuracy: 0.3600
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7207 - accuracy: 0.3644 - val_loss: 1.7232 - val_accuracy: 0.3600
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7204 - accuracy: 0.3641 - val_loss: 1.7229 - val_accuracy: 0.3602
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7201 - accuracy: 0.3645 - val_loss: 1.7227 - val_accuracy: 0.3603
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7198 - accuracy: 0.3643 - val_loss: 1.7225 - val_accuracy: 0.3607
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7195 - accuracy: 0.3648 - val_loss: 1.7223 - val_accuracy: 0.3607
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7193 - accuracy: 0.3649 - val_loss: 1.7221 - val_accuracy: 0.3604
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7190 - accuracy: 0.3650 - val_loss: 1.7219 - val_accuracy: 0.3600
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7188 - accuracy: 0.3650 - val_loss: 1.7217 - val_accuracy: 0.3602
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 424/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7186 - accuracy: 0.3652 - val_loss: 1.7216 - val_accuracy: 0.3605
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7183 - accuracy: 0.3653 - val_loss: 1.7214 - val_accuracy: 0.3609
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7182 - accuracy: 0.3656 - val_loss: 1.7213 - val_accuracy: 0.3610
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7179 - accuracy: 0.3656 - val_loss: 1.7212 - val_accuracy: 0.3610
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7178 - accuracy: 0.3657 - val_loss: 1.7211 - val_accuracy: 0.3611
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7176 - accuracy: 0.3659 - val_loss: 1.7210 - val_accuracy: 0.3613
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7174 - accuracy: 0.3660 - val_loss: 1.7209 - val_accuracy: 0.3614
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7173 - accuracy: 0.3659 - val_loss: 1.7208 - val_accuracy: 0.3617
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7171 - accuracy: 0.3660 - val_loss: 1.7207 - val_accuracy: 0.3615
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7170 - accuracy: 0.3660 - val_loss: 1.7206 - val_accuracy: 0.3617
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7168 - accuracy: 0.3661 - val_loss: 1.7205 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7167 - accuracy: 0.3661 - val_loss: 1.7205 - val_accuracy: 0.3620
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7166 - accuracy: 0.3662 - val_loss: 1.7204 - val_accuracy: 0.3618
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7165 - accuracy: 0.3663 - val_loss: 1.7203 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7163 - accuracy: 0.3662 - val_loss: 1.7203 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7163 - accuracy: 0.3663 - val_loss: 1.7202 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 440/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7161 - accuracy: 0.3664 - val_loss: 1.7201 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7160 - accuracy: 0.3665 - val_loss: 1.7201 - val_accuracy: 0.3620
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7160 - accuracy: 0.3667 - val_loss: 1.7200 - val_accuracy: 0.3616
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7158 - accuracy: 0.3665 - val_loss: 1.7199 - val_accuracy: 0.3617
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7157 - accuracy: 0.3666 - val_loss: 1.7199 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7157 - accuracy: 0.3666 - val_loss: 1.7199 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7156 - accuracy: 0.3665 - val_loss: 1.7198 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7155 - accuracy: 0.3666 - val_loss: 1.7197 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7154 - accuracy: 0.3666 - val_loss: 1.7197 - val_accuracy: 0.3618
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7154 - accuracy: 0.3666 - val_loss: 1.7196 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7153 - accuracy: 0.3667 - val_loss: 1.7196 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7152 - accuracy: 0.3668 - val_loss: 1.7195 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 452/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7151 - accuracy: 0.3668 - val_loss: 1.7195 - val_accuracy: 0.3620
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7150 - accuracy: 0.3668 - val_loss: 1.7195 - val_accuracy: 0.3620
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7149 - accuracy: 0.3668 - val_loss: 1.7194 - val_accuracy: 0.3621
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7149 - accuracy: 0.3668 - val_loss: 1.7193 - val_accuracy: 0.3620
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7148 - accuracy: 0.3670 - val_loss: 1.7193 - val_accuracy: 0.3618
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7148 - accuracy: 0.3670 - val_loss: 1.7192 - val_accuracy: 0.3620
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7147 - accuracy: 0.3671 - val_loss: 1.7192 - val_accuracy: 0.3619
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7146 - accuracy: 0.3670 - val_loss: 1.7191 - val_accuracy: 0.3620
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7145 - accuracy: 0.3669 - val_loss: 1.7190 - val_accuracy: 0.3620
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 461/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7144 - accuracy: 0.3668 - val_loss: 1.7190 - val_accuracy: 0.3621
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7144 - accuracy: 0.3671 - val_loss: 1.7190 - val_accuracy: 0.3621
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7143 - accuracy: 0.3672 - val_loss: 1.7189 - val_accuracy: 0.3621
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7142 - accuracy: 0.3674 - val_loss: 1.7189 - val_accuracy: 0.3621
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7141 - accuracy: 0.3674 - val_loss: 1.7189 - val_accuracy: 0.3622
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7141 - accuracy: 0.3674 - val_loss: 1.7188 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7140 - accuracy: 0.3675 - val_loss: 1.7188 - val_accuracy: 0.3623
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7140 - accuracy: 0.3677 - val_loss: 1.7187 - val_accuracy: 0.3623
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7139 - accuracy: 0.3676 - val_loss: 1.7187 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7139 - accuracy: 0.3674 - val_loss: 1.7187 - val_accuracy: 0.3623
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7138 - accuracy: 0.3675 - val_loss: 1.7186 - val_accuracy: 0.3622
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7138 - accuracy: 0.3674 - val_loss: 1.7186 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7137 - accuracy: 0.3675 - val_loss: 1.7186 - val_accuracy: 0.3625
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7137 - accuracy: 0.3675 - val_loss: 1.7185 - val_accuracy: 0.3625
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7136 - accuracy: 0.3675 - val_loss: 1.7185 - val_accuracy: 0.3622
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7136 - accuracy: 0.3676 - val_loss: 1.7184 - val_accuracy: 0.3623
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7135 - accuracy: 0.3676 - val_loss: 1.7184 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 478/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7135 - accuracy: 0.3676 - val_loss: 1.7184 - val_accuracy: 0.3623
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7135 - accuracy: 0.3676 - val_loss: 1.7183 - val_accuracy: 0.3623
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7134 - accuracy: 0.3677 - val_loss: 1.7183 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7134 - accuracy: 0.3676 - val_loss: 1.7183 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 482/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7133 - accuracy: 0.3676 - val_loss: 1.7182 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7133 - accuracy: 0.3676 - val_loss: 1.7182 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7133 - accuracy: 0.3677 - val_loss: 1.7182 - val_accuracy: 0.3625
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7132 - accuracy: 0.3678 - val_loss: 1.7181 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7132 - accuracy: 0.3676 - val_loss: 1.7181 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.3678 - val_loss: 1.7181 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.3679 - val_loss: 1.7180 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.3679 - val_loss: 1.7180 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7130 - accuracy: 0.3677 - val_loss: 1.7179 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7130 - accuracy: 0.3678 - val_loss: 1.7179 - val_accuracy: 0.3624
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7129 - accuracy: 0.3679 - val_loss: 1.7179 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7129 - accuracy: 0.3677 - val_loss: 1.7179 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7129 - accuracy: 0.3679 - val_loss: 1.7179 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7128 - accuracy: 0.3677 - val_loss: 1.7179 - val_accuracy: 0.3627
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 496/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7128 - accuracy: 0.3677 - val_loss: 1.7178 - val_accuracy: 0.3627
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7128 - accuracy: 0.3680 - val_loss: 1.7177 - val_accuracy: 0.3627
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7127 - accuracy: 0.3679 - val_loss: 1.7177 - val_accuracy: 0.3627
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7127 - accuracy: 0.3679 - val_loss: 1.7177 - val_accuracy: 0.3626
[-0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7127 - accuracy: 0.3679 - val_loss: 1.7177 - val_accuracy: 0.3625
[-0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 1/200
235/235 [==============================] - 4s 14ms/step - loss: 2.1737 - accuracy: 0.9248 - val_loss: 1.5242 - val_accuracy: 0.8890
Epoch 2/200
235/235 [==============================] - 3s 13ms/step - loss: 0.4353 - accuracy: 0.9594 - val_loss: 0.4910 - val_accuracy: 0.9330
Epoch 3/200
235/235 [==============================] - 3s 13ms/step - loss: 0.3160 - accuracy: 0.9626 - val_loss: 0.3328 - val_accuracy: 0.9539
Epoch 4/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2849 - accuracy: 0.9646 - val_loss: 0.3188 - val_accuracy: 0.9489
Epoch 5/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2622 - accuracy: 0.9672 - val_loss: 0.3016 - val_accuracy: 0.9517
Epoch 6/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2511 - accuracy: 0.9682 - val_loss: 0.2965 - val_accuracy: 0.9513
Epoch 7/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2421 - accuracy: 0.9696 - val_loss: 0.3048 - val_accuracy: 0.9483
Epoch 8/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2340 - accuracy: 0.9703 - val_loss: 0.2974 - val_accuracy: 0.9440
Epoch 9/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2258 - accuracy: 0.9701 - val_loss: 0.2905 - val_accuracy: 0.9431
Epoch 10/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2198 - accuracy: 0.9703 - val_loss: 0.2530 - val_accuracy: 0.9561
Epoch 11/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2111 - accuracy: 0.9718 - val_loss: 0.2501 - val_accuracy: 0.9565
Epoch 12/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2095 - accuracy: 0.9714 - val_loss: 0.2555 - val_accuracy: 0.9560
Epoch 13/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2060 - accuracy: 0.9722 - val_loss: 0.2789 - val_accuracy: 0.9477
Epoch 14/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2020 - accuracy: 0.9721 - val_loss: 0.2356 - val_accuracy: 0.9598
Epoch 15/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1966 - accuracy: 0.9720 - val_loss: 0.2957 - val_accuracy: 0.9384
Epoch 16/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1935 - accuracy: 0.9732 - val_loss: 0.2861 - val_accuracy: 0.9417
Epoch 17/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1905 - accuracy: 0.9729 - val_loss: 0.2594 - val_accuracy: 0.9499
Epoch 18/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1917 - accuracy: 0.9723 - val_loss: 0.2609 - val_accuracy: 0.9489
Epoch 19/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1878 - accuracy: 0.9728 - val_loss: 0.2464 - val_accuracy: 0.9543
Epoch 20/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1825 - accuracy: 0.9737 - val_loss: 0.2536 - val_accuracy: 0.9500
Epoch 21/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1830 - accuracy: 0.9735 - val_loss: 0.2378 - val_accuracy: 0.9559
Epoch 22/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1783 - accuracy: 0.9748 - val_loss: 0.2248 - val_accuracy: 0.9606
Epoch 23/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1759 - accuracy: 0.9748 - val_loss: 0.2905 - val_accuracy: 0.9398
Epoch 24/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1769 - accuracy: 0.9738 - val_loss: 0.2315 - val_accuracy: 0.9555
Epoch 25/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1742 - accuracy: 0.9750 - val_loss: 0.2395 - val_accuracy: 0.9537
Epoch 26/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1759 - accuracy: 0.9744 - val_loss: 0.2273 - val_accuracy: 0.9585
Epoch 27/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1745 - accuracy: 0.9746 - val_loss: 0.2475 - val_accuracy: 0.9524
Epoch 28/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1732 - accuracy: 0.9755 - val_loss: 0.2300 - val_accuracy: 0.9561
Epoch 29/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1706 - accuracy: 0.9751 - val_loss: 0.2366 - val_accuracy: 0.9541
Epoch 30/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1686 - accuracy: 0.9754 - val_loss: 0.2577 - val_accuracy: 0.9481
Epoch 31/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1694 - accuracy: 0.9751 - val_loss: 0.2064 - val_accuracy: 0.9657
Epoch 32/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1667 - accuracy: 0.9761 - val_loss: 0.2472 - val_accuracy: 0.9514
Epoch 33/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1679 - accuracy: 0.9752 - val_loss: 0.2317 - val_accuracy: 0.9547
Epoch 34/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1684 - accuracy: 0.9746 - val_loss: 0.2305 - val_accuracy: 0.9549
Epoch 35/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1649 - accuracy: 0.9758 - val_loss: 0.2384 - val_accuracy: 0.9530
Epoch 36/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1639 - accuracy: 0.9754 - val_loss: 0.2561 - val_accuracy: 0.9492
Epoch 37/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1651 - accuracy: 0.9752 - val_loss: 0.2818 - val_accuracy: 0.9369
Epoch 38/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1634 - accuracy: 0.9763 - val_loss: 0.2169 - val_accuracy: 0.9604
Epoch 39/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1627 - accuracy: 0.9767 - val_loss: 0.3096 - val_accuracy: 0.9311
Epoch 40/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1627 - accuracy: 0.9767 - val_loss: 0.2196 - val_accuracy: 0.9583
Epoch 41/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1632 - accuracy: 0.9757 - val_loss: 0.2205 - val_accuracy: 0.9604
Epoch 42/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1610 - accuracy: 0.9758 - val_loss: 0.2648 - val_accuracy: 0.9433
Epoch 43/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1598 - accuracy: 0.9768 - val_loss: 0.2101 - val_accuracy: 0.9610
Epoch 44/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1600 - accuracy: 0.9767 - val_loss: 0.2638 - val_accuracy: 0.9459
Epoch 45/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1580 - accuracy: 0.9772 - val_loss: 0.2580 - val_accuracy: 0.9461
Epoch 46/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1573 - accuracy: 0.9773 - val_loss: 0.2209 - val_accuracy: 0.9580
Epoch 47/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1564 - accuracy: 0.9778 - val_loss: 0.2135 - val_accuracy: 0.9622
Epoch 48/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1580 - accuracy: 0.9758 - val_loss: 0.2297 - val_accuracy: 0.9577
Epoch 49/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1580 - accuracy: 0.9763 - val_loss: 0.2120 - val_accuracy: 0.9602
Epoch 50/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1610 - accuracy: 0.9761 - val_loss: 0.2164 - val_accuracy: 0.9597
Epoch 51/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1572 - accuracy: 0.9766 - val_loss: 0.2352 - val_accuracy: 0.9559
Epoch 52/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1596 - accuracy: 0.9759 - val_loss: 0.2283 - val_accuracy: 0.9569
Epoch 53/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1573 - accuracy: 0.9767 - val_loss: 0.2164 - val_accuracy: 0.9616
Epoch 54/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1589 - accuracy: 0.9762 - val_loss: 0.2016 - val_accuracy: 0.9631
Epoch 55/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1577 - accuracy: 0.9757 - val_loss: 0.2363 - val_accuracy: 0.9567
Epoch 56/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1528 - accuracy: 0.9786 - val_loss: 0.2122 - val_accuracy: 0.9610
Epoch 57/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1551 - accuracy: 0.9771 - val_loss: 0.2280 - val_accuracy: 0.9569
Epoch 58/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1582 - accuracy: 0.9765 - val_loss: 0.2427 - val_accuracy: 0.9498
Epoch 59/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1543 - accuracy: 0.9770 - val_loss: 0.2089 - val_accuracy: 0.9615
Epoch 60/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1523 - accuracy: 0.9779 - val_loss: 0.2028 - val_accuracy: 0.9648
Epoch 61/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1546 - accuracy: 0.9771 - val_loss: 0.2453 - val_accuracy: 0.9492
Epoch 62/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1559 - accuracy: 0.9768 - val_loss: 0.2028 - val_accuracy: 0.9646
Epoch 63/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1570 - accuracy: 0.9759 - val_loss: 0.2352 - val_accuracy: 0.9570
Epoch 64/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1554 - accuracy: 0.9773 - val_loss: 0.2496 - val_accuracy: 0.9523
Epoch 65/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1549 - accuracy: 0.9773 - val_loss: 0.2600 - val_accuracy: 0.9469
Epoch 66/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1550 - accuracy: 0.9768 - val_loss: 0.2354 - val_accuracy: 0.9553
Epoch 67/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1539 - accuracy: 0.9770 - val_loss: 0.2097 - val_accuracy: 0.9602
Epoch 68/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1553 - accuracy: 0.9770 - val_loss: 0.1960 - val_accuracy: 0.9653
Epoch 69/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1553 - accuracy: 0.9771 - val_loss: 0.2380 - val_accuracy: 0.9513
Epoch 70/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1561 - accuracy: 0.9763 - val_loss: 0.2187 - val_accuracy: 0.9585
Epoch 71/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1549 - accuracy: 0.9764 - val_loss: 0.2224 - val_accuracy: 0.9568
Epoch 72/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1508 - accuracy: 0.9774 - val_loss: 0.2300 - val_accuracy: 0.9557
Epoch 73/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1526 - accuracy: 0.9769 - val_loss: 0.2341 - val_accuracy: 0.9508
Epoch 74/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1510 - accuracy: 0.9772 - val_loss: 0.2401 - val_accuracy: 0.9498
Epoch 75/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1542 - accuracy: 0.9766 - val_loss: 0.2441 - val_accuracy: 0.9468
Epoch 76/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1511 - accuracy: 0.9777 - val_loss: 0.2059 - val_accuracy: 0.9619
Epoch 77/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1508 - accuracy: 0.9777 - val_loss: 0.2043 - val_accuracy: 0.9603
Epoch 78/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1504 - accuracy: 0.9780 - val_loss: 0.2156 - val_accuracy: 0.9578
Epoch 79/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1509 - accuracy: 0.9773 - val_loss: 0.2334 - val_accuracy: 0.9555
Epoch 80/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1540 - accuracy: 0.9771 - val_loss: 0.2820 - val_accuracy: 0.9391
Epoch 81/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1587 - accuracy: 0.9755 - val_loss: 0.2220 - val_accuracy: 0.9582
Epoch 82/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1483 - accuracy: 0.9779 - val_loss: 0.2257 - val_accuracy: 0.9531
Epoch 83/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1531 - accuracy: 0.9763 - val_loss: 0.2023 - val_accuracy: 0.9631
Epoch 84/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1506 - accuracy: 0.9774 - val_loss: 0.2146 - val_accuracy: 0.9604
Epoch 85/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1471 - accuracy: 0.9781 - val_loss: 0.2139 - val_accuracy: 0.9586
Epoch 86/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1489 - accuracy: 0.9778 - val_loss: 0.2020 - val_accuracy: 0.9630
Epoch 87/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1481 - accuracy: 0.9786 - val_loss: 0.2045 - val_accuracy: 0.9606
Epoch 88/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1476 - accuracy: 0.9776 - val_loss: 0.2242 - val_accuracy: 0.9565
Epoch 89/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1495 - accuracy: 0.9774 - val_loss: 0.2372 - val_accuracy: 0.9501
Epoch 90/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1467 - accuracy: 0.9778 - val_loss: 0.2216 - val_accuracy: 0.9582
Epoch 91/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1481 - accuracy: 0.9779 - val_loss: 0.2084 - val_accuracy: 0.9604
Epoch 92/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1490 - accuracy: 0.9784 - val_loss: 0.2122 - val_accuracy: 0.9588
Epoch 93/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1482 - accuracy: 0.9780 - val_loss: 0.2125 - val_accuracy: 0.9582
Epoch 94/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1501 - accuracy: 0.9768 - val_loss: 0.2381 - val_accuracy: 0.9502
Epoch 95/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1480 - accuracy: 0.9785 - val_loss: 0.1908 - val_accuracy: 0.9647
Epoch 96/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1463 - accuracy: 0.9784 - val_loss: 0.2145 - val_accuracy: 0.9582
Epoch 97/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1455 - accuracy: 0.9779 - val_loss: 0.1963 - val_accuracy: 0.9639
Epoch 98/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1490 - accuracy: 0.9770 - val_loss: 0.2141 - val_accuracy: 0.9583
Epoch 99/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1452 - accuracy: 0.9787 - val_loss: 0.2060 - val_accuracy: 0.9602
Epoch 100/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1474 - accuracy: 0.9773 - val_loss: 0.2376 - val_accuracy: 0.9529
Epoch 101/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1501 - accuracy: 0.9772 - val_loss: 0.2658 - val_accuracy: 0.9426
Epoch 102/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1482 - accuracy: 0.9775 - val_loss: 0.2295 - val_accuracy: 0.9521
Epoch 103/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1481 - accuracy: 0.9778 - val_loss: 0.2269 - val_accuracy: 0.9545
Epoch 104/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1488 - accuracy: 0.9777 - val_loss: 0.2024 - val_accuracy: 0.9615
Epoch 105/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1456 - accuracy: 0.9784 - val_loss: 0.2086 - val_accuracy: 0.9586
Epoch 106/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1488 - accuracy: 0.9768 - val_loss: 0.2156 - val_accuracy: 0.9593
Epoch 107/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1431 - accuracy: 0.9786 - val_loss: 0.2278 - val_accuracy: 0.9530
Epoch 108/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1486 - accuracy: 0.9768 - val_loss: 0.1887 - val_accuracy: 0.9666
Epoch 109/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1454 - accuracy: 0.9783 - val_loss: 0.2081 - val_accuracy: 0.9584
Epoch 110/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1428 - accuracy: 0.9783 - val_loss: 0.2436 - val_accuracy: 0.9491
Epoch 111/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1513 - accuracy: 0.9768 - val_loss: 0.2471 - val_accuracy: 0.9496
Epoch 112/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1474 - accuracy: 0.9780 - val_loss: 0.2137 - val_accuracy: 0.9594
Epoch 113/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1475 - accuracy: 0.9775 - val_loss: 0.2124 - val_accuracy: 0.9576
Epoch 114/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1490 - accuracy: 0.9774 - val_loss: 0.2183 - val_accuracy: 0.9569
Epoch 115/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1444 - accuracy: 0.9786 - val_loss: 0.2108 - val_accuracy: 0.9601
Epoch 116/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1455 - accuracy: 0.9776 - val_loss: 0.2479 - val_accuracy: 0.9450
Epoch 117/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9782 - val_loss: 0.1917 - val_accuracy: 0.9668
Epoch 118/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1471 - accuracy: 0.9777 - val_loss: 0.2174 - val_accuracy: 0.9569
Epoch 119/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1417 - accuracy: 0.9790 - val_loss: 0.2200 - val_accuracy: 0.9579
Epoch 120/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1440 - accuracy: 0.9778 - val_loss: 0.2018 - val_accuracy: 0.9613
Epoch 121/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1453 - accuracy: 0.9779 - val_loss: 0.2096 - val_accuracy: 0.9579
Epoch 122/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1406 - accuracy: 0.9794 - val_loss: 0.1959 - val_accuracy: 0.9635
Epoch 123/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1468 - accuracy: 0.9773 - val_loss: 0.2047 - val_accuracy: 0.9617
Epoch 124/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1418 - accuracy: 0.9785 - val_loss: 0.2532 - val_accuracy: 0.9470
Epoch 125/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1485 - accuracy: 0.9770 - val_loss: 0.2010 - val_accuracy: 0.9638
Epoch 126/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1451 - accuracy: 0.9783 - val_loss: 0.2201 - val_accuracy: 0.9553
Epoch 127/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1466 - accuracy: 0.9777 - val_loss: 0.2111 - val_accuracy: 0.9593
Epoch 128/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1406 - accuracy: 0.9790 - val_loss: 0.2143 - val_accuracy: 0.9575
Epoch 129/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1485 - accuracy: 0.9768 - val_loss: 0.1982 - val_accuracy: 0.9623
Epoch 130/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1429 - accuracy: 0.9785 - val_loss: 0.2142 - val_accuracy: 0.9578
Epoch 131/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1422 - accuracy: 0.9785 - val_loss: 0.2115 - val_accuracy: 0.9582
Epoch 132/200
235/235 [==============================] - 3s 11ms/step - loss: 0.1437 - accuracy: 0.9790 - val_loss: 0.2235 - val_accuracy: 0.9564
Epoch 133/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1453 - accuracy: 0.9781 - val_loss: 0.2057 - val_accuracy: 0.9623
Epoch 134/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1428 - accuracy: 0.9789 - val_loss: 0.2346 - val_accuracy: 0.9517
Epoch 135/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1431 - accuracy: 0.9783 - val_loss: 0.2210 - val_accuracy: 0.9581
Epoch 136/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1431 - accuracy: 0.9789 - val_loss: 0.2003 - val_accuracy: 0.9630
Epoch 137/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1452 - accuracy: 0.9776 - val_loss: 0.2035 - val_accuracy: 0.9616
Epoch 138/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1445 - accuracy: 0.9780 - val_loss: 0.2006 - val_accuracy: 0.9632
Epoch 139/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1435 - accuracy: 0.9781 - val_loss: 0.1917 - val_accuracy: 0.9631
Epoch 140/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1463 - accuracy: 0.9779 - val_loss: 0.1955 - val_accuracy: 0.9633
Epoch 141/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9790 - val_loss: 0.2072 - val_accuracy: 0.9593
Epoch 142/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9789 - val_loss: 0.2386 - val_accuracy: 0.9517
Epoch 143/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1464 - accuracy: 0.9774 - val_loss: 0.2407 - val_accuracy: 0.9499
Epoch 144/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1442 - accuracy: 0.9783 - val_loss: 0.2230 - val_accuracy: 0.9547
Epoch 145/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1445 - accuracy: 0.9784 - val_loss: 0.2205 - val_accuracy: 0.9554
Epoch 146/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1432 - accuracy: 0.9784 - val_loss: 0.2292 - val_accuracy: 0.9542
Epoch 147/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1450 - accuracy: 0.9785 - val_loss: 0.2308 - val_accuracy: 0.9554
Epoch 148/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1434 - accuracy: 0.9787 - val_loss: 0.2135 - val_accuracy: 0.9568
Epoch 149/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1381 - accuracy: 0.9796 - val_loss: 0.1945 - val_accuracy: 0.9637
Epoch 150/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1441 - accuracy: 0.9773 - val_loss: 0.2043 - val_accuracy: 0.9607
Epoch 151/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1433 - accuracy: 0.9783 - val_loss: 0.1970 - val_accuracy: 0.9644
Epoch 152/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9779 - val_loss: 0.2014 - val_accuracy: 0.9613
Epoch 153/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1429 - accuracy: 0.9781 - val_loss: 0.2060 - val_accuracy: 0.9595
Epoch 154/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1414 - accuracy: 0.9787 - val_loss: 0.1985 - val_accuracy: 0.9623
Epoch 155/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1403 - accuracy: 0.9790 - val_loss: 0.2149 - val_accuracy: 0.9589
Epoch 156/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1431 - accuracy: 0.9788 - val_loss: 0.1991 - val_accuracy: 0.9620
Epoch 157/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9793 - val_loss: 0.2390 - val_accuracy: 0.9485
Epoch 158/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1453 - accuracy: 0.9778 - val_loss: 0.2029 - val_accuracy: 0.9604
Epoch 159/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1423 - accuracy: 0.9786 - val_loss: 0.2228 - val_accuracy: 0.9560
Epoch 160/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1454 - accuracy: 0.9777 - val_loss: 0.2035 - val_accuracy: 0.9625
Epoch 161/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1415 - accuracy: 0.9789 - val_loss: 0.2168 - val_accuracy: 0.9559
Epoch 162/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1462 - accuracy: 0.9768 - val_loss: 0.2062 - val_accuracy: 0.9579
Epoch 163/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1401 - accuracy: 0.9790 - val_loss: 0.2008 - val_accuracy: 0.9592
Epoch 164/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1417 - accuracy: 0.9785 - val_loss: 0.1998 - val_accuracy: 0.9613
Epoch 165/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1448 - accuracy: 0.9781 - val_loss: 0.1890 - val_accuracy: 0.9643
Epoch 166/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1388 - accuracy: 0.9802 - val_loss: 0.2218 - val_accuracy: 0.9574
Epoch 167/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1457 - accuracy: 0.9778 - val_loss: 0.2218 - val_accuracy: 0.9560
Epoch 168/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1430 - accuracy: 0.9779 - val_loss: 0.2269 - val_accuracy: 0.9534
Epoch 169/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1432 - accuracy: 0.9790 - val_loss: 0.2055 - val_accuracy: 0.9589
Epoch 170/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1386 - accuracy: 0.9797 - val_loss: 0.2038 - val_accuracy: 0.9622
Epoch 171/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1419 - accuracy: 0.9783 - val_loss: 0.1909 - val_accuracy: 0.9623
Epoch 172/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1422 - accuracy: 0.9782 - val_loss: 0.2146 - val_accuracy: 0.9569
Epoch 173/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9787 - val_loss: 0.2059 - val_accuracy: 0.9600
Epoch 174/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1430 - accuracy: 0.9780 - val_loss: 0.2064 - val_accuracy: 0.9592
Epoch 175/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1408 - accuracy: 0.9787 - val_loss: 0.1816 - val_accuracy: 0.9646
Epoch 176/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9775 - val_loss: 0.2180 - val_accuracy: 0.9556
Epoch 177/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1458 - accuracy: 0.9775 - val_loss: 0.1949 - val_accuracy: 0.9650
Epoch 178/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1391 - accuracy: 0.9795 - val_loss: 0.1996 - val_accuracy: 0.9606
Epoch 179/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1433 - accuracy: 0.9776 - val_loss: 0.2121 - val_accuracy: 0.9608
Epoch 180/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1417 - accuracy: 0.9789 - val_loss: 0.1968 - val_accuracy: 0.9618
Epoch 181/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1403 - accuracy: 0.9790 - val_loss: 0.2084 - val_accuracy: 0.9587
Epoch 182/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1407 - accuracy: 0.9788 - val_loss: 0.2106 - val_accuracy: 0.9582
Epoch 183/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1412 - accuracy: 0.9783 - val_loss: 0.2039 - val_accuracy: 0.9619
Epoch 184/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9781 - val_loss: 0.2209 - val_accuracy: 0.9555
Epoch 185/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1411 - accuracy: 0.9780 - val_loss: 0.2305 - val_accuracy: 0.9528
Epoch 186/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1415 - accuracy: 0.9789 - val_loss: 0.2030 - val_accuracy: 0.9590
Epoch 187/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1399 - accuracy: 0.9785 - val_loss: 0.2407 - val_accuracy: 0.9505
Epoch 188/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1427 - accuracy: 0.9780 - val_loss: 0.1906 - val_accuracy: 0.9644
Epoch 189/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9794 - val_loss: 0.2146 - val_accuracy: 0.9581
Epoch 190/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1467 - accuracy: 0.9765 - val_loss: 0.2115 - val_accuracy: 0.9594
Epoch 191/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9782 - val_loss: 0.2116 - val_accuracy: 0.9571
Epoch 192/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1421 - accuracy: 0.9788 - val_loss: 0.2322 - val_accuracy: 0.9532
Epoch 193/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1429 - accuracy: 0.9780 - val_loss: 0.1936 - val_accuracy: 0.9630
Epoch 194/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9792 - val_loss: 0.1965 - val_accuracy: 0.9628
Epoch 195/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1412 - accuracy: 0.9781 - val_loss: 0.2546 - val_accuracy: 0.9474
Epoch 196/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1468 - accuracy: 0.9773 - val_loss: 0.1937 - val_accuracy: 0.9664
Epoch 197/200
235/235 [==============================] - 4s 15ms/step - loss: 0.1404 - accuracy: 0.9793 - val_loss: 0.2041 - val_accuracy: 0.9593
Epoch 198/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9787 - val_loss: 0.1964 - val_accuracy: 0.9626
Epoch 199/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9779 - val_loss: 0.1977 - val_accuracy: 0.9596
Epoch 200/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1386 - accuracy: 0.9795 - val_loss: 0.1818 - val_accuracy: 0.9655
Epoch 1/200
235/235 [==============================] - 4s 14ms/step - loss: 0.2500 - accuracy: 0.9257 - val_loss: 0.2034 - val_accuracy: 0.9598
Epoch 2/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0872 - accuracy: 0.9747 - val_loss: 0.0950 - val_accuracy: 0.9702
Epoch 3/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0502 - accuracy: 0.9861 - val_loss: 0.0908 - val_accuracy: 0.9724
Epoch 4/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0304 - accuracy: 0.9924 - val_loss: 0.0774 - val_accuracy: 0.9761
Epoch 5/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0192 - accuracy: 0.9956 - val_loss: 0.0825 - val_accuracy: 0.9762
Epoch 6/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0140 - accuracy: 0.9968 - val_loss: 0.0743 - val_accuracy: 0.9787
Epoch 7/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0123 - accuracy: 0.9967 - val_loss: 0.0877 - val_accuracy: 0.9747
Epoch 8/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.0790 - val_accuracy: 0.9794
Epoch 9/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0102 - accuracy: 0.9970 - val_loss: 0.0986 - val_accuracy: 0.9742
Epoch 10/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0126 - accuracy: 0.9962 - val_loss: 0.0937 - val_accuracy: 0.9725
Epoch 11/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0137 - accuracy: 0.9954 - val_loss: 0.1006 - val_accuracy: 0.9715
Epoch 12/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0085 - accuracy: 0.9976 - val_loss: 0.0962 - val_accuracy: 0.9758
Epoch 13/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0081 - accuracy: 0.9973 - val_loss: 0.0849 - val_accuracy: 0.9790
Epoch 14/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0092 - accuracy: 0.9971 - val_loss: 0.0831 - val_accuracy: 0.9786
Epoch 15/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0060 - accuracy: 0.9984 - val_loss: 0.0656 - val_accuracy: 0.9825
Epoch 16/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9994 - val_loss: 0.0674 - val_accuracy: 0.9842
Epoch 17/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.0708 - val_accuracy: 0.9814
Epoch 18/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0704 - val_accuracy: 0.9837
Epoch 19/200
235/235 [==============================] - 3s 13ms/step - loss: 5.2802e-04 - accuracy: 1.0000 - val_loss: 0.0660 - val_accuracy: 0.9837
Epoch 20/200
235/235 [==============================] - 3s 13ms/step - loss: 2.4735e-04 - accuracy: 1.0000 - val_loss: 0.0636 - val_accuracy: 0.9842
Epoch 21/200
235/235 [==============================] - 3s 13ms/step - loss: 1.5895e-04 - accuracy: 1.0000 - val_loss: 0.0644 - val_accuracy: 0.9846
Epoch 22/200
235/235 [==============================] - 3s 13ms/step - loss: 3.5584e-04 - accuracy: 0.9999 - val_loss: 0.0761 - val_accuracy: 0.9836
Epoch 23/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0256 - accuracy: 0.9917 - val_loss: 0.1689 - val_accuracy: 0.9609
Epoch 24/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0247 - accuracy: 0.9919 - val_loss: 0.0782 - val_accuracy: 0.9795
Epoch 25/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0069 - accuracy: 0.9980 - val_loss: 0.0719 - val_accuracy: 0.9824
Epoch 26/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0034 - accuracy: 0.9992 - val_loss: 0.0651 - val_accuracy: 0.9841
Epoch 27/200
235/235 [==============================] - 3s 11ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.0630 - val_accuracy: 0.9846
Epoch 28/200
235/235 [==============================] - 3s 13ms/step - loss: 5.2952e-04 - accuracy: 1.0000 - val_loss: 0.0692 - val_accuracy: 0.9839
Epoch 29/200
235/235 [==============================] - 3s 13ms/step - loss: 3.4885e-04 - accuracy: 1.0000 - val_loss: 0.0620 - val_accuracy: 0.9847
Epoch 30/200
235/235 [==============================] - 3s 13ms/step - loss: 2.5717e-04 - accuracy: 1.0000 - val_loss: 0.0619 - val_accuracy: 0.9854
Epoch 31/200
235/235 [==============================] - 3s 13ms/step - loss: 2.5684e-04 - accuracy: 1.0000 - val_loss: 0.0631 - val_accuracy: 0.9860
Epoch 32/200
235/235 [==============================] - 3s 13ms/step - loss: 1.5152e-04 - accuracy: 1.0000 - val_loss: 0.0637 - val_accuracy: 0.9852
Epoch 33/200
235/235 [==============================] - 3s 13ms/step - loss: 1.2892e-04 - accuracy: 1.0000 - val_loss: 0.0637 - val_accuracy: 0.9854
Epoch 34/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0813 - val_accuracy: 0.9809
Epoch 35/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0223 - accuracy: 0.9926 - val_loss: 0.1289 - val_accuracy: 0.9717
Epoch 36/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0130 - accuracy: 0.9956 - val_loss: 0.0763 - val_accuracy: 0.9816
Epoch 37/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.0734 - val_accuracy: 0.9810
Epoch 38/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.0644 - val_accuracy: 0.9851
Epoch 39/200
235/235 [==============================] - 3s 13ms/step - loss: 6.8622e-04 - accuracy: 0.9999 - val_loss: 0.0637 - val_accuracy: 0.9852
Epoch 40/200
235/235 [==============================] - 3s 13ms/step - loss: 4.1688e-04 - accuracy: 1.0000 - val_loss: 0.0629 - val_accuracy: 0.9859
Epoch 41/200
235/235 [==============================] - 3s 13ms/step - loss: 2.2065e-04 - accuracy: 1.0000 - val_loss: 0.0624 - val_accuracy: 0.9862
Epoch 42/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4948e-04 - accuracy: 1.0000 - val_loss: 0.0632 - val_accuracy: 0.9862
Epoch 43/200
235/235 [==============================] - 3s 13ms/step - loss: 1.3227e-04 - accuracy: 1.0000 - val_loss: 0.0634 - val_accuracy: 0.9863
Epoch 44/200
235/235 [==============================] - 3s 13ms/step - loss: 9.6100e-05 - accuracy: 1.0000 - val_loss: 0.0637 - val_accuracy: 0.9864
Epoch 45/200
235/235 [==============================] - 3s 13ms/step - loss: 9.5771e-05 - accuracy: 1.0000 - val_loss: 0.0642 - val_accuracy: 0.9863
Epoch 46/200
235/235 [==============================] - 3s 13ms/step - loss: 1.3316e-04 - accuracy: 1.0000 - val_loss: 0.0645 - val_accuracy: 0.9861
Epoch 47/200
235/235 [==============================] - 3s 13ms/step - loss: 8.4740e-05 - accuracy: 1.0000 - val_loss: 0.0654 - val_accuracy: 0.9862
Epoch 48/200
235/235 [==============================] - 3s 13ms/step - loss: 8.8681e-05 - accuracy: 1.0000 - val_loss: 0.0643 - val_accuracy: 0.9864
Epoch 49/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1404 - val_accuracy: 0.9761
Epoch 50/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0286 - accuracy: 0.9909 - val_loss: 0.1092 - val_accuracy: 0.9751
Epoch 51/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0089 - accuracy: 0.9971 - val_loss: 0.0812 - val_accuracy: 0.9816
Epoch 52/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0657 - val_accuracy: 0.9846
Epoch 53/200
235/235 [==============================] - 3s 13ms/step - loss: 9.7368e-04 - accuracy: 0.9999 - val_loss: 0.0676 - val_accuracy: 0.9844
Epoch 54/200
235/235 [==============================] - 3s 13ms/step - loss: 7.5978e-04 - accuracy: 0.9999 - val_loss: 0.0679 - val_accuracy: 0.9846
Epoch 55/200
235/235 [==============================] - 3s 13ms/step - loss: 5.7695e-04 - accuracy: 0.9999 - val_loss: 0.0668 - val_accuracy: 0.9852
Epoch 56/200
235/235 [==============================] - 3s 13ms/step - loss: 3.0574e-04 - accuracy: 1.0000 - val_loss: 0.0664 - val_accuracy: 0.9850
Epoch 57/200
235/235 [==============================] - 3s 13ms/step - loss: 2.2152e-04 - accuracy: 1.0000 - val_loss: 0.0666 - val_accuracy: 0.9850
Epoch 58/200
235/235 [==============================] - 3s 13ms/step - loss: 1.3104e-04 - accuracy: 1.0000 - val_loss: 0.0693 - val_accuracy: 0.9855
Epoch 59/200
235/235 [==============================] - 3s 13ms/step - loss: 1.2827e-04 - accuracy: 1.0000 - val_loss: 0.0665 - val_accuracy: 0.9857
Epoch 60/200
235/235 [==============================] - 3s 13ms/step - loss: 2.4903e-04 - accuracy: 0.9999 - val_loss: 0.0782 - val_accuracy: 0.9834
Epoch 61/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.1336 - val_accuracy: 0.9706
Epoch 62/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0155 - accuracy: 0.9950 - val_loss: 0.0894 - val_accuracy: 0.9817
Epoch 63/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 0.0845 - val_accuracy: 0.9819
Epoch 64/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0768 - val_accuracy: 0.9842
Epoch 65/200
235/235 [==============================] - 3s 13ms/step - loss: 4.7961e-04 - accuracy: 0.9999 - val_loss: 0.0749 - val_accuracy: 0.9846
Epoch 66/200
235/235 [==============================] - 3s 13ms/step - loss: 2.1002e-04 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9854
Epoch 67/200
235/235 [==============================] - 3s 13ms/step - loss: 1.3170e-04 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9847
Epoch 68/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0363e-04 - accuracy: 1.0000 - val_loss: 0.0727 - val_accuracy: 0.9854
Epoch 69/200
235/235 [==============================] - 3s 13ms/step - loss: 8.2688e-05 - accuracy: 1.0000 - val_loss: 0.0730 - val_accuracy: 0.9855
Epoch 70/200
235/235 [==============================] - 3s 13ms/step - loss: 7.3269e-05 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9847
Epoch 71/200
235/235 [==============================] - 3s 13ms/step - loss: 6.0541e-05 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9850
Epoch 72/200
235/235 [==============================] - 3s 13ms/step - loss: 5.9681e-05 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9855
Epoch 73/200
235/235 [==============================] - 3s 13ms/step - loss: 5.8945e-05 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9856
Epoch 74/200
235/235 [==============================] - 3s 13ms/step - loss: 4.1104e-05 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9854
Epoch 75/200
235/235 [==============================] - 3s 13ms/step - loss: 3.7607e-05 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9850
Epoch 76/200
235/235 [==============================] - 3s 13ms/step - loss: 5.8262e-05 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9844
Epoch 77/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9995 - val_loss: 0.2597 - val_accuracy: 0.9580
Epoch 78/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0228 - accuracy: 0.9926 - val_loss: 0.1128 - val_accuracy: 0.9766
Epoch 79/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.0804 - val_accuracy: 0.9824
Epoch 80/200
235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0761 - val_accuracy: 0.9830
Epoch 81/200
235/235 [==============================] - 3s 13ms/step - loss: 7.6214e-04 - accuracy: 0.9999 - val_loss: 0.0722 - val_accuracy: 0.9841
Epoch 82/200
235/235 [==============================] - 3s 13ms/step - loss: 3.1554e-04 - accuracy: 0.9999 - val_loss: 0.0712 - val_accuracy: 0.9848
Epoch 83/200
235/235 [==============================] - 3s 13ms/step - loss: 1.7432e-04 - accuracy: 1.0000 - val_loss: 0.0726 - val_accuracy: 0.9852
Epoch 84/200
235/235 [==============================] - 3s 13ms/step - loss: 2.9353e-04 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9851
Epoch 85/200
235/235 [==============================] - 3s 13ms/step - loss: 1.2013e-04 - accuracy: 1.0000 - val_loss: 0.0726 - val_accuracy: 0.9857
Epoch 86/200
235/235 [==============================] - 3s 13ms/step - loss: 8.8858e-05 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9849
Epoch 87/200
235/235 [==============================] - 3s 13ms/step - loss: 6.8913e-05 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9851
Epoch 88/200
235/235 [==============================] - 3s 13ms/step - loss: 5.5074e-05 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9852
Epoch 89/200
235/235 [==============================] - 3s 13ms/step - loss: 4.9412e-05 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9851
Epoch 90/200
235/235 [==============================] - 3s 13ms/step - loss: 6.4044e-04 - accuracy: 0.9998 - val_loss: 0.0771 - val_accuracy: 0.9841
Epoch 91/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0039 - accuracy: 0.9989 - val_loss: 0.1449 - val_accuracy: 0.9723
Epoch 92/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0111 - accuracy: 0.9961 - val_loss: 0.0946 - val_accuracy: 0.9806
Epoch 93/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.0862 - val_accuracy: 0.9823
Epoch 94/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.0824 - val_accuracy: 0.9828
Epoch 95/200
235/235 [==============================] - 3s 14ms/step - loss: 6.0720e-04 - accuracy: 0.9999 - val_loss: 0.0827 - val_accuracy: 0.9826
Epoch 96/200
235/235 [==============================] - 3s 14ms/step - loss: 1.7349e-04 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9837
Epoch 97/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0773e-04 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9838
Epoch 98/200
235/235 [==============================] - 3s 13ms/step - loss: 7.5179e-05 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9842
Epoch 99/200
235/235 [==============================] - 3s 13ms/step - loss: 6.9671e-05 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9841
Epoch 100/200
235/235 [==============================] - 3s 13ms/step - loss: 6.3644e-05 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9840
Epoch 101/200
235/235 [==============================] - 2s 11ms/step - loss: 4.7930e-04 - accuracy: 0.9999 - val_loss: 0.0934 - val_accuracy: 0.9810
Epoch 102/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9990 - val_loss: 0.1061 - val_accuracy: 0.9796
Epoch 103/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.1127 - val_accuracy: 0.9778
Epoch 104/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.0850 - val_accuracy: 0.9834
Epoch 105/200
235/235 [==============================] - 3s 13ms/step - loss: 8.6719e-04 - accuracy: 0.9998 - val_loss: 0.0896 - val_accuracy: 0.9832
Epoch 106/200
235/235 [==============================] - 3s 13ms/step - loss: 7.0985e-04 - accuracy: 0.9998 - val_loss: 0.0881 - val_accuracy: 0.9823
Epoch 107/200
235/235 [==============================] - 3s 13ms/step - loss: 2.9134e-04 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9834
Epoch 108/200
235/235 [==============================] - 3s 13ms/step - loss: 3.8053e-04 - accuracy: 0.9999 - val_loss: 0.0876 - val_accuracy: 0.9840
Epoch 109/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0990e-04 - accuracy: 1.0000 - val_loss: 0.0874 - val_accuracy: 0.9842
Epoch 110/200
235/235 [==============================] - 3s 13ms/step - loss: 6.3434e-05 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9842
Epoch 111/200
235/235 [==============================] - 3s 13ms/step - loss: 4.4527e-05 - accuracy: 1.0000 - val_loss: 0.0862 - val_accuracy: 0.9841
Epoch 112/200
235/235 [==============================] - 3s 13ms/step - loss: 5.1674e-05 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9844
Epoch 113/200
235/235 [==============================] - 3s 13ms/step - loss: 4.1969e-05 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9848
Epoch 114/200
235/235 [==============================] - 3s 13ms/step - loss: 3.5263e-05 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9846
Epoch 115/200
235/235 [==============================] - 3s 13ms/step - loss: 2.5140e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9847
Epoch 116/200
235/235 [==============================] - 3s 13ms/step - loss: 2.0595e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9847
Epoch 117/200
235/235 [==============================] - 3s 13ms/step - loss: 1.7066e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9846
Epoch 118/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4851e-05 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9843
Epoch 119/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4968e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9851
Epoch 120/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4227e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9849
Epoch 121/200
235/235 [==============================] - 3s 13ms/step - loss: 1.2198e-05 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9846
Epoch 122/200
235/235 [==============================] - 3s 13ms/step - loss: 1.1184e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9849
Epoch 123/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0178e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9848
Epoch 124/200
235/235 [==============================] - 3s 13ms/step - loss: 8.6506e-06 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9846
Epoch 125/200
235/235 [==============================] - 3s 13ms/step - loss: 8.0232e-06 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9847
Epoch 126/200
235/235 [==============================] - 3s 13ms/step - loss: 8.0942e-06 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9846
Epoch 127/200
235/235 [==============================] - 3s 13ms/step - loss: 7.7319e-06 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9847
Epoch 128/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0390e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9846
Epoch 129/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0092 - accuracy: 0.9974 - val_loss: 0.1943 - val_accuracy: 0.9661
Epoch 130/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0127 - accuracy: 0.9959 - val_loss: 0.0960 - val_accuracy: 0.9825
Epoch 131/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.0897 - val_accuracy: 0.9839
Epoch 132/200
235/235 [==============================] - 3s 13ms/step - loss: 3.5302e-04 - accuracy: 0.9999 - val_loss: 0.0891 - val_accuracy: 0.9846
Epoch 133/200
235/235 [==============================] - 3s 13ms/step - loss: 1.6709e-04 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9842
Epoch 134/200
235/235 [==============================] - 3s 13ms/step - loss: 1.1259e-04 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9843
Epoch 135/200
235/235 [==============================] - 3s 13ms/step - loss: 8.7330e-05 - accuracy: 1.0000 - val_loss: 0.0887 - val_accuracy: 0.9842
Epoch 136/200
235/235 [==============================] - 3s 13ms/step - loss: 6.5443e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9843
Epoch 137/200
235/235 [==============================] - 3s 13ms/step - loss: 6.3298e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9840
Epoch 138/200
235/235 [==============================] - 3s 13ms/step - loss: 2.3583e-04 - accuracy: 0.9999 - val_loss: 0.0889 - val_accuracy: 0.9836
Epoch 139/200
235/235 [==============================] - 3s 13ms/step - loss: 1.7986e-04 - accuracy: 0.9999 - val_loss: 0.0912 - val_accuracy: 0.9842
Epoch 140/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1250 - val_accuracy: 0.9778
Epoch 141/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.1199 - val_accuracy: 0.9799
Epoch 142/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 0.1083 - val_accuracy: 0.9822
Epoch 143/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1050 - val_accuracy: 0.9829
Epoch 144/200
235/235 [==============================] - 3s 13ms/step - loss: 4.2916e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9836
Epoch 145/200
235/235 [==============================] - 3s 13ms/step - loss: 1.2900e-04 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9843
Epoch 146/200
235/235 [==============================] - 3s 13ms/step - loss: 3.2260e-04 - accuracy: 0.9999 - val_loss: 0.0968 - val_accuracy: 0.9840
Epoch 147/200
235/235 [==============================] - 3s 13ms/step - loss: 3.0878e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9834
Epoch 148/200
235/235 [==============================] - 3s 13ms/step - loss: 1.5986e-04 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9838
Epoch 149/200
235/235 [==============================] - 3s 14ms/step - loss: 4.6506e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9836
Epoch 150/200
235/235 [==============================] - 3s 13ms/step - loss: 9.3467e-05 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9841
Epoch 151/200
235/235 [==============================] - 3s 13ms/step - loss: 7.6336e-05 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9850
Epoch 152/200
235/235 [==============================] - 3s 14ms/step - loss: 3.4568e-05 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9847
Epoch 153/200
235/235 [==============================] - 3s 14ms/step - loss: 1.3326e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9831
Epoch 154/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1169 - val_accuracy: 0.9797
Epoch 155/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0065 - accuracy: 0.9978 - val_loss: 0.1048 - val_accuracy: 0.9814
Epoch 156/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1043 - val_accuracy: 0.9841
Epoch 157/200
235/235 [==============================] - 3s 14ms/step - loss: 9.2297e-04 - accuracy: 0.9998 - val_loss: 0.1003 - val_accuracy: 0.9846
Epoch 158/200
235/235 [==============================] - 3s 14ms/step - loss: 2.9797e-04 - accuracy: 0.9999 - val_loss: 0.0965 - val_accuracy: 0.9858
Epoch 159/200
235/235 [==============================] - 3s 15ms/step - loss: 1.9169e-04 - accuracy: 0.9999 - val_loss: 0.0968 - val_accuracy: 0.9854
Epoch 160/200
235/235 [==============================] - 3s 14ms/step - loss: 5.8025e-04 - accuracy: 0.9999 - val_loss: 0.0951 - val_accuracy: 0.9855
Epoch 161/200
235/235 [==============================] - 4s 15ms/step - loss: 2.7857e-04 - accuracy: 0.9999 - val_loss: 0.0920 - val_accuracy: 0.9856
Epoch 162/200
235/235 [==============================] - 4s 15ms/step - loss: 1.3455e-04 - accuracy: 1.0000 - val_loss: 0.0935 - val_accuracy: 0.9858
Epoch 163/200
235/235 [==============================] - 4s 15ms/step - loss: 2.7533e-04 - accuracy: 0.9999 - val_loss: 0.0920 - val_accuracy: 0.9857
Epoch 164/200
235/235 [==============================] - 4s 15ms/step - loss: 2.9938e-04 - accuracy: 0.9999 - val_loss: 0.0915 - val_accuracy: 0.9853
Epoch 165/200
235/235 [==============================] - 3s 15ms/step - loss: 9.3321e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9861
Epoch 166/200
235/235 [==============================] - 3s 15ms/step - loss: 3.1419e-04 - accuracy: 0.9999 - val_loss: 0.0967 - val_accuracy: 0.9852
Epoch 167/200
235/235 [==============================] - 3s 15ms/step - loss: 3.7885e-04 - accuracy: 0.9999 - val_loss: 0.0988 - val_accuracy: 0.9859
Epoch 168/200
235/235 [==============================] - 3s 15ms/step - loss: 1.0869e-04 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9852
Epoch 169/200
235/235 [==============================] - 3s 14ms/step - loss: 2.6530e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9858
Epoch 170/200
235/235 [==============================] - 4s 15ms/step - loss: 2.5307e-05 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9852
Epoch 171/200
235/235 [==============================] - 4s 15ms/step - loss: 2.1889e-05 - accuracy: 1.0000 - val_loss: 0.0959 - val_accuracy: 0.9863
Epoch 172/200
235/235 [==============================] - 3s 14ms/step - loss: 2.1447e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9858
Epoch 173/200
235/235 [==============================] - 3s 14ms/step - loss: 1.4869e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9853
Epoch 174/200
235/235 [==============================] - 3s 15ms/step - loss: 1.0171e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9854
Epoch 175/200
235/235 [==============================] - 3s 15ms/step - loss: 8.7902e-06 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9856
Epoch 176/200
235/235 [==============================] - 3s 14ms/step - loss: 1.3818e-05 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9856
Epoch 177/200
235/235 [==============================] - 3s 14ms/step - loss: 6.9275e-06 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 0.9860
Epoch 178/200
235/235 [==============================] - 3s 14ms/step - loss: 7.0674e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9858
Epoch 179/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9991 - val_loss: 0.1946 - val_accuracy: 0.9686
Epoch 180/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0123 - accuracy: 0.9966 - val_loss: 0.1087 - val_accuracy: 0.9823
Epoch 181/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.1076 - val_accuracy: 0.9831
Epoch 182/200
235/235 [==============================] - 3s 14ms/step - loss: 4.1651e-04 - accuracy: 0.9999 - val_loss: 0.0969 - val_accuracy: 0.9844
Epoch 183/200
235/235 [==============================] - 3s 14ms/step - loss: 1.0187e-04 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9841
Epoch 184/200
235/235 [==============================] - 3s 14ms/step - loss: 7.6745e-05 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9846
Epoch 185/200
235/235 [==============================] - 3s 14ms/step - loss: 6.3686e-05 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9845
Epoch 186/200
235/235 [==============================] - 4s 15ms/step - loss: 4.2680e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9845
Epoch 187/200
235/235 [==============================] - 3s 14ms/step - loss: 4.0016e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9848
Epoch 188/200
235/235 [==============================] - 3s 14ms/step - loss: 2.9176e-05 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9848
Epoch 189/200
235/235 [==============================] - 3s 14ms/step - loss: 2.8233e-05 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9848
Epoch 190/200
235/235 [==============================] - 3s 14ms/step - loss: 2.2996e-04 - accuracy: 0.9999 - val_loss: 0.0980 - val_accuracy: 0.9850
Epoch 191/200
235/235 [==============================] - 3s 14ms/step - loss: 1.8016e-04 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9851
Epoch 192/200
235/235 [==============================] - 3s 15ms/step - loss: 9.6464e-05 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9849
Epoch 193/200
235/235 [==============================] - 3s 14ms/step - loss: 3.1440e-04 - accuracy: 0.9999 - val_loss: 0.1198 - val_accuracy: 0.9803
Epoch 194/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.1195 - val_accuracy: 0.9799
Epoch 195/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9989 - val_loss: 0.1102 - val_accuracy: 0.9824
Epoch 196/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1122 - val_accuracy: 0.9826
Epoch 197/200
235/235 [==============================] - 3s 14ms/step - loss: 9.9016e-04 - accuracy: 0.9997 - val_loss: 0.1051 - val_accuracy: 0.9822
Epoch 198/200
235/235 [==============================] - 3s 14ms/step - loss: 3.0982e-04 - accuracy: 0.9999 - val_loss: 0.1049 - val_accuracy: 0.9828
Epoch 199/200
235/235 [==============================] - 3s 14ms/step - loss: 9.3398e-05 - accuracy: 1.0000 - val_loss: 0.1020 - val_accuracy: 0.9831
Epoch 200/200
235/235 [==============================] - 3s 14ms/step - loss: 8.2174e-05 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9837
Epoch 1/200
235/235 [==============================] - 3s 10ms/step - loss: 1.5630 - accuracy: 0.8562 - val_loss: 0.9242 - val_accuracy: 0.9017
Epoch 2/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8750 - accuracy: 0.8969 - val_loss: 0.8301 - val_accuracy: 0.8999
Epoch 3/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8358 - accuracy: 0.8970 - val_loss: 0.8150 - val_accuracy: 0.8979
Epoch 4/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.8966 - val_loss: 0.8085 - val_accuracy: 0.8968
Epoch 5/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8188 - accuracy: 0.8967 - val_loss: 0.8032 - val_accuracy: 0.8976
Epoch 6/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8154 - accuracy: 0.8964 - val_loss: 0.8003 - val_accuracy: 0.8972
Epoch 7/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8125 - accuracy: 0.8972 - val_loss: 0.7985 - val_accuracy: 0.8979
Epoch 8/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8114 - accuracy: 0.8973 - val_loss: 0.7966 - val_accuracy: 0.8983
Epoch 9/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8102 - accuracy: 0.8979 - val_loss: 0.7955 - val_accuracy: 0.8986
Epoch 10/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8093 - accuracy: 0.8978 - val_loss: 0.7934 - val_accuracy: 0.8995
Epoch 11/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8084 - accuracy: 0.8979 - val_loss: 0.7931 - val_accuracy: 0.8998
Epoch 12/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8082 - accuracy: 0.8978 - val_loss: 0.7923 - val_accuracy: 0.8993
Epoch 13/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8080 - accuracy: 0.8982 - val_loss: 0.7910 - val_accuracy: 0.9003
Epoch 14/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8072 - accuracy: 0.8981 - val_loss: 0.7921 - val_accuracy: 0.8998
Epoch 15/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8072 - accuracy: 0.8983 - val_loss: 0.7922 - val_accuracy: 0.8991
Epoch 16/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8067 - accuracy: 0.8980 - val_loss: 0.7909 - val_accuracy: 0.8995
Epoch 17/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8065 - accuracy: 0.8983 - val_loss: 0.7899 - val_accuracy: 0.9004
Epoch 18/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8061 - accuracy: 0.8982 - val_loss: 0.7904 - val_accuracy: 0.9005
Epoch 19/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8062 - accuracy: 0.8982 - val_loss: 0.7908 - val_accuracy: 0.9000
Epoch 20/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8062 - accuracy: 0.8980 - val_loss: 0.7892 - val_accuracy: 0.9009
Epoch 21/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8055 - accuracy: 0.8985 - val_loss: 0.7891 - val_accuracy: 0.9009
Epoch 22/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8056 - accuracy: 0.8987 - val_loss: 0.7901 - val_accuracy: 0.9003
Epoch 23/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8057 - accuracy: 0.8985 - val_loss: 0.7892 - val_accuracy: 0.9005
Epoch 24/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8055 - accuracy: 0.8982 - val_loss: 0.7893 - val_accuracy: 0.9009
Epoch 25/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8054 - accuracy: 0.8985 - val_loss: 0.7887 - val_accuracy: 0.9009
Epoch 26/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8054 - accuracy: 0.8984 - val_loss: 0.7895 - val_accuracy: 0.9013
Epoch 27/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.8985 - val_loss: 0.7889 - val_accuracy: 0.9016
Epoch 28/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8049 - accuracy: 0.8989 - val_loss: 0.7889 - val_accuracy: 0.9009
Epoch 29/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.8988 - val_loss: 0.7890 - val_accuracy: 0.9011
Epoch 30/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8052 - accuracy: 0.8985 - val_loss: 0.7887 - val_accuracy: 0.9009
Epoch 31/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8052 - accuracy: 0.8987 - val_loss: 0.7883 - val_accuracy: 0.9010
Epoch 32/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.8989 - val_loss: 0.7891 - val_accuracy: 0.9015
Epoch 33/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8049 - accuracy: 0.8989 - val_loss: 0.7890 - val_accuracy: 0.9008
Epoch 34/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.8987 - val_loss: 0.7884 - val_accuracy: 0.9011
Epoch 35/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8049 - accuracy: 0.8986 - val_loss: 0.7879 - val_accuracy: 0.9012
Epoch 36/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8988 - val_loss: 0.7887 - val_accuracy: 0.9015
Epoch 37/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8047 - accuracy: 0.8988 - val_loss: 0.7876 - val_accuracy: 0.9009
Epoch 38/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8989 - val_loss: 0.7883 - val_accuracy: 0.9013
Epoch 39/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8988 - val_loss: 0.7877 - val_accuracy: 0.9013
Epoch 40/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.8985 - val_loss: 0.7888 - val_accuracy: 0.9013
Epoch 41/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8047 - accuracy: 0.8984 - val_loss: 0.7879 - val_accuracy: 0.9017
Epoch 42/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8990 - val_loss: 0.7892 - val_accuracy: 0.9008
Epoch 43/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8990 - val_loss: 0.7881 - val_accuracy: 0.9019
Epoch 44/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.8988 - val_loss: 0.7883 - val_accuracy: 0.9014
Epoch 45/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8987 - val_loss: 0.7881 - val_accuracy: 0.9006
Epoch 46/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8985 - val_loss: 0.7882 - val_accuracy: 0.9009
Epoch 47/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8987 - val_loss: 0.7883 - val_accuracy: 0.9015
Epoch 48/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8047 - accuracy: 0.8986 - val_loss: 0.7885 - val_accuracy: 0.9017
Epoch 49/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9024
Epoch 50/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8986 - val_loss: 0.7885 - val_accuracy: 0.9004
Epoch 51/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.8985 - val_loss: 0.7877 - val_accuracy: 0.9018
Epoch 52/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8991 - val_loss: 0.7868 - val_accuracy: 0.9013
Epoch 53/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8992 - val_loss: 0.7876 - val_accuracy: 0.9020
Epoch 54/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8988 - val_loss: 0.7861 - val_accuracy: 0.9018
Epoch 55/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.8988 - val_loss: 0.7875 - val_accuracy: 0.9016
Epoch 56/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8989 - val_loss: 0.7869 - val_accuracy: 0.9022
Epoch 57/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8990 - val_loss: 0.7877 - val_accuracy: 0.9017
Epoch 58/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7876 - val_accuracy: 0.9018
Epoch 59/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8990 - val_loss: 0.7864 - val_accuracy: 0.9017
Epoch 60/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7872 - val_accuracy: 0.9017
Epoch 61/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8990 - val_loss: 0.7865 - val_accuracy: 0.9021
Epoch 62/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8990 - val_loss: 0.7861 - val_accuracy: 0.9017
Epoch 63/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8989 - val_loss: 0.7871 - val_accuracy: 0.9024
Epoch 64/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8992 - val_loss: 0.7873 - val_accuracy: 0.9015
Epoch 65/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8989 - val_loss: 0.7874 - val_accuracy: 0.9013
Epoch 66/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8990 - val_loss: 0.7874 - val_accuracy: 0.9018
Epoch 67/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8987 - val_loss: 0.7877 - val_accuracy: 0.9016
Epoch 68/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8992 - val_loss: 0.7877 - val_accuracy: 0.9020
Epoch 69/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9020
Epoch 70/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8992 - val_loss: 0.7869 - val_accuracy: 0.9024
Epoch 71/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8988 - val_loss: 0.7870 - val_accuracy: 0.9016
Epoch 72/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8991 - val_loss: 0.7873 - val_accuracy: 0.9016
Epoch 73/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8990 - val_loss: 0.7869 - val_accuracy: 0.9018
Epoch 74/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7869 - val_accuracy: 0.9020
Epoch 75/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8991 - val_loss: 0.7868 - val_accuracy: 0.9021
Epoch 76/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8990 - val_loss: 0.7872 - val_accuracy: 0.9017
Epoch 77/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8986 - val_loss: 0.7868 - val_accuracy: 0.9019
Epoch 78/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8988 - val_loss: 0.7864 - val_accuracy: 0.9018
Epoch 79/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8992 - val_loss: 0.7870 - val_accuracy: 0.9014
Epoch 80/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8991 - val_loss: 0.7868 - val_accuracy: 0.9020
Epoch 81/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9016
Epoch 82/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7859 - val_accuracy: 0.9019
Epoch 83/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8990 - val_loss: 0.7870 - val_accuracy: 0.9021
Epoch 84/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8989 - val_loss: 0.7872 - val_accuracy: 0.9021
Epoch 85/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8987 - val_loss: 0.7873 - val_accuracy: 0.9019
Epoch 86/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7876 - val_accuracy: 0.9016
Epoch 87/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7872 - val_accuracy: 0.9017
Epoch 88/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7877 - val_accuracy: 0.9015
Epoch 89/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8994 - val_loss: 0.7877 - val_accuracy: 0.9012
Epoch 90/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7866 - val_accuracy: 0.9019
Epoch 91/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7874 - val_accuracy: 0.9024
Epoch 92/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9019
Epoch 93/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8993 - val_loss: 0.7875 - val_accuracy: 0.9019
Epoch 94/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7875 - val_accuracy: 0.9023
Epoch 95/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7877 - val_accuracy: 0.9014
Epoch 96/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8988 - val_loss: 0.7871 - val_accuracy: 0.9017
Epoch 97/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8991 - val_loss: 0.7878 - val_accuracy: 0.9020
Epoch 98/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9020
Epoch 99/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8988 - val_loss: 0.7866 - val_accuracy: 0.9016
Epoch 100/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9021
Epoch 101/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8992 - val_loss: 0.7866 - val_accuracy: 0.9021
Epoch 102/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9015
Epoch 103/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8986 - val_loss: 0.7879 - val_accuracy: 0.9020
Epoch 104/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8991 - val_loss: 0.7870 - val_accuracy: 0.9020
Epoch 105/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7866 - val_accuracy: 0.9022
Epoch 106/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8990 - val_loss: 0.7868 - val_accuracy: 0.9009
Epoch 107/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8992 - val_loss: 0.7861 - val_accuracy: 0.9023
Epoch 108/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8995 - val_loss: 0.7876 - val_accuracy: 0.9012
Epoch 109/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8989 - val_loss: 0.7871 - val_accuracy: 0.9018
Epoch 110/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7866 - val_accuracy: 0.9022
Epoch 111/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7869 - val_accuracy: 0.9020
Epoch 112/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8989 - val_loss: 0.7867 - val_accuracy: 0.9013
Epoch 113/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8989 - val_loss: 0.7872 - val_accuracy: 0.9021
Epoch 114/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8997 - val_loss: 0.7866 - val_accuracy: 0.9018
Epoch 115/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8989 - val_loss: 0.7874 - val_accuracy: 0.9018
Epoch 116/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8992 - val_loss: 0.7862 - val_accuracy: 0.9021
Epoch 117/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7870 - val_accuracy: 0.9018
Epoch 118/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7874 - val_accuracy: 0.9011
Epoch 119/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8991 - val_loss: 0.7870 - val_accuracy: 0.9022
Epoch 120/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8990 - val_loss: 0.7868 - val_accuracy: 0.9021
Epoch 121/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8992 - val_loss: 0.7868 - val_accuracy: 0.9026
Epoch 122/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8988 - val_loss: 0.7867 - val_accuracy: 0.9023
Epoch 123/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7870 - val_accuracy: 0.9022
Epoch 124/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8988 - val_loss: 0.7872 - val_accuracy: 0.9021
Epoch 125/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7868 - val_accuracy: 0.9022
Epoch 126/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8992 - val_loss: 0.7875 - val_accuracy: 0.9013
Epoch 127/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8990 - val_loss: 0.7869 - val_accuracy: 0.9019
Epoch 128/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8992 - val_loss: 0.7875 - val_accuracy: 0.9020
Epoch 129/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7870 - val_accuracy: 0.9020
Epoch 130/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8990 - val_loss: 0.7866 - val_accuracy: 0.9025
Epoch 131/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8989 - val_loss: 0.7863 - val_accuracy: 0.9025
Epoch 132/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7869 - val_accuracy: 0.9018
Epoch 133/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7871 - val_accuracy: 0.9018
Epoch 134/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8995 - val_loss: 0.7877 - val_accuracy: 0.9017
Epoch 135/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.8993 - val_loss: 0.7869 - val_accuracy: 0.9017
Epoch 136/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8994 - val_loss: 0.7871 - val_accuracy: 0.9019
Epoch 137/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8992 - val_loss: 0.7859 - val_accuracy: 0.9022
Epoch 138/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8992 - val_loss: 0.7871 - val_accuracy: 0.9018
Epoch 139/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8988 - val_loss: 0.7871 - val_accuracy: 0.9021
Epoch 140/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7865 - val_accuracy: 0.9015
Epoch 141/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8991 - val_loss: 0.7866 - val_accuracy: 0.9020
Epoch 142/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7868 - val_accuracy: 0.9017
Epoch 143/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8991 - val_loss: 0.7874 - val_accuracy: 0.9021
Epoch 144/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8990 - val_loss: 0.7862 - val_accuracy: 0.9020
Epoch 145/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7866 - val_accuracy: 0.9018
Epoch 146/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7875 - val_accuracy: 0.9014
Epoch 147/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8988 - val_loss: 0.7864 - val_accuracy: 0.9019
Epoch 148/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8994 - val_loss: 0.7875 - val_accuracy: 0.9013
Epoch 149/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7865 - val_accuracy: 0.9024
Epoch 150/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8994 - val_loss: 0.7868 - val_accuracy: 0.9019
Epoch 151/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8990 - val_loss: 0.7873 - val_accuracy: 0.9020
Epoch 152/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8986 - val_loss: 0.7868 - val_accuracy: 0.9016
Epoch 153/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8991 - val_loss: 0.7870 - val_accuracy: 0.9023
Epoch 154/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8994 - val_loss: 0.7866 - val_accuracy: 0.9022
Epoch 155/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7862 - val_accuracy: 0.9018
Epoch 156/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8992 - val_loss: 0.7869 - val_accuracy: 0.9020
Epoch 157/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8993 - val_loss: 0.7869 - val_accuracy: 0.9017
Epoch 158/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8991 - val_loss: 0.7872 - val_accuracy: 0.9021
Epoch 159/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.8990 - val_loss: 0.7871 - val_accuracy: 0.9017
Epoch 160/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8994 - val_loss: 0.7872 - val_accuracy: 0.9015
Epoch 161/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8992 - val_loss: 0.7878 - val_accuracy: 0.9011
Epoch 162/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7861 - val_accuracy: 0.9021
Epoch 163/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7866 - val_accuracy: 0.9018
Epoch 164/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7864 - val_accuracy: 0.9020
Epoch 165/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8995 - val_loss: 0.7876 - val_accuracy: 0.9020
Epoch 166/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8990 - val_loss: 0.7864 - val_accuracy: 0.9019
Epoch 167/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7873 - val_accuracy: 0.9022
Epoch 168/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8990 - val_loss: 0.7865 - val_accuracy: 0.9017
Epoch 169/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8996 - val_loss: 0.7871 - val_accuracy: 0.9019
Epoch 170/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8994 - val_loss: 0.7870 - val_accuracy: 0.9015
Epoch 171/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8994 - val_loss: 0.7871 - val_accuracy: 0.9021
Epoch 172/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8991 - val_loss: 0.7868 - val_accuracy: 0.9022
Epoch 173/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9021
Epoch 174/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8995 - val_loss: 0.7864 - val_accuracy: 0.9019
Epoch 175/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7870 - val_accuracy: 0.9024
Epoch 176/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8990 - val_loss: 0.7868 - val_accuracy: 0.9018
Epoch 177/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8989 - val_loss: 0.7863 - val_accuracy: 0.9023
Epoch 178/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8992 - val_loss: 0.7873 - val_accuracy: 0.9022
Epoch 179/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8988 - val_loss: 0.7869 - val_accuracy: 0.9020
Epoch 180/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8988 - val_loss: 0.7873 - val_accuracy: 0.9021
Epoch 181/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8990 - val_loss: 0.7861 - val_accuracy: 0.9019
Epoch 182/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8990 - val_loss: 0.7874 - val_accuracy: 0.9019
Epoch 183/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7861 - val_accuracy: 0.9022
Epoch 184/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7875 - val_accuracy: 0.9020
Epoch 185/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7870 - val_accuracy: 0.9017
Epoch 186/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8994 - val_loss: 0.7873 - val_accuracy: 0.9014
Epoch 187/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8994 - val_loss: 0.7868 - val_accuracy: 0.9021
Epoch 188/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9018
Epoch 189/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8993 - val_loss: 0.7869 - val_accuracy: 0.9020
Epoch 190/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8993 - val_loss: 0.7868 - val_accuracy: 0.9020
Epoch 191/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8990 - val_loss: 0.7866 - val_accuracy: 0.9020
Epoch 192/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8988 - val_loss: 0.7869 - val_accuracy: 0.9021
Epoch 193/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7869 - val_accuracy: 0.9023
Epoch 194/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8992 - val_loss: 0.7873 - val_accuracy: 0.9024
Epoch 195/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9025
Epoch 196/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7864 - val_accuracy: 0.9023
Epoch 197/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8990 - val_loss: 0.7871 - val_accuracy: 0.9024
Epoch 198/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7865 - val_accuracy: 0.9017
Epoch 199/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7869 - val_accuracy: 0.9022
Epoch 200/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8993 - val_loss: 0.7859 - val_accuracy: 0.9024
Epoch 1/200
235/235 [==============================] - 2s 8ms/step - loss: 0.4727 - accuracy: 0.8708 - val_loss: 0.2562 - val_accuracy: 0.9248
Epoch 2/200
235/235 [==============================] - 2s 8ms/step - loss: 0.2279 - accuracy: 0.9343 - val_loss: 0.1894 - val_accuracy: 0.9429
Epoch 3/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1709 - accuracy: 0.9510 - val_loss: 0.1531 - val_accuracy: 0.9549
Epoch 4/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1367 - accuracy: 0.9603 - val_loss: 0.1326 - val_accuracy: 0.9604
Epoch 5/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1131 - accuracy: 0.9664 - val_loss: 0.1207 - val_accuracy: 0.9624
Epoch 6/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0953 - accuracy: 0.9717 - val_loss: 0.1125 - val_accuracy: 0.9651
Epoch 7/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0812 - accuracy: 0.9758 - val_loss: 0.1078 - val_accuracy: 0.9670
Epoch 8/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0699 - accuracy: 0.9790 - val_loss: 0.1033 - val_accuracy: 0.9676
Epoch 9/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0603 - accuracy: 0.9823 - val_loss: 0.1012 - val_accuracy: 0.9686
Epoch 10/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0519 - accuracy: 0.9850 - val_loss: 0.0991 - val_accuracy: 0.9689
Epoch 11/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0448 - accuracy: 0.9876 - val_loss: 0.1000 - val_accuracy: 0.9693
Epoch 12/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0388 - accuracy: 0.9896 - val_loss: 0.0995 - val_accuracy: 0.9699
Epoch 13/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0336 - accuracy: 0.9915 - val_loss: 0.1003 - val_accuracy: 0.9716
Epoch 14/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0289 - accuracy: 0.9929 - val_loss: 0.1015 - val_accuracy: 0.9715
Epoch 15/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0250 - accuracy: 0.9943 - val_loss: 0.1025 - val_accuracy: 0.9718
Epoch 16/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0215 - accuracy: 0.9955 - val_loss: 0.1043 - val_accuracy: 0.9724
Epoch 17/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0186 - accuracy: 0.9965 - val_loss: 0.1068 - val_accuracy: 0.9724
Epoch 18/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.1082 - val_accuracy: 0.9724
Epoch 19/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0138 - accuracy: 0.9978 - val_loss: 0.1132 - val_accuracy: 0.9718
Epoch 20/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0119 - accuracy: 0.9984 - val_loss: 0.1175 - val_accuracy: 0.9717
Epoch 21/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0107 - accuracy: 0.9985 - val_loss: 0.1230 - val_accuracy: 0.9710
Epoch 22/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0095 - accuracy: 0.9986 - val_loss: 0.1242 - val_accuracy: 0.9713
Epoch 23/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0090 - accuracy: 0.9985 - val_loss: 0.1363 - val_accuracy: 0.9706
Epoch 24/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9983 - val_loss: 0.1312 - val_accuracy: 0.9709
Epoch 25/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0084 - accuracy: 0.9983 - val_loss: 0.1423 - val_accuracy: 0.9709
Epoch 26/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0088 - accuracy: 0.9977 - val_loss: 0.1384 - val_accuracy: 0.9715
Epoch 27/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0078 - accuracy: 0.9981 - val_loss: 0.1490 - val_accuracy: 0.9706
Epoch 28/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0070 - accuracy: 0.9981 - val_loss: 0.1358 - val_accuracy: 0.9725
Epoch 29/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0061 - accuracy: 0.9985 - val_loss: 0.1359 - val_accuracy: 0.9735
Epoch 30/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0057 - accuracy: 0.9985 - val_loss: 0.1346 - val_accuracy: 0.9726
Epoch 31/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9990 - val_loss: 0.1401 - val_accuracy: 0.9728
Epoch 32/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9989 - val_loss: 0.1454 - val_accuracy: 0.9725
Epoch 33/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0035 - accuracy: 0.9995 - val_loss: 0.1540 - val_accuracy: 0.9717
Epoch 34/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9990 - val_loss: 0.1417 - val_accuracy: 0.9732
Epoch 35/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.1620 - val_accuracy: 0.9697
Epoch 36/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.1590 - val_accuracy: 0.9701
Epoch 37/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.1550 - val_accuracy: 0.9721
Epoch 38/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.1539 - val_accuracy: 0.9729
Epoch 39/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.1458 - val_accuracy: 0.9741
Epoch 40/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 0.9998 - val_loss: 0.1515 - val_accuracy: 0.9727
Epoch 41/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 0.9998 - val_loss: 0.1595 - val_accuracy: 0.9720
Epoch 42/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.1482 - val_accuracy: 0.9737
Epoch 43/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0051 - accuracy: 0.9981 - val_loss: 0.1798 - val_accuracy: 0.9677
Epoch 44/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0075 - accuracy: 0.9975 - val_loss: 0.1630 - val_accuracy: 0.9722
Epoch 45/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.1630 - val_accuracy: 0.9713
Epoch 46/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1505 - val_accuracy: 0.9739
Epoch 47/200
235/235 [==============================] - 2s 8ms/step - loss: 8.2963e-04 - accuracy: 0.9999 - val_loss: 0.1476 - val_accuracy: 0.9747
Epoch 48/200
235/235 [==============================] - 2s 8ms/step - loss: 6.1204e-04 - accuracy: 1.0000 - val_loss: 0.1499 - val_accuracy: 0.9749
Epoch 49/200
235/235 [==============================] - 2s 8ms/step - loss: 6.5627e-04 - accuracy: 0.9999 - val_loss: 0.1532 - val_accuracy: 0.9742
Epoch 50/200
235/235 [==============================] - 2s 8ms/step - loss: 3.0503e-04 - accuracy: 1.0000 - val_loss: 0.1511 - val_accuracy: 0.9744
Epoch 51/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2239e-04 - accuracy: 1.0000 - val_loss: 0.1514 - val_accuracy: 0.9746
Epoch 52/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8022e-04 - accuracy: 1.0000 - val_loss: 0.1519 - val_accuracy: 0.9744
Epoch 53/200
235/235 [==============================] - 2s 8ms/step - loss: 1.5741e-04 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9745
Epoch 54/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4068e-04 - accuracy: 1.0000 - val_loss: 0.1527 - val_accuracy: 0.9744
Epoch 55/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2738e-04 - accuracy: 1.0000 - val_loss: 0.1533 - val_accuracy: 0.9743
Epoch 56/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1583e-04 - accuracy: 1.0000 - val_loss: 0.1540 - val_accuracy: 0.9744
Epoch 57/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0558e-04 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9744
Epoch 58/200
235/235 [==============================] - 2s 8ms/step - loss: 9.6603e-05 - accuracy: 1.0000 - val_loss: 0.1555 - val_accuracy: 0.9747
Epoch 59/200
235/235 [==============================] - 2s 8ms/step - loss: 8.8239e-05 - accuracy: 1.0000 - val_loss: 0.1564 - val_accuracy: 0.9746
Epoch 60/200
235/235 [==============================] - 2s 8ms/step - loss: 8.0766e-05 - accuracy: 1.0000 - val_loss: 0.1572 - val_accuracy: 0.9751
Epoch 61/200
235/235 [==============================] - 2s 8ms/step - loss: 7.3818e-05 - accuracy: 1.0000 - val_loss: 0.1583 - val_accuracy: 0.9752
Epoch 62/200
235/235 [==============================] - 2s 8ms/step - loss: 6.7451e-05 - accuracy: 1.0000 - val_loss: 0.1593 - val_accuracy: 0.9752
Epoch 63/200
235/235 [==============================] - 2s 8ms/step - loss: 6.1634e-05 - accuracy: 1.0000 - val_loss: 0.1603 - val_accuracy: 0.9754
Epoch 64/200
235/235 [==============================] - 2s 8ms/step - loss: 5.6047e-05 - accuracy: 1.0000 - val_loss: 0.1613 - val_accuracy: 0.9755
Epoch 65/200
235/235 [==============================] - 2s 9ms/step - loss: 5.1083e-05 - accuracy: 1.0000 - val_loss: 0.1625 - val_accuracy: 0.9756
Epoch 66/200
235/235 [==============================] - 2s 9ms/step - loss: 4.6468e-05 - accuracy: 1.0000 - val_loss: 0.1637 - val_accuracy: 0.9756
Epoch 67/200
235/235 [==============================] - 2s 8ms/step - loss: 4.2245e-05 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9755
Epoch 68/200
235/235 [==============================] - 2s 8ms/step - loss: 3.8341e-05 - accuracy: 1.0000 - val_loss: 0.1661 - val_accuracy: 0.9754
Epoch 69/200
235/235 [==============================] - 2s 8ms/step - loss: 3.4662e-05 - accuracy: 1.0000 - val_loss: 0.1675 - val_accuracy: 0.9753
Epoch 70/200
235/235 [==============================] - 2s 8ms/step - loss: 3.1363e-05 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.9753
Epoch 71/200
235/235 [==============================] - 2s 8ms/step - loss: 2.8292e-05 - accuracy: 1.0000 - val_loss: 0.1702 - val_accuracy: 0.9753
Epoch 72/200
235/235 [==============================] - 2s 8ms/step - loss: 2.5557e-05 - accuracy: 1.0000 - val_loss: 0.1717 - val_accuracy: 0.9753
Epoch 73/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2980e-05 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9754
Epoch 74/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0661e-05 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9753
Epoch 75/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8541e-05 - accuracy: 1.0000 - val_loss: 0.1761 - val_accuracy: 0.9753
Epoch 76/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6647e-05 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9753
Epoch 77/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4888e-05 - accuracy: 1.0000 - val_loss: 0.1792 - val_accuracy: 0.9752
Epoch 78/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3299e-05 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9752
Epoch 79/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1920e-05 - accuracy: 1.0000 - val_loss: 0.1825 - val_accuracy: 0.9750
Epoch 80/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0625e-05 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9749
Epoch 81/200
235/235 [==============================] - 2s 8ms/step - loss: 9.4743e-06 - accuracy: 1.0000 - val_loss: 0.1858 - val_accuracy: 0.9749
Epoch 82/200
235/235 [==============================] - 2s 8ms/step - loss: 8.4510e-06 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9749
Epoch 83/200
235/235 [==============================] - 2s 8ms/step - loss: 7.5154e-06 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9748
Epoch 84/200
235/235 [==============================] - 2s 8ms/step - loss: 6.6881e-06 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9750
Epoch 85/200
235/235 [==============================] - 2s 8ms/step - loss: 5.9486e-06 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9749
Epoch 86/200
235/235 [==============================] - 2s 8ms/step - loss: 5.2764e-06 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9750
Epoch 87/200
235/235 [==============================] - 2s 8ms/step - loss: 4.6952e-06 - accuracy: 1.0000 - val_loss: 0.1962 - val_accuracy: 0.9751
Epoch 88/200
235/235 [==============================] - 2s 8ms/step - loss: 4.1655e-06 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9752
Epoch 89/200
235/235 [==============================] - 2s 8ms/step - loss: 3.6936e-06 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9751
Epoch 90/200
235/235 [==============================] - 2s 8ms/step - loss: 3.2785e-06 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9752
Epoch 91/200
235/235 [==============================] - 2s 8ms/step - loss: 2.9047e-06 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9752
Epoch 92/200
235/235 [==============================] - 2s 8ms/step - loss: 2.5774e-06 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9752
Epoch 93/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2841e-06 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9750
Epoch 94/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0245e-06 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9749
Epoch 95/200
235/235 [==============================] - 2s 8ms/step - loss: 1.7953e-06 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9749
Epoch 96/200
235/235 [==============================] - 2s 8ms/step - loss: 1.5899e-06 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9748
Epoch 97/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4100e-06 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9749
Epoch 98/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2515e-06 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9749
Epoch 99/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1105e-06 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9747
Epoch 100/200
235/235 [==============================] - 2s 8ms/step - loss: 9.8493e-07 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 0.9748
Epoch 101/200
235/235 [==============================] - 2s 8ms/step - loss: 8.7410e-07 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9746
Epoch 102/200
235/235 [==============================] - 2s 8ms/step - loss: 7.7644e-07 - accuracy: 1.0000 - val_loss: 0.2230 - val_accuracy: 0.9746
Epoch 103/200
235/235 [==============================] - 2s 8ms/step - loss: 6.8919e-07 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9748
Epoch 104/200
235/235 [==============================] - 2s 8ms/step - loss: 6.1290e-07 - accuracy: 1.0000 - val_loss: 0.2265 - val_accuracy: 0.9747
Epoch 105/200
235/235 [==============================] - 2s 8ms/step - loss: 5.4580e-07 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9748
Epoch 106/200
235/235 [==============================] - 2s 8ms/step - loss: 4.8559e-07 - accuracy: 1.0000 - val_loss: 0.2299 - val_accuracy: 0.9747
Epoch 107/200
235/235 [==============================] - 2s 8ms/step - loss: 4.3296e-07 - accuracy: 1.0000 - val_loss: 0.2316 - val_accuracy: 0.9747
Epoch 108/200
235/235 [==============================] - 2s 8ms/step - loss: 3.8604e-07 - accuracy: 1.0000 - val_loss: 0.2334 - val_accuracy: 0.9745
Epoch 109/200
235/235 [==============================] - 2s 8ms/step - loss: 3.4529e-07 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 0.9745
Epoch 110/200
235/235 [==============================] - 2s 8ms/step - loss: 3.0814e-07 - accuracy: 1.0000 - val_loss: 0.2368 - val_accuracy: 0.9745
Epoch 111/200
235/235 [==============================] - 2s 7ms/step - loss: 2.7618e-07 - accuracy: 1.0000 - val_loss: 0.2383 - val_accuracy: 0.9745
Epoch 112/200
235/235 [==============================] - 2s 8ms/step - loss: 2.4750e-07 - accuracy: 1.0000 - val_loss: 0.2399 - val_accuracy: 0.9745
Epoch 113/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2214e-07 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9746
Epoch 114/200
235/235 [==============================] - 2s 8ms/step - loss: 1.9964e-07 - accuracy: 1.0000 - val_loss: 0.2431 - val_accuracy: 0.9746
Epoch 115/200
235/235 [==============================] - 2s 8ms/step - loss: 1.7973e-07 - accuracy: 1.0000 - val_loss: 0.2446 - val_accuracy: 0.9745
Epoch 116/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6200e-07 - accuracy: 1.0000 - val_loss: 0.2461 - val_accuracy: 0.9744
Epoch 117/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4651e-07 - accuracy: 1.0000 - val_loss: 0.2476 - val_accuracy: 0.9744
Epoch 118/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3242e-07 - accuracy: 1.0000 - val_loss: 0.2490 - val_accuracy: 0.9745
Epoch 119/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2014e-07 - accuracy: 1.0000 - val_loss: 0.2504 - val_accuracy: 0.9745
Epoch 120/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0935e-07 - accuracy: 1.0000 - val_loss: 0.2517 - val_accuracy: 0.9745
Epoch 121/200
235/235 [==============================] - 2s 8ms/step - loss: 9.9643e-08 - accuracy: 1.0000 - val_loss: 0.2530 - val_accuracy: 0.9745
Epoch 122/200
235/235 [==============================] - 2s 8ms/step - loss: 9.0901e-08 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9745
Epoch 123/200
235/235 [==============================] - 2s 8ms/step - loss: 8.3252e-08 - accuracy: 1.0000 - val_loss: 0.2555 - val_accuracy: 0.9745
Epoch 124/200
235/235 [==============================] - 2s 8ms/step - loss: 7.6210e-08 - accuracy: 1.0000 - val_loss: 0.2568 - val_accuracy: 0.9745
Epoch 125/200
235/235 [==============================] - 2s 8ms/step - loss: 7.0079e-08 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9745
Epoch 126/200
235/235 [==============================] - 2s 8ms/step - loss: 6.4592e-08 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9746
Epoch 127/200
235/235 [==============================] - 2s 8ms/step - loss: 5.9617e-08 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9746
Epoch 128/200
235/235 [==============================] - 2s 8ms/step - loss: 5.5186e-08 - accuracy: 1.0000 - val_loss: 0.2613 - val_accuracy: 0.9746
Epoch 129/200
235/235 [==============================] - 2s 8ms/step - loss: 5.1127e-08 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9747
Epoch 130/200
235/235 [==============================] - 2s 8ms/step - loss: 4.7588e-08 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9746
Epoch 131/200
235/235 [==============================] - 2s 8ms/step - loss: 4.4378e-08 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9748
Epoch 132/200
235/235 [==============================] - 2s 8ms/step - loss: 4.1358e-08 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9748
Epoch 133/200
235/235 [==============================] - 2s 8ms/step - loss: 3.8733e-08 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9748
Epoch 134/200
235/235 [==============================] - 2s 8ms/step - loss: 3.6363e-08 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9748
Epoch 135/200
235/235 [==============================] - 2s 8ms/step - loss: 3.4171e-08 - accuracy: 1.0000 - val_loss: 0.2678 - val_accuracy: 0.9749
Epoch 136/200
235/235 [==============================] - 2s 8ms/step - loss: 3.2298e-08 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9749
Epoch 137/200
235/235 [==============================] - 2s 8ms/step - loss: 3.0446e-08 - accuracy: 1.0000 - val_loss: 0.2693 - val_accuracy: 0.9749
Epoch 138/200
235/235 [==============================] - 2s 8ms/step - loss: 2.8797e-08 - accuracy: 1.0000 - val_loss: 0.2701 - val_accuracy: 0.9749
Epoch 139/200
235/235 [==============================] - 2s 8ms/step - loss: 2.7237e-08 - accuracy: 1.0000 - val_loss: 0.2708 - val_accuracy: 0.9748
Epoch 140/200
235/235 [==============================] - 2s 8ms/step - loss: 2.5892e-08 - accuracy: 1.0000 - val_loss: 0.2715 - val_accuracy: 0.9748
Epoch 141/200
235/235 [==============================] - 2s 8ms/step - loss: 2.4565e-08 - accuracy: 1.0000 - val_loss: 0.2722 - val_accuracy: 0.9749
Epoch 142/200
235/235 [==============================] - 2s 8ms/step - loss: 2.3387e-08 - accuracy: 1.0000 - val_loss: 0.2729 - val_accuracy: 0.9748
Epoch 143/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2316e-08 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9748
Epoch 144/200
235/235 [==============================] - 2s 8ms/step - loss: 2.1350e-08 - accuracy: 1.0000 - val_loss: 0.2742 - val_accuracy: 0.9749
Epoch 145/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0500e-08 - accuracy: 1.0000 - val_loss: 0.2748 - val_accuracy: 0.9750
Epoch 146/200
235/235 [==============================] - 2s 8ms/step - loss: 1.9602e-08 - accuracy: 1.0000 - val_loss: 0.2752 - val_accuracy: 0.9750
Epoch 147/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8803e-08 - accuracy: 1.0000 - val_loss: 0.2759 - val_accuracy: 0.9750
Epoch 148/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8060e-08 - accuracy: 1.0000 - val_loss: 0.2765 - val_accuracy: 0.9751
Epoch 149/200
235/235 [==============================] - 2s 8ms/step - loss: 1.7349e-08 - accuracy: 1.0000 - val_loss: 0.2769 - val_accuracy: 0.9750
Epoch 150/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6669e-08 - accuracy: 1.0000 - val_loss: 0.2774 - val_accuracy: 0.9751
Epoch 151/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6123e-08 - accuracy: 1.0000 - val_loss: 0.2779 - val_accuracy: 0.9750
Epoch 152/200
235/235 [==============================] - 2s 8ms/step - loss: 1.5545e-08 - accuracy: 1.0000 - val_loss: 0.2782 - val_accuracy: 0.9749
Epoch 153/200
235/235 [==============================] - 2s 8ms/step - loss: 1.5036e-08 - accuracy: 1.0000 - val_loss: 0.2788 - val_accuracy: 0.9750
Epoch 154/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4565e-08 - accuracy: 1.0000 - val_loss: 0.2791 - val_accuracy: 0.9750
Epoch 155/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4077e-08 - accuracy: 1.0000 - val_loss: 0.2796 - val_accuracy: 0.9750
Epoch 156/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3645e-08 - accuracy: 1.0000 - val_loss: 0.2800 - val_accuracy: 0.9750
Epoch 157/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3230e-08 - accuracy: 1.0000 - val_loss: 0.2803 - val_accuracy: 0.9750
Epoch 158/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2837e-08 - accuracy: 1.0000 - val_loss: 0.2806 - val_accuracy: 0.9750
Epoch 159/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2438e-08 - accuracy: 1.0000 - val_loss: 0.2809 - val_accuracy: 0.9750
Epoch 160/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2094e-08 - accuracy: 1.0000 - val_loss: 0.2814 - val_accuracy: 0.9750
Epoch 161/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1750e-08 - accuracy: 1.0000 - val_loss: 0.2816 - val_accuracy: 0.9750
Epoch 162/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1379e-08 - accuracy: 1.0000 - val_loss: 0.2818 - val_accuracy: 0.9749
Epoch 163/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1086e-08 - accuracy: 1.0000 - val_loss: 0.2820 - val_accuracy: 0.9750
Epoch 164/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0786e-08 - accuracy: 1.0000 - val_loss: 0.2824 - val_accuracy: 0.9751
Epoch 165/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0602e-08 - accuracy: 1.0000 - val_loss: 0.2825 - val_accuracy: 0.9749
Epoch 166/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0316e-08 - accuracy: 1.0000 - val_loss: 0.2828 - val_accuracy: 0.9750
Epoch 167/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0061e-08 - accuracy: 1.0000 - val_loss: 0.2831 - val_accuracy: 0.9749
Epoch 168/200
235/235 [==============================] - 2s 8ms/step - loss: 9.8288e-09 - accuracy: 1.0000 - val_loss: 0.2832 - val_accuracy: 0.9750
Epoch 169/200
235/235 [==============================] - 2s 8ms/step - loss: 9.6401e-09 - accuracy: 1.0000 - val_loss: 0.2836 - val_accuracy: 0.9749
Epoch 170/200
235/235 [==============================] - 2s 8ms/step - loss: 9.3997e-09 - accuracy: 1.0000 - val_loss: 0.2836 - val_accuracy: 0.9749
Epoch 171/200
235/235 [==============================] - 2s 8ms/step - loss: 9.2208e-09 - accuracy: 1.0000 - val_loss: 0.2839 - val_accuracy: 0.9749
Epoch 172/200
235/235 [==============================] - 2s 8ms/step - loss: 9.0082e-09 - accuracy: 1.0000 - val_loss: 0.2841 - val_accuracy: 0.9749
Epoch 173/200
235/235 [==============================] - 2s 8ms/step - loss: 8.7758e-09 - accuracy: 1.0000 - val_loss: 0.2843 - val_accuracy: 0.9749
Epoch 174/200
235/235 [==============================] - 2s 8ms/step - loss: 8.6387e-09 - accuracy: 1.0000 - val_loss: 0.2844 - val_accuracy: 0.9749
Epoch 175/200
235/235 [==============================] - 2s 8ms/step - loss: 8.4182e-09 - accuracy: 1.0000 - val_loss: 0.2848 - val_accuracy: 0.9749
Epoch 176/200
235/235 [==============================] - 2s 8ms/step - loss: 8.2751e-09 - accuracy: 1.0000 - val_loss: 0.2851 - val_accuracy: 0.9749
Epoch 177/200
235/235 [==============================] - 2s 8ms/step - loss: 8.1182e-09 - accuracy: 1.0000 - val_loss: 0.2853 - val_accuracy: 0.9749
Epoch 178/200
235/235 [==============================] - 2s 8ms/step - loss: 7.9930e-09 - accuracy: 1.0000 - val_loss: 0.2854 - val_accuracy: 0.9749
Epoch 179/200
235/235 [==============================] - 2s 8ms/step - loss: 7.7486e-09 - accuracy: 1.0000 - val_loss: 0.2856 - val_accuracy: 0.9748
Epoch 180/200
235/235 [==============================] - 2s 8ms/step - loss: 7.6751e-09 - accuracy: 1.0000 - val_loss: 0.2857 - val_accuracy: 0.9747
Epoch 181/200
235/235 [==============================] - 2s 8ms/step - loss: 7.5042e-09 - accuracy: 1.0000 - val_loss: 0.2858 - val_accuracy: 0.9748
Epoch 182/200
235/235 [==============================] - 2s 8ms/step - loss: 7.3870e-09 - accuracy: 1.0000 - val_loss: 0.2861 - val_accuracy: 0.9747
Epoch 183/200
235/235 [==============================] - 2s 8ms/step - loss: 7.1903e-09 - accuracy: 1.0000 - val_loss: 0.2862 - val_accuracy: 0.9747
Epoch 184/200
235/235 [==============================] - 2s 8ms/step - loss: 7.0969e-09 - accuracy: 1.0000 - val_loss: 0.2863 - val_accuracy: 0.9748
Epoch 185/200
235/235 [==============================] - 2s 8ms/step - loss: 6.9559e-09 - accuracy: 1.0000 - val_loss: 0.2866 - val_accuracy: 0.9747
Epoch 186/200
235/235 [==============================] - 2s 8ms/step - loss: 6.8247e-09 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9746
Epoch 187/200
235/235 [==============================] - 2s 8ms/step - loss: 6.7055e-09 - accuracy: 1.0000 - val_loss: 0.2869 - val_accuracy: 0.9746
Epoch 188/200
235/235 [==============================] - 2s 8ms/step - loss: 6.5764e-09 - accuracy: 1.0000 - val_loss: 0.2870 - val_accuracy: 0.9745
Epoch 189/200
235/235 [==============================] - 2s 8ms/step - loss: 6.4969e-09 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9746
Epoch 190/200
235/235 [==============================] - 2s 8ms/step - loss: 6.3817e-09 - accuracy: 1.0000 - val_loss: 0.2872 - val_accuracy: 0.9745
Epoch 191/200
235/235 [==============================] - 2s 8ms/step - loss: 6.2744e-09 - accuracy: 1.0000 - val_loss: 0.2873 - val_accuracy: 0.9745
Epoch 192/200
235/235 [==============================] - 2s 8ms/step - loss: 6.1631e-09 - accuracy: 1.0000 - val_loss: 0.2875 - val_accuracy: 0.9745
Epoch 193/200
235/235 [==============================] - 2s 8ms/step - loss: 6.0618e-09 - accuracy: 1.0000 - val_loss: 0.2876 - val_accuracy: 0.9746
Epoch 194/200
235/235 [==============================] - 2s 8ms/step - loss: 5.9446e-09 - accuracy: 1.0000 - val_loss: 0.2877 - val_accuracy: 0.9747
Epoch 195/200
235/235 [==============================] - 2s 8ms/step - loss: 5.8671e-09 - accuracy: 1.0000 - val_loss: 0.2878 - val_accuracy: 0.9746
Epoch 196/200
235/235 [==============================] - 2s 8ms/step - loss: 5.7062e-09 - accuracy: 1.0000 - val_loss: 0.2879 - val_accuracy: 0.9746
Epoch 197/200
235/235 [==============================] - 2s 8ms/step - loss: 5.6187e-09 - accuracy: 1.0000 - val_loss: 0.2880 - val_accuracy: 0.9747
Epoch 198/200
235/235 [==============================] - 2s 8ms/step - loss: 5.5591e-09 - accuracy: 1.0000 - val_loss: 0.2882 - val_accuracy: 0.9747
Epoch 199/200
235/235 [==============================] - 2s 8ms/step - loss: 5.4995e-09 - accuracy: 1.0000 - val_loss: 0.2883 - val_accuracy: 0.9747
Epoch 200/200
235/235 [==============================] - 2s 8ms/step - loss: 5.3863e-09 - accuracy: 1.0000 - val_loss: 0.2884 - val_accuracy: 0.9747
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.03702937625348568
Thresholhold 0.04698251932859421
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.06157276965677738
Thresholhold 0.09646058827638626
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.11527079716324806
Thresholhold 0.05421745777130127
Using suggest threshold.
Applying new mask
Percentage zeros 0.249
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 0. 1. 1.]
 [0. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 0. 1. 1.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 0. 1. 1. 0. 0. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
  1/235 [..............................] - ETA: 4:22:18 - loss: 8.0735 - accuracy: 0.1133WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0086s vs `on_train_batch_begin` time: 11.0853s). Check your callbacks.
235/235 [==============================] - 70s 12ms/step - loss: 2.1990 - accuracy: 0.9126 - val_loss: 1.8356 - val_accuracy: 0.7753
[ 3.2516712e-07 -2.7024671e-06 -3.1244156e-07 ... -2.8429953e-03
 -8.9802414e-02 -1.4404383e-01]
Sparsity at: 0.4990570999248685
Epoch 2/500
235/235 [==============================] - 3s 13ms/step - loss: 0.4643 - accuracy: 0.9624 - val_loss: 0.6491 - val_accuracy: 0.9479
[-1.9452903e-13 -5.1824933e-12 -7.5400027e-15 ...  3.2725368e-02
 -6.4627461e-02 -1.2673180e-01]
Sparsity at: 0.4990570999248685
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2954 - accuracy: 0.9670 - val_loss: 0.3028 - val_accuracy: 0.9622
[-7.9141589e-18 -6.5815181e-17  7.1753617e-18 ...  5.0768904e-02
 -4.7156829e-02 -9.4213687e-02]
Sparsity at: 0.4990570999248685
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2580 - accuracy: 0.9700 - val_loss: 0.2929 - val_accuracy: 0.9549
[ 1.3020048e-23 -2.9036794e-22 -2.1165660e-23 ...  7.3847942e-02
 -4.4844553e-02 -6.8516657e-02]
Sparsity at: 0.4990570999248685
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2385 - accuracy: 0.9712 - val_loss: 0.2767 - val_accuracy: 0.9564
[-1.6890093e-28  2.3128108e-28  1.7465688e-28 ...  9.8315038e-02
 -4.8644219e-02 -5.2487023e-02]
Sparsity at: 0.4990570999248685
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2282 - accuracy: 0.9732 - val_loss: 0.2569 - val_accuracy: 0.9619
[ 2.4301027e-35 -7.7633192e-34 -1.5248102e-34 ...  1.2401281e-01
 -4.3334827e-02 -3.7429992e-02]
Sparsity at: 0.4990570999248685
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2177 - accuracy: 0.9736 - val_loss: 0.2609 - val_accuracy: 0.9583
[ 2.4301027e-35  6.2977183e-34 -1.5248102e-34 ...  1.3538611e-01
 -3.9015461e-02 -3.7069984e-02]
Sparsity at: 0.4990570999248685
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2097 - accuracy: 0.9745 - val_loss: 0.2403 - val_accuracy: 0.9628
[ 2.4301027e-35  6.2977183e-34 -1.5248102e-34 ...  1.4269824e-01
 -3.0737000e-02 -3.2334145e-02]
Sparsity at: 0.4990570999248685
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2014 - accuracy: 0.9748 - val_loss: 0.2477 - val_accuracy: 0.9595
[ 2.4301027e-35  6.2977183e-34 -1.5248102e-34 ...  1.3941075e-01
 -3.3443172e-02 -2.8537080e-02]
Sparsity at: 0.4990570999248685
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1980 - accuracy: 0.9753 - val_loss: 0.2311 - val_accuracy: 0.9620
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.4180030e-01
 -2.9412949e-02 -2.0636633e-02]
Sparsity at: 0.4990570999248685
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1903 - accuracy: 0.9766 - val_loss: 0.2325 - val_accuracy: 0.9609
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.4770201e-01
 -3.2879747e-02 -2.1796416e-02]
Sparsity at: 0.49906085649887305
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1896 - accuracy: 0.9757 - val_loss: 0.2238 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.4073397e-01
 -3.4778457e-02 -2.2761177e-02]
Sparsity at: 0.49906085649887305
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1857 - accuracy: 0.9757 - val_loss: 0.2309 - val_accuracy: 0.9602
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.4807923e-01
 -3.5239980e-02 -2.5581982e-02]
Sparsity at: 0.49906085649887305
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1793 - accuracy: 0.9771 - val_loss: 0.2315 - val_accuracy: 0.9569
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.4449796e-01
 -3.4427825e-02 -3.0831164e-02]
Sparsity at: 0.49906085649887305
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1800 - accuracy: 0.9763 - val_loss: 0.2507 - val_accuracy: 0.9522
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.4495338e-01
 -2.9290933e-02 -2.6060814e-02]
Sparsity at: 0.49906085649887305
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1751 - accuracy: 0.9772 - val_loss: 0.2194 - val_accuracy: 0.9624
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.4387380e-01
 -2.3434890e-02 -2.2215407e-02]
Sparsity at: 0.49906085649887305
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1730 - accuracy: 0.9766 - val_loss: 0.2173 - val_accuracy: 0.9624
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.4289762e-01
 -2.4991723e-02 -2.3437455e-02]
Sparsity at: 0.4990646130728775
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1698 - accuracy: 0.9777 - val_loss: 0.2256 - val_accuracy: 0.9595
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.3880359e-01
 -2.8190466e-02 -2.4149274e-02]
Sparsity at: 0.4990646130728775
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1693 - accuracy: 0.9771 - val_loss: 0.2353 - val_accuracy: 0.9577
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2990518e-01
 -2.3204282e-02 -2.1257430e-02]
Sparsity at: 0.4990646130728775
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1658 - accuracy: 0.9780 - val_loss: 0.2371 - val_accuracy: 0.9563
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.3673255e-01
 -3.3271465e-02 -2.2169089e-02]
Sparsity at: 0.4990646130728775
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1600 - accuracy: 0.9791 - val_loss: 0.2395 - val_accuracy: 0.9546
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.3372675e-01
 -3.1127065e-02 -2.3714870e-02]
Sparsity at: 0.4990646130728775
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1623 - accuracy: 0.9779 - val_loss: 0.2168 - val_accuracy: 0.9614
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.3705161e-01
 -3.4036573e-02 -2.0164441e-02]
Sparsity at: 0.4990646130728775
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1633 - accuracy: 0.9774 - val_loss: 0.2121 - val_accuracy: 0.9617
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2779360e-01
 -4.0591486e-02 -1.7904308e-02]
Sparsity at: 0.4990646130728775
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1603 - accuracy: 0.9780 - val_loss: 0.2371 - val_accuracy: 0.9561
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.3419038e-01
 -3.9705627e-02 -9.8506780e-03]
Sparsity at: 0.4990646130728775
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1587 - accuracy: 0.9784 - val_loss: 0.2150 - val_accuracy: 0.9596
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2982833e-01
 -3.4766037e-02 -6.1551309e-03]
Sparsity at: 0.4990646130728775
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1567 - accuracy: 0.9781 - val_loss: 0.2189 - val_accuracy: 0.9574
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2714484e-01
 -3.4287896e-02 -6.4728386e-03]
Sparsity at: 0.4990646130728775
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1586 - accuracy: 0.9778 - val_loss: 0.2277 - val_accuracy: 0.9579
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2906639e-01
 -3.1838134e-02 -1.1366219e-02]
Sparsity at: 0.4990646130728775
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1522 - accuracy: 0.9793 - val_loss: 0.2013 - val_accuracy: 0.9639
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2826347e-01
 -3.3876583e-02 -1.3617308e-02]
Sparsity at: 0.4990646130728775
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1520 - accuracy: 0.9790 - val_loss: 0.2005 - val_accuracy: 0.9622
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2656803e-01
 -2.7728448e-02 -1.3380367e-02]
Sparsity at: 0.4990646130728775
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1482 - accuracy: 0.9798 - val_loss: 0.1959 - val_accuracy: 0.9660
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.3245885e-01
 -2.8199118e-02 -9.2595043e-03]
Sparsity at: 0.4990646130728775
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9800 - val_loss: 0.2181 - val_accuracy: 0.9579
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2289265e-01
 -2.4929702e-02 -1.1284482e-02]
Sparsity at: 0.4990646130728775
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1507 - accuracy: 0.9789 - val_loss: 0.2027 - val_accuracy: 0.9645
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2357540e-01
 -2.3656294e-02 -1.3087492e-02]
Sparsity at: 0.4990646130728775
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1494 - accuracy: 0.9789 - val_loss: 0.1919 - val_accuracy: 0.9680
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.2288350e-01
 -2.3512403e-02 -2.1896288e-02]
Sparsity at: 0.4990646130728775
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1500 - accuracy: 0.9789 - val_loss: 0.2390 - val_accuracy: 0.9515
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1849226e-01
 -1.9743333e-02 -2.1349028e-02]
Sparsity at: 0.4990646130728775
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1453 - accuracy: 0.9801 - val_loss: 0.2116 - val_accuracy: 0.9587
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1712099e-01
 -1.5877519e-02 -2.2148145e-02]
Sparsity at: 0.4990646130728775
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1447 - accuracy: 0.9799 - val_loss: 0.1853 - val_accuracy: 0.9654
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.18665911e-01
 -1.48033025e-02 -2.77230330e-02]
Sparsity at: 0.4990646130728775
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1431 - accuracy: 0.9805 - val_loss: 0.2113 - val_accuracy: 0.9597
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.16605684e-01
 -1.70457568e-02 -2.46723704e-02]
Sparsity at: 0.4990646130728775
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9805 - val_loss: 0.2439 - val_accuracy: 0.9475
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.23846635e-01
 -1.59696676e-02 -2.85557564e-02]
Sparsity at: 0.4990646130728775
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9787 - val_loss: 0.2330 - val_accuracy: 0.9529
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.12500206e-01
 -1.26188770e-02 -2.31704768e-02]
Sparsity at: 0.4990646130728775
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9793 - val_loss: 0.2169 - val_accuracy: 0.9567
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.12576224e-01
 -2.25286894e-02 -1.80566255e-02]
Sparsity at: 0.4990646130728775
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9807 - val_loss: 0.2207 - val_accuracy: 0.9550
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1326956e-01
 -2.3931531e-02 -1.9872673e-02]
Sparsity at: 0.4990646130728775
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1460 - accuracy: 0.9791 - val_loss: 0.2155 - val_accuracy: 0.9555
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1725549e-01
 -2.5225190e-02 -1.9058302e-02]
Sparsity at: 0.4990646130728775
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1424 - accuracy: 0.9798 - val_loss: 0.1890 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1045574e-01
 -3.1097116e-02 -1.6292842e-02]
Sparsity at: 0.4990646130728775
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9807 - val_loss: 0.1893 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1311470e-01
 -2.9580811e-02 -1.7479774e-02]
Sparsity at: 0.4990646130728775
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9782 - val_loss: 0.2052 - val_accuracy: 0.9623
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1114168e-01
 -2.7089834e-02 -1.0597990e-02]
Sparsity at: 0.4990646130728775
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9807 - val_loss: 0.2062 - val_accuracy: 0.9605
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1449118e-01
 -2.3293629e-02 -2.0317370e-02]
Sparsity at: 0.4990646130728775
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9805 - val_loss: 0.2024 - val_accuracy: 0.9616
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.08019374e-01
 -3.20873149e-02 -1.38234627e-02]
Sparsity at: 0.4990646130728775
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9812 - val_loss: 0.1964 - val_accuracy: 0.9658
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.1229980e-01
 -2.6377989e-02 -1.7086959e-02]
Sparsity at: 0.4990646130728775
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9803 - val_loss: 0.1986 - val_accuracy: 0.9626
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.0670014e-01
 -2.8510800e-02 -1.3014200e-02]
Sparsity at: 0.4990646130728775
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9795 - val_loss: 0.2044 - val_accuracy: 0.9618
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.06501214e-01
 -3.14009376e-02 -1.33210421e-02]
Sparsity at: 0.4990646130728775
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 1.97998486942231e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 4.1518576282012116e-10
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.02615267192720605
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.249
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 1. 0. 1. 1.]
 [0. 1. 0. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 0. 1. 1. 1. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 0. 0. 0.]
 [1. 1. 1. 1. 0. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 0. 1. 1.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 0. 1. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 0. 1.]
 [1. 1. 1. 0. 0. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 0. 1. 1. 1. 1. 0.]
 [1. 0. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.]
 [0. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 0. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 0. 1. 1. 0. 0. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 0. 0. 1.]
 [1. 0. 1. 1. 0. 1. 1. 0. 0. 1.]
 [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 218s 12ms/step - loss: 0.1410 - accuracy: 0.9798 - val_loss: 0.2031 - val_accuracy: 0.9622
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.0216445e-01
 -2.9031906e-02 -4.9266792e-03]
Sparsity at: 0.4990646130728775
Epoch 52/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1392 - accuracy: 0.9809 - val_loss: 0.2123 - val_accuracy: 0.9557
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.0533931e-01
 -3.1304531e-02 -7.8413403e-03]
Sparsity at: 0.4990646130728775
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9807 - val_loss: 0.2155 - val_accuracy: 0.9571
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.7516291e-02
 -3.0046932e-02 -6.2251054e-03]
Sparsity at: 0.4990646130728775
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9803 - val_loss: 0.1963 - val_accuracy: 0.9640
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.05405375e-01
 -3.40809226e-02 -2.76778452e-03]
Sparsity at: 0.4990646130728775
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1422 - accuracy: 0.9790 - val_loss: 0.1931 - val_accuracy: 0.9649
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.0065380e-01
 -3.1292714e-02 -2.4235400e-03]
Sparsity at: 0.4990646130728775
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9794 - val_loss: 0.1908 - val_accuracy: 0.9658
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.0336387e-01
 -3.5925906e-02 -6.2457481e-03]
Sparsity at: 0.4990646130728775
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9802 - val_loss: 0.1921 - val_accuracy: 0.9662
[ 2.43010269e-35  4.83000297e-34 -1.52481018e-34 ...  1.02969006e-01
 -2.85652969e-02 -7.58287683e-03]
Sparsity at: 0.4990646130728775
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9807 - val_loss: 0.2053 - val_accuracy: 0.9614
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.0060604e-01
 -3.2730751e-02 -1.3487593e-02]
Sparsity at: 0.4990646130728775
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9794 - val_loss: 0.1815 - val_accuracy: 0.9673
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.7383089e-02
 -3.3228386e-02 -9.5593706e-03]
Sparsity at: 0.4990646130728775
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9806 - val_loss: 0.2069 - val_accuracy: 0.9609
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.2883646e-02
 -3.8999550e-02 -6.6111474e-03]
Sparsity at: 0.4990646130728775
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9801 - val_loss: 0.1760 - val_accuracy: 0.9686
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.4028339e-02
 -4.3009229e-02 -1.1744168e-02]
Sparsity at: 0.4990646130728775
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9804 - val_loss: 0.2017 - val_accuracy: 0.9602
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.3351141e-02
 -4.0263925e-02 -5.3798617e-03]
Sparsity at: 0.4990646130728775
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9802 - val_loss: 0.2206 - val_accuracy: 0.9534
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.0260461e-02
 -3.7853736e-02 -8.3367471e-03]
Sparsity at: 0.4990646130728775
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9795 - val_loss: 0.1982 - val_accuracy: 0.9616
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.1740035e-02
 -3.6197659e-02 -3.6647578e-03]
Sparsity at: 0.4990646130728775
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9801 - val_loss: 0.2008 - val_accuracy: 0.9614
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.6211719e-02
 -3.9025985e-02 -1.2332376e-03]
Sparsity at: 0.4990646130728775
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9801 - val_loss: 0.2225 - val_accuracy: 0.9554
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.8218063e-02
 -4.3001432e-02 -5.4468317e-03]
Sparsity at: 0.4990646130728775
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9808 - val_loss: 0.1913 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.3244113e-02
 -3.1186070e-02 -1.0912329e-02]
Sparsity at: 0.4990646130728775
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9804 - val_loss: 0.2012 - val_accuracy: 0.9618
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4076338e-02
 -3.0445876e-02 -4.7609378e-03]
Sparsity at: 0.4990646130728775
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9804 - val_loss: 0.2106 - val_accuracy: 0.9605
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.9413799e-02
 -3.5725482e-02 -8.0840355e-03]
Sparsity at: 0.4990646130728775
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9807 - val_loss: 0.2005 - val_accuracy: 0.9622
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.9458026e-02
 -3.2345943e-02 -1.2858319e-02]
Sparsity at: 0.4990646130728775
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9799 - val_loss: 0.2050 - val_accuracy: 0.9610
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.8047810e-02
 -3.9924309e-02 -6.0624573e-03]
Sparsity at: 0.4990646130728775
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9801 - val_loss: 0.2183 - val_accuracy: 0.9560
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.3228020e-02
 -3.4409579e-02 -7.4734702e-03]
Sparsity at: 0.4990646130728775
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9810 - val_loss: 0.2027 - val_accuracy: 0.9603
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.7458856e-02
 -3.3984419e-02 -1.3455188e-02]
Sparsity at: 0.4990646130728775
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9808 - val_loss: 0.2012 - val_accuracy: 0.9606
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.1118835e-02
 -3.2223009e-02 -7.6602860e-03]
Sparsity at: 0.4990646130728775
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9808 - val_loss: 0.2176 - val_accuracy: 0.9574
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3347380e-02
 -3.4052953e-02 -9.2499657e-03]
Sparsity at: 0.4990646130728775
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9798 - val_loss: 0.1903 - val_accuracy: 0.9647
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.6058885e-02
 -2.9969865e-02 -9.1278758e-03]
Sparsity at: 0.4990646130728775
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9816 - val_loss: 0.1865 - val_accuracy: 0.9671
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.8588886e-02
 -3.5780240e-02 -1.1137055e-02]
Sparsity at: 0.4990646130728775
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9811 - val_loss: 0.2389 - val_accuracy: 0.9505
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.5217237e-02
 -3.6592830e-02 -8.8278512e-03]
Sparsity at: 0.4990646130728775
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9800 - val_loss: 0.1928 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3480626e-02
 -4.0244151e-02 -1.5103066e-02]
Sparsity at: 0.4990646130728775
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9821 - val_loss: 0.2009 - val_accuracy: 0.9636
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3134897e-02
 -3.5703976e-02 -1.8586395e-02]
Sparsity at: 0.4990646130728775
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9802 - val_loss: 0.1919 - val_accuracy: 0.9667
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3321884e-02
 -3.5714004e-02 -7.3275915e-03]
Sparsity at: 0.4990646130728775
Epoch 82/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9806 - val_loss: 0.1896 - val_accuracy: 0.9645
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.6707257e-02
 -3.4485251e-02 -1.1072615e-02]
Sparsity at: 0.4990646130728775
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9819 - val_loss: 0.2021 - val_accuracy: 0.9611
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.2121067e-02
 -3.8933024e-02 -1.0218536e-02]
Sparsity at: 0.4990646130728775
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9803 - val_loss: 0.1919 - val_accuracy: 0.9672
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.6136269e-02
 -3.7639730e-02 -1.0959951e-02]
Sparsity at: 0.4990646130728775
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9808 - val_loss: 0.2040 - val_accuracy: 0.9618
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.3479831e-02
 -3.4481358e-02 -1.3022921e-02]
Sparsity at: 0.4990646130728775
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9814 - val_loss: 0.2039 - val_accuracy: 0.9581
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.8424253e-02
 -3.5150114e-02 -1.1521598e-02]
Sparsity at: 0.4990646130728775
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9794 - val_loss: 0.1896 - val_accuracy: 0.9631
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.1256323e-02
 -3.1995628e-02 -4.3646451e-03]
Sparsity at: 0.4990646130728775
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9809 - val_loss: 0.2374 - val_accuracy: 0.9519
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.5590417e-02
 -3.5723213e-02 -5.5943551e-03]
Sparsity at: 0.4990646130728775
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9818 - val_loss: 0.1984 - val_accuracy: 0.9622
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.0361597e-02
 -3.9723706e-02 -1.1772426e-02]
Sparsity at: 0.4990646130728775
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9806 - val_loss: 0.2067 - val_accuracy: 0.9611
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3187737e-02
 -3.9827980e-02 -6.4904764e-03]
Sparsity at: 0.4990646130728775
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9822 - val_loss: 0.2124 - val_accuracy: 0.9560
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.5095808e-02
 -4.1745052e-02 -1.2713413e-02]
Sparsity at: 0.4990646130728775
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9811 - val_loss: 0.1837 - val_accuracy: 0.9675
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.5999938e-02
 -3.9119866e-02 -1.1606320e-03]
Sparsity at: 0.4990646130728775
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1322 - accuracy: 0.9810 - val_loss: 0.1899 - val_accuracy: 0.9639
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.1125237e-02
 -4.1048095e-02 -5.3042336e-03]
Sparsity at: 0.4990646130728775
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9801 - val_loss: 0.2146 - val_accuracy: 0.9573
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.7829845e-02
 -3.7022736e-02 -5.4009221e-03]
Sparsity at: 0.4990646130728775
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9813 - val_loss: 0.2067 - val_accuracy: 0.9589
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.6646827e-02
 -3.3785097e-02 -4.1853623e-03]
Sparsity at: 0.4990646130728775
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9814 - val_loss: 0.1961 - val_accuracy: 0.9636
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.5005025e-02
 -3.3235203e-02 -1.9651565e-03]
Sparsity at: 0.4990646130728775
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9813 - val_loss: 0.2250 - val_accuracy: 0.9552
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.2283609e-02
 -3.7922949e-02  8.1962842e-04]
Sparsity at: 0.4990646130728775
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9808 - val_loss: 0.1992 - val_accuracy: 0.9621
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.8205943e-02
 -3.7396371e-02  5.0896418e-04]
Sparsity at: 0.4990646130728775
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9816 - val_loss: 0.1965 - val_accuracy: 0.9636
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.4333303e-02
 -3.4131184e-02 -1.8500192e-03]
Sparsity at: 0.4990646130728775
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9819 - val_loss: 0.2192 - val_accuracy: 0.9581
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.7949308e-02
 -3.9483614e-02 -6.2176748e-03]
Sparsity at: 0.4990646130728775
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 3.249622759097033e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 2.7366301750201237e-05
Thresholhold -0.0021473937667906284
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.035031816048792574
Thresholhold 0.06007113307714462
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
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 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 240s 12ms/step - loss: 0.1325 - accuracy: 0.9806 - val_loss: 0.2019 - val_accuracy: 0.9616
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.4153095e-02
 -4.1713137e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 102/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1307 - accuracy: 0.9808 - val_loss: 0.1830 - val_accuracy: 0.9669
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.3752351e-02
 -4.1555680e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9808 - val_loss: 0.1909 - val_accuracy: 0.9638
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6810340e-02
 -4.4376079e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9814 - val_loss: 0.2261 - val_accuracy: 0.9527
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.7703411e-02
 -4.6923324e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9807 - val_loss: 0.1792 - val_accuracy: 0.9668
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.9385581e-02
 -4.3198194e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 106/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1280 - accuracy: 0.9816 - val_loss: 0.2078 - val_accuracy: 0.9601
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.0054114e-02
 -4.4841763e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9811 - val_loss: 0.2170 - val_accuracy: 0.9562
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2786003e-02
 -3.7705638e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9814 - val_loss: 0.1874 - val_accuracy: 0.9653
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6734172e-02
 -4.2044513e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9809 - val_loss: 0.1772 - val_accuracy: 0.9691
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.1491092e-02
 -4.1254226e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9806 - val_loss: 0.2357 - val_accuracy: 0.9521
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.8186104e-02
 -4.2629603e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9814 - val_loss: 0.1766 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.7791760e-02
 -4.5308668e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9814 - val_loss: 0.1850 - val_accuracy: 0.9660
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9970737e-02
 -4.5667335e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1285 - accuracy: 0.9806 - val_loss: 0.1753 - val_accuracy: 0.9686
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5151587e-02
 -3.8311619e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9801 - val_loss: 0.2068 - val_accuracy: 0.9596
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6308513e-02
 -4.6791196e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9806 - val_loss: 0.1877 - val_accuracy: 0.9636
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5842874e-02
 -4.6072509e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9811 - val_loss: 0.1886 - val_accuracy: 0.9648
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.3804351e-02
 -4.8296969e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9793 - val_loss: 0.1778 - val_accuracy: 0.9663
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1685767e-02
 -4.7509484e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 118/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1284 - accuracy: 0.9809 - val_loss: 0.1648 - val_accuracy: 0.9711
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5286405e-02
 -4.7037240e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9812 - val_loss: 0.1869 - val_accuracy: 0.9637
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6707738e-02
 -4.7545038e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9805 - val_loss: 0.1877 - val_accuracy: 0.9640
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2296947e-02
 -5.3387381e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9791 - val_loss: 0.1799 - val_accuracy: 0.9661
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5195166e-02
 -4.9064200e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9808 - val_loss: 0.1920 - val_accuracy: 0.9608
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.3770831e-02
 -4.4713102e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9809 - val_loss: 0.1953 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0163233e-02
 -4.8844308e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9809 - val_loss: 0.2206 - val_accuracy: 0.9575
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.4309135e-02
 -5.8023777e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9811 - val_loss: 0.1988 - val_accuracy: 0.9621
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6108122e-02
 -5.9665013e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9808 - val_loss: 0.2029 - val_accuracy: 0.9607
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.8489239e-02
 -5.7320721e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9810 - val_loss: 0.2085 - val_accuracy: 0.9590
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.8289392e-02
 -5.4693259e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9808 - val_loss: 0.2139 - val_accuracy: 0.9576
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.7348287e-02
 -5.6001794e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9797 - val_loss: 0.1702 - val_accuracy: 0.9705
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.3662440e-02
 -5.6459140e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9812 - val_loss: 0.1689 - val_accuracy: 0.9688
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6184014e-02
 -5.3563055e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 131/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1250 - accuracy: 0.9811 - val_loss: 0.1963 - val_accuracy: 0.9633
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.1934141e-02
 -5.2874990e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 132/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1280 - accuracy: 0.9807 - val_loss: 0.2099 - val_accuracy: 0.9590
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.9467440e-02
 -4.8977815e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9808 - val_loss: 0.2017 - val_accuracy: 0.9582
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.8042517e-02
 -5.3853985e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9809 - val_loss: 0.2361 - val_accuracy: 0.9497
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.5117767e-02
 -5.3059746e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9804 - val_loss: 0.1960 - val_accuracy: 0.9624
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.7062003e-02
 -5.6845471e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9814 - val_loss: 0.1792 - val_accuracy: 0.9662
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.4245311e-02
 -5.6123320e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9812 - val_loss: 0.1852 - val_accuracy: 0.9664
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.0886932e-02
 -4.5882858e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9811 - val_loss: 0.2043 - val_accuracy: 0.9603
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2373591e-02
 -5.0678208e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9805 - val_loss: 0.1819 - val_accuracy: 0.9659
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.4700601e-02
 -3.8172096e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9815 - val_loss: 0.1967 - val_accuracy: 0.9613
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.8051852e-02
 -4.1603245e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 141/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1246 - accuracy: 0.9817 - val_loss: 0.1983 - val_accuracy: 0.9618
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.4672647e-02
 -4.8387717e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 142/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1265 - accuracy: 0.9807 - val_loss: 0.1753 - val_accuracy: 0.9669
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5198623e-02
 -4.5971073e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9821 - val_loss: 0.1858 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6450365e-02
 -4.1316841e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9816 - val_loss: 0.1760 - val_accuracy: 0.9668
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6992618e-02
 -4.4316400e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9818 - val_loss: 0.1982 - val_accuracy: 0.9620
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5910071e-02
 -4.5756068e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9806 - val_loss: 0.1825 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6028252e-02
 -4.4554263e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1247 - accuracy: 0.9819 - val_loss: 0.1895 - val_accuracy: 0.9633
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.0561081e-02
 -4.9273774e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9802 - val_loss: 0.1826 - val_accuracy: 0.9649
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.1731858e-02
 -5.3298570e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 149/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1258 - accuracy: 0.9815 - val_loss: 0.1787 - val_accuracy: 0.9658
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5369494e-02
 -5.1626660e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 150/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9814 - val_loss: 0.2002 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.7033254e-02
 -5.4476336e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 4.3824882699611385e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 3.387793882952462e-05
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 ...
 [1. 1. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.04549154068983019
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
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235/235 [==============================] - 247s 12ms/step - loss: 0.1242 - accuracy: 0.9816 - val_loss: 0.1851 - val_accuracy: 0.9653
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6644713e-02
 -5.1309980e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 152/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9818 - val_loss: 0.2322 - val_accuracy: 0.9514
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5132573e-02
 -5.0654743e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9817 - val_loss: 0.1715 - val_accuracy: 0.9705
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.8993405e-02
 -4.7729790e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 154/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1249 - accuracy: 0.9815 - val_loss: 0.2057 - val_accuracy: 0.9628
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.4992269e-02
 -5.1991716e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 155/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1270 - accuracy: 0.9812 - val_loss: 0.1799 - val_accuracy: 0.9666
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2374858e-02
 -5.3462394e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9826 - val_loss: 0.1762 - val_accuracy: 0.9681
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.7823924e-02
 -5.9274200e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9810 - val_loss: 0.1749 - val_accuracy: 0.9683
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.9508411e-02
 -5.4240901e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9815 - val_loss: 0.1943 - val_accuracy: 0.9630
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.4938217e-02
 -6.1101396e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9806 - val_loss: 0.1943 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2065346e-02
 -5.3729214e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1265 - accuracy: 0.9808 - val_loss: 0.1827 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.4635597e-02
 -5.3411961e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9815 - val_loss: 0.1989 - val_accuracy: 0.9600
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2592653e-02
 -4.9779791e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9820 - val_loss: 0.1798 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.1669057e-02
 -5.0605670e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9810 - val_loss: 0.1872 - val_accuracy: 0.9641
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.1541525e-02
 -5.4424278e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9813 - val_loss: 0.1957 - val_accuracy: 0.9634
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.1156584e-02
 -5.9582531e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9811 - val_loss: 0.1934 - val_accuracy: 0.9643
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.3771819e-02
 -5.4980770e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9805 - val_loss: 0.1846 - val_accuracy: 0.9663
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.6204643e-02
 -5.4552551e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9822 - val_loss: 0.1790 - val_accuracy: 0.9674
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.7373065e-02
 -5.9153091e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9812 - val_loss: 0.1924 - val_accuracy: 0.9646
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.1450939e-02
 -5.3423770e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9820 - val_loss: 0.1854 - val_accuracy: 0.9666
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.0813281e-02
 -6.0137209e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9808 - val_loss: 0.1902 - val_accuracy: 0.9634
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.5508893e-02
 -6.0987826e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9810 - val_loss: 0.2056 - val_accuracy: 0.9597
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.6336853e-02
 -6.4013273e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1252 - accuracy: 0.9811 - val_loss: 0.1828 - val_accuracy: 0.9654
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.0794178e-02
 -5.8093440e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9804 - val_loss: 0.1878 - val_accuracy: 0.9645
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.9662994e-02
 -4.5517623e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1243 - accuracy: 0.9818 - val_loss: 0.1791 - val_accuracy: 0.9671
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.9455480e-02
 -5.2206144e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9822 - val_loss: 0.1745 - val_accuracy: 0.9673
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.6533690e-02
 -4.6288349e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 176/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1235 - accuracy: 0.9817 - val_loss: 0.1947 - val_accuracy: 0.9630
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.7102900e-02
 -5.0720576e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9820 - val_loss: 0.1854 - val_accuracy: 0.9624
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.8827068e-02
 -4.7553882e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 178/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1265 - accuracy: 0.9813 - val_loss: 0.1727 - val_accuracy: 0.9684
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.9972766e-02
 -4.9455214e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 179/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1242 - accuracy: 0.9813 - val_loss: 0.1790 - val_accuracy: 0.9662
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.1821755e-02
 -4.8202977e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9809 - val_loss: 0.1918 - val_accuracy: 0.9637
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.7524930e-02
 -4.2053681e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9804 - val_loss: 0.1849 - val_accuracy: 0.9664
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.8666814e-02
 -5.1102839e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9818 - val_loss: 0.1890 - val_accuracy: 0.9645
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3357804e-02
 -5.2880403e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9806 - val_loss: 0.1744 - val_accuracy: 0.9684
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.9391643e-02
 -5.0100256e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 184/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1263 - accuracy: 0.9808 - val_loss: 0.1780 - val_accuracy: 0.9674
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3936356e-02
 -4.7739953e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9819 - val_loss: 0.1912 - val_accuracy: 0.9634
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.8962550e-02
 -5.8271457e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9807 - val_loss: 0.1756 - val_accuracy: 0.9671
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.8321353e-02
 -5.3413030e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9809 - val_loss: 0.1800 - val_accuracy: 0.9647
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.6347321e-02
 -5.7265740e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9811 - val_loss: 0.1740 - val_accuracy: 0.9673
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.5714476e-02
 -5.2130401e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9823 - val_loss: 0.1712 - val_accuracy: 0.9690
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.9397954e-02
 -4.8189718e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9800 - val_loss: 0.1888 - val_accuracy: 0.9659
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.5601600e-02
 -4.3553635e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9804 - val_loss: 0.1843 - val_accuracy: 0.9664
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.0346867e-02
 -4.4926006e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9812 - val_loss: 0.1620 - val_accuracy: 0.9704
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.5573636e-02
 -4.3258540e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9815 - val_loss: 0.1859 - val_accuracy: 0.9663
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.9673037e-02
 -4.6526659e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9810 - val_loss: 0.1889 - val_accuracy: 0.9651
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.2042396e-02
 -5.1611707e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9811 - val_loss: 0.1869 - val_accuracy: 0.9654
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4665097e-02
 -4.5845572e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9819 - val_loss: 0.2066 - val_accuracy: 0.9602
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.9864582e-02
 -4.8335023e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9819 - val_loss: 0.1890 - val_accuracy: 0.9637
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4804125e-02
 -5.5267274e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9815 - val_loss: 0.1736 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.5101239e-02
 -5.6904275e-02 -0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9819 - val_loss: 0.1845 - val_accuracy: 0.9625
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.7628350e-02
 -5.1236529e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9815 - val_loss: 0.1910 - val_accuracy: 0.9650
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.5922398e-02
 -4.4920709e-02  0.0000000e+00]
Sparsity at: 0.5009804658151765
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 5.534202426343992e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.0019179677067498457
Thresholhold 1.3473632520799583e-07
Using suggest threshold.
Applying new mask
Percentage zeros 0.7637
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.06272514250561123
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 222s 12ms/step - loss: 0.1277 - accuracy: 0.9801 - val_loss: 0.1815 - val_accuracy: 0.9670
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3273061e-02
 -4.2444535e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 202/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1223 - accuracy: 0.9816 - val_loss: 0.1971 - val_accuracy: 0.9623
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.6281210e-02
 -4.9002454e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9806 - val_loss: 0.1761 - val_accuracy: 0.9674
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3101735e-02
 -4.5378406e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9810 - val_loss: 0.1743 - val_accuracy: 0.9676
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.2586281e-02
 -5.4883596e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9809 - val_loss: 0.1852 - val_accuracy: 0.9644
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.7525003e-02
 -5.6930233e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9814 - val_loss: 0.1591 - val_accuracy: 0.9734
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4817775e-02
 -5.3554110e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9814 - val_loss: 0.1815 - val_accuracy: 0.9685
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.5084975e-02
 -5.8694873e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9815 - val_loss: 0.1797 - val_accuracy: 0.9672
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.7991312e-02
 -5.2663766e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9821 - val_loss: 0.1699 - val_accuracy: 0.9684
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.9164637e-02
 -5.5979781e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9809 - val_loss: 0.1838 - val_accuracy: 0.9666
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.1922708e-02
 -4.8950512e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1244 - accuracy: 0.9809 - val_loss: 0.1829 - val_accuracy: 0.9647
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.2653513e-02
 -4.6645068e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9819 - val_loss: 0.1749 - val_accuracy: 0.9687
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.1962144e-02
 -4.6410728e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9808 - val_loss: 0.1766 - val_accuracy: 0.9683
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4473334e-02
 -4.5723654e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9812 - val_loss: 0.1728 - val_accuracy: 0.9687
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.0912292e-02
 -4.3305788e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9807 - val_loss: 0.2040 - val_accuracy: 0.9609
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4561668e-02
 -3.8257103e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9815 - val_loss: 0.1732 - val_accuracy: 0.9684
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.9160511e-02
 -3.7258040e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 217/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1215 - accuracy: 0.9814 - val_loss: 0.1835 - val_accuracy: 0.9674
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.3753735e-02
 -3.8720492e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9829 - val_loss: 0.1774 - val_accuracy: 0.9669
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.0109492e-02
 -3.8732864e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9821 - val_loss: 0.1865 - val_accuracy: 0.9642
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4607773e-02
 -3.8562607e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9802 - val_loss: 0.1952 - val_accuracy: 0.9608
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.2834944e-02
 -4.0912442e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9815 - val_loss: 0.1789 - val_accuracy: 0.9662
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.1630543e-02
 -4.3507129e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9824 - val_loss: 0.1982 - val_accuracy: 0.9629
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.9129167e-02
 -3.9730258e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9817 - val_loss: 0.1887 - val_accuracy: 0.9648
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4036402e-02
 -3.6696710e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9823 - val_loss: 0.1815 - val_accuracy: 0.9674
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.2216173e-02
 -3.8851839e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9808 - val_loss: 0.1748 - val_accuracy: 0.9665
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4119216e-02
 -3.5044391e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 226/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1204 - accuracy: 0.9824 - val_loss: 0.1800 - val_accuracy: 0.9658
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  9.0393059e-02
 -3.6499742e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9827 - val_loss: 0.1816 - val_accuracy: 0.9632
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.7993331e-02
 -3.4650363e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9815 - val_loss: 0.1966 - val_accuracy: 0.9601
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.4375538e-02
 -3.3407096e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9819 - val_loss: 0.1781 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  8.1816010e-02
 -3.6354475e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1241 - accuracy: 0.9808 - val_loss: 0.1786 - val_accuracy: 0.9667
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.8956388e-02
 -3.5611629e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9825 - val_loss: 0.1807 - val_accuracy: 0.9677
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.1143523e-02
 -4.1211564e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9806 - val_loss: 0.1814 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.9114134e-02
 -4.2323619e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9809 - val_loss: 0.1684 - val_accuracy: 0.9690
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.6989064e-02
 -4.7698054e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9830 - val_loss: 0.1887 - val_accuracy: 0.9646
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.8205521e-02
 -4.5620017e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9812 - val_loss: 0.1810 - val_accuracy: 0.9672
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2019987e-02
 -3.8553219e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9812 - val_loss: 0.1782 - val_accuracy: 0.9677
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2069451e-02
 -4.0509690e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9825 - val_loss: 0.1822 - val_accuracy: 0.9672
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.7631550e-02
 -4.0560920e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9813 - val_loss: 0.1769 - val_accuracy: 0.9677
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5033257e-02
 -3.8046215e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9818 - val_loss: 0.1768 - val_accuracy: 0.9667
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.0019148e-02
 -3.7541594e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9826 - val_loss: 0.1836 - val_accuracy: 0.9659
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.2931865e-02
 -4.8101734e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9817 - val_loss: 0.1881 - val_accuracy: 0.9632
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.3701555e-02
 -5.0687797e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9817 - val_loss: 0.1806 - val_accuracy: 0.9641
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.8405636e-02
 -4.4079047e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9822 - val_loss: 0.1787 - val_accuracy: 0.9677
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.3570820e-02
 -3.8991448e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 244/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1221 - accuracy: 0.9814 - val_loss: 0.2010 - val_accuracy: 0.9618
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0963273e-02
 -4.7763743e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9824 - val_loss: 0.1705 - val_accuracy: 0.9687
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1253607e-02
 -4.1996706e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9819 - val_loss: 0.1737 - val_accuracy: 0.9667
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.6949668e-02
 -3.7555564e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9816 - val_loss: 0.1926 - val_accuracy: 0.9650
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1061051e-02
 -4.5742612e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9818 - val_loss: 0.2097 - val_accuracy: 0.9572
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1773714e-02
 -5.4334398e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9815 - val_loss: 0.1748 - val_accuracy: 0.9671
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8386438e-02
 -4.4209778e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9814 - val_loss: 0.1812 - val_accuracy: 0.9667
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.2588669e-02
 -4.4599790e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.001930874255643561
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.007406638455651315
Thresholhold -3.9400154491886497e-05
Using suggest threshold.
Applying new mask
Percentage zeros 0.7637
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.07629896593094188
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 203s 12ms/step - loss: 0.1236 - accuracy: 0.9810 - val_loss: 0.1844 - val_accuracy: 0.9662
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9696715e-02
 -5.2972123e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 252/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1210 - accuracy: 0.9812 - val_loss: 0.2036 - val_accuracy: 0.9591
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1732911e-02
 -5.0197758e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9815 - val_loss: 0.1767 - val_accuracy: 0.9673
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7258092e-02
 -4.4403467e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9819 - val_loss: 0.1643 - val_accuracy: 0.9714
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9170172e-02
 -4.8469592e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9820 - val_loss: 0.1900 - val_accuracy: 0.9635
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0328159e-02
 -4.5164734e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9809 - val_loss: 0.1794 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8638662e-02
 -4.8170734e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9817 - val_loss: 0.1802 - val_accuracy: 0.9640
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.6878380e-02
 -3.7134618e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9816 - val_loss: 0.1834 - val_accuracy: 0.9652
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4294255e-02
 -3.5178769e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9823 - val_loss: 0.1957 - val_accuracy: 0.9632
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1715998e-02
 -4.1311860e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9814 - val_loss: 0.1662 - val_accuracy: 0.9690
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1148189e-02
 -4.3737374e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9814 - val_loss: 0.1940 - val_accuracy: 0.9624
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7351753e-02
 -3.8536653e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9828 - val_loss: 0.2005 - val_accuracy: 0.9606
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8126897e-02
 -3.5436902e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9812 - val_loss: 0.2097 - val_accuracy: 0.9583
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7143640e-02
 -3.8507584e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9817 - val_loss: 0.1689 - val_accuracy: 0.9692
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9633780e-02
 -4.1500472e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 265/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1208 - accuracy: 0.9818 - val_loss: 0.1971 - val_accuracy: 0.9620
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1735682e-02
 -4.7247067e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9810 - val_loss: 0.1805 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.4279199e-02
 -4.9546435e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9823 - val_loss: 0.1796 - val_accuracy: 0.9646
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0687639e-02
 -4.3423194e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9822 - val_loss: 0.1909 - val_accuracy: 0.9616
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.6595564e-02
 -5.1196244e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9819 - val_loss: 0.2077 - val_accuracy: 0.9597
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8121249e-02
 -4.9615230e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9816 - val_loss: 0.1874 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2576989e-02
 -4.9543031e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9818 - val_loss: 0.1799 - val_accuracy: 0.9643
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3376768e-02
 -4.3356668e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1244 - accuracy: 0.9808 - val_loss: 0.1846 - val_accuracy: 0.9654
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2715134e-02
 -3.1923495e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9823 - val_loss: 0.1795 - val_accuracy: 0.9669
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3808670e-02
 -3.7514828e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 274/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1234 - accuracy: 0.9806 - val_loss: 0.1878 - val_accuracy: 0.9654
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.1647700e-02
 -3.5442106e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 275/500
235/235 [==============================] - 4s 17ms/step - loss: 0.1179 - accuracy: 0.9830 - val_loss: 0.1766 - val_accuracy: 0.9664
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.1340185e-02
 -3.2402080e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9814 - val_loss: 0.1895 - val_accuracy: 0.9632
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3857207e-02
 -3.2855079e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9816 - val_loss: 0.1926 - val_accuracy: 0.9623
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7986353e-02
 -3.0507095e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9811 - val_loss: 0.1795 - val_accuracy: 0.9690
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4378774e-02
 -3.5495017e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9829 - val_loss: 0.1868 - val_accuracy: 0.9640
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.5975627e-02
 -3.9069470e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9810 - val_loss: 0.1664 - val_accuracy: 0.9700
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4068558e-02
 -4.1458447e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9821 - val_loss: 0.1705 - val_accuracy: 0.9692
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9569031e-02
 -4.7539957e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9815 - val_loss: 0.1872 - val_accuracy: 0.9663
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.6400812e-02
 -4.0377919e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9812 - val_loss: 0.1762 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3374320e-02
 -4.6579041e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9819 - val_loss: 0.1874 - val_accuracy: 0.9646
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2447822e-02
 -4.4216827e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 285/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1197 - accuracy: 0.9818 - val_loss: 0.1802 - val_accuracy: 0.9663
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4317858e-02
 -4.6591286e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9818 - val_loss: 0.1740 - val_accuracy: 0.9676
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8510337e-02
 -4.3629818e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9808 - val_loss: 0.1679 - val_accuracy: 0.9695
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3273242e-02
 -5.1111978e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9817 - val_loss: 0.2006 - val_accuracy: 0.9603
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0299508e-02
 -5.2788112e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9812 - val_loss: 0.1822 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5149784e-02
 -4.7281776e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 290/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1230 - accuracy: 0.9809 - val_loss: 0.1847 - val_accuracy: 0.9628
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5108925e-02
 -4.3764487e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 291/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1211 - accuracy: 0.9819 - val_loss: 0.1796 - val_accuracy: 0.9647
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.3365869e-02
 -4.7766022e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9816 - val_loss: 0.1756 - val_accuracy: 0.9674
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8159258e-02
 -4.6221983e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9812 - val_loss: 0.1638 - val_accuracy: 0.9704
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.6811202e-02
 -5.0119221e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9815 - val_loss: 0.1839 - val_accuracy: 0.9660
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4606996e-02
 -5.1408641e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9811 - val_loss: 0.1759 - val_accuracy: 0.9649
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3786229e-02
 -5.6371730e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9816 - val_loss: 0.1882 - val_accuracy: 0.9614
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0890051e-02
 -5.7071332e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9820 - val_loss: 0.1810 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4841630e-02
 -5.2711930e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9797 - val_loss: 0.1826 - val_accuracy: 0.9653
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.1687062e-02
 -5.6083657e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9824 - val_loss: 0.1830 - val_accuracy: 0.9649
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.5452410e-02
 -5.7725169e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 300/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1214 - accuracy: 0.9813 - val_loss: 0.1790 - val_accuracy: 0.9662
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.9231458e-02
 -5.5183873e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.009478229064779131
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.01935673618825362
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.7637
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.08640147756436711
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 173s 12ms/step - loss: 0.1206 - accuracy: 0.9815 - val_loss: 0.1774 - val_accuracy: 0.9693
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.4369943e-02
 -5.3008098e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 302/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1253 - accuracy: 0.9798 - val_loss: 0.1903 - val_accuracy: 0.9624
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.9863767e-02
 -5.7023995e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9826 - val_loss: 0.1648 - val_accuracy: 0.9690
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.6663009e-02
 -5.0374798e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9823 - val_loss: 0.1821 - val_accuracy: 0.9657
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.8397577e-02
 -5.3025898e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9811 - val_loss: 0.1841 - val_accuracy: 0.9663
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.2758457e-02
 -6.1350178e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9817 - val_loss: 0.1975 - val_accuracy: 0.9634
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.2939901e-02
 -4.9583387e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1177 - accuracy: 0.9824 - val_loss: 0.1973 - val_accuracy: 0.9621
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0117098e-02
 -5.0896283e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9809 - val_loss: 0.1710 - val_accuracy: 0.9683
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.6190865e-02
 -5.3821113e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9812 - val_loss: 0.1855 - val_accuracy: 0.9647
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.6129581e-02
 -5.9413936e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9819 - val_loss: 0.1727 - val_accuracy: 0.9681
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.7898848e-02
 -5.5294558e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9808 - val_loss: 0.1793 - val_accuracy: 0.9670
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.1917970e-02
 -5.2081071e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9815 - val_loss: 0.1725 - val_accuracy: 0.9676
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2356623e-02
 -4.6857473e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9815 - val_loss: 0.1855 - val_accuracy: 0.9653
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7389520e-02
 -5.8947045e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9814 - val_loss: 0.1710 - val_accuracy: 0.9664
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7414986e-02
 -6.1920211e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1177 - accuracy: 0.9824 - val_loss: 0.1777 - val_accuracy: 0.9641
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3125571e-02
 -5.9684813e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9811 - val_loss: 0.1736 - val_accuracy: 0.9677
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2644875e-02
 -6.8043016e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9810 - val_loss: 0.1760 - val_accuracy: 0.9672
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7034552e-02
 -6.3794054e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9813 - val_loss: 0.1651 - val_accuracy: 0.9708
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7562057e-02
 -6.3511446e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9819 - val_loss: 0.2032 - val_accuracy: 0.9602
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7024650e-02
 -6.5533839e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9812 - val_loss: 0.1730 - val_accuracy: 0.9684
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.5569761e-02
 -6.9111377e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 321/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1222 - accuracy: 0.9811 - val_loss: 0.1792 - val_accuracy: 0.9661
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3033147e-02
 -6.5309189e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9814 - val_loss: 0.1782 - val_accuracy: 0.9677
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.6667637e-02
 -6.7463025e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9810 - val_loss: 0.1809 - val_accuracy: 0.9636
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4592106e-02
 -6.7473255e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9819 - val_loss: 0.1956 - val_accuracy: 0.9606
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7963576e-02
 -7.1060248e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9813 - val_loss: 0.1725 - val_accuracy: 0.9674
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4509021e-02
 -6.5719426e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9821 - val_loss: 0.1835 - val_accuracy: 0.9644
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0640138e-02
 -6.2829845e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9810 - val_loss: 0.1862 - val_accuracy: 0.9648
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9789173e-02
 -7.3465258e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 328/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1198 - accuracy: 0.9816 - val_loss: 0.2043 - val_accuracy: 0.9592
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1750036e-02
 -7.2529994e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9801 - val_loss: 0.1798 - val_accuracy: 0.9660
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1198343e-02
 -7.6601893e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9828 - val_loss: 0.1677 - val_accuracy: 0.9696
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.5074930e-02
 -8.1219181e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9821 - val_loss: 0.1844 - val_accuracy: 0.9645
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0943883e-02
 -7.7246159e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9811 - val_loss: 0.1866 - val_accuracy: 0.9676
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0629010e-02
 -8.0631077e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 333/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1218 - accuracy: 0.9809 - val_loss: 0.1641 - val_accuracy: 0.9698
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.3222788e-02
 -8.7490901e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9821 - val_loss: 0.1858 - val_accuracy: 0.9639
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9663884e-02
 -8.7933935e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9814 - val_loss: 0.1900 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9311308e-02
 -8.1089906e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9812 - val_loss: 0.1709 - val_accuracy: 0.9699
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.5227948e-02
 -7.9613738e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1157 - accuracy: 0.9824 - val_loss: 0.1811 - val_accuracy: 0.9658
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.0469864e-02
 -7.8176275e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9816 - val_loss: 0.1700 - val_accuracy: 0.9697
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.7454822e-02
 -7.5582162e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9820 - val_loss: 0.1937 - val_accuracy: 0.9610
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.9663316e-02
 -7.2121166e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9812 - val_loss: 0.1716 - val_accuracy: 0.9693
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.0657559e-02
 -7.3629960e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9832 - val_loss: 0.1783 - val_accuracy: 0.9669
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  7.2989903e-02
 -7.3160090e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9808 - val_loss: 0.1780 - val_accuracy: 0.9671
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.7845210e-02
 -7.0872329e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9810 - val_loss: 0.1753 - val_accuracy: 0.9672
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9804909e-02
 -7.1400575e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9815 - val_loss: 0.1827 - val_accuracy: 0.9660
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8919687e-02
 -7.0807412e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9824 - val_loss: 0.1714 - val_accuracy: 0.9694
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0287867e-02
 -6.7067578e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9817 - val_loss: 0.1681 - val_accuracy: 0.9689
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.1404780e-02
 -7.3658973e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1172 - accuracy: 0.9821 - val_loss: 0.1881 - val_accuracy: 0.9641
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0033813e-02
 -7.0336565e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9810 - val_loss: 0.1736 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.2218059e-02
 -7.4841708e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9825 - val_loss: 0.1799 - val_accuracy: 0.9652
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0810562e-02
 -7.6333873e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9810 - val_loss: 0.2111 - val_accuracy: 0.9587
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9665427e-02
 -6.8433031e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.016688620131205534
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.029351832709330505
Thresholhold -9.85904989647679e-05
Using suggest threshold.
Applying new mask
Percentage zeros 0.7637
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.09073571856490581
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 165s 12ms/step - loss: 0.1207 - accuracy: 0.9818 - val_loss: 0.1627 - val_accuracy: 0.9695
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.5560101e-02
 -7.1519010e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 352/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1154 - accuracy: 0.9824 - val_loss: 0.1796 - val_accuracy: 0.9666
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0437996e-02
 -7.6312751e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9818 - val_loss: 0.1918 - val_accuracy: 0.9643
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.6559142e-02
 -8.2453147e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9815 - val_loss: 0.1681 - val_accuracy: 0.9702
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8854088e-02
 -8.4034249e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9813 - val_loss: 0.1834 - val_accuracy: 0.9653
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3068284e-02
 -7.9083376e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9811 - val_loss: 0.1882 - val_accuracy: 0.9621
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.8552554e-02
 -8.1572741e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9816 - val_loss: 0.1846 - val_accuracy: 0.9631
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0652105e-02
 -8.2072325e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9821 - val_loss: 0.1799 - val_accuracy: 0.9638
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.7726262e-02
 -8.5656025e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9812 - val_loss: 0.1737 - val_accuracy: 0.9657
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.1560655e-02
 -8.3771244e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9826 - val_loss: 0.1841 - val_accuracy: 0.9673
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.9595211e-02
 -8.4752977e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9821 - val_loss: 0.1806 - val_accuracy: 0.9675
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2828282e-02
 -7.7370092e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9805 - val_loss: 0.1903 - val_accuracy: 0.9652
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0753614e-02
 -8.3689690e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 363/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1169 - accuracy: 0.9825 - val_loss: 0.1684 - val_accuracy: 0.9700
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.5417288e-02
 -7.7832952e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9813 - val_loss: 0.1775 - val_accuracy: 0.9663
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0313890e-02
 -7.7491939e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9823 - val_loss: 0.1851 - val_accuracy: 0.9647
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0849091e-02
 -7.6174654e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9818 - val_loss: 0.1753 - val_accuracy: 0.9690
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0584696e-02
 -7.9181299e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9816 - val_loss: 0.1792 - val_accuracy: 0.9645
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.9366359e-02
 -7.7394627e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9818 - val_loss: 0.1738 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.8921525e-02
 -7.2019443e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 369/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1174 - accuracy: 0.9823 - val_loss: 0.1802 - val_accuracy: 0.9644
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.5693485e-02
 -8.0594011e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 370/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1202 - accuracy: 0.9815 - val_loss: 0.1802 - val_accuracy: 0.9657
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2301489e-02
 -7.8005575e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 371/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1201 - accuracy: 0.9818 - val_loss: 0.1915 - val_accuracy: 0.9623
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0460115e-02
 -7.9404175e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9808 - val_loss: 0.1665 - val_accuracy: 0.9716
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.1340688e-02
 -7.0210971e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9814 - val_loss: 0.1727 - val_accuracy: 0.9684
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.8372758e-02
 -7.2725527e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1172 - accuracy: 0.9823 - val_loss: 0.1859 - val_accuracy: 0.9658
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.7838617e-02
 -6.6613324e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9818 - val_loss: 0.1609 - val_accuracy: 0.9712
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.6686564e-02
 -7.4004747e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9813 - val_loss: 0.1803 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.1728692e-02
 -7.4736468e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9815 - val_loss: 0.1707 - val_accuracy: 0.9681
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.6745118e-02
 -7.9371236e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9814 - val_loss: 0.1795 - val_accuracy: 0.9646
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0736431e-02
 -7.2571523e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9819 - val_loss: 0.1759 - val_accuracy: 0.9658
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2565034e-02
 -7.6930694e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 380/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1184 - accuracy: 0.9815 - val_loss: 0.1686 - val_accuracy: 0.9669
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3262077e-02
 -7.8081153e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9822 - val_loss: 0.1748 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.1591378e-02
 -7.6572403e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9802 - val_loss: 0.1807 - val_accuracy: 0.9659
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.5055182e-02
 -7.7161364e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9816 - val_loss: 0.1801 - val_accuracy: 0.9657
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7602547e-02
 -7.3279753e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9818 - val_loss: 0.1649 - val_accuracy: 0.9690
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0775517e-02
 -6.9644541e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1168 - accuracy: 0.9824 - val_loss: 0.1779 - val_accuracy: 0.9649
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.9477376e-02
 -6.3372791e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9807 - val_loss: 0.1751 - val_accuracy: 0.9684
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0969962e-02
 -6.7346632e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9823 - val_loss: 0.1748 - val_accuracy: 0.9663
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.6709619e-02
 -5.9531592e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9817 - val_loss: 0.1756 - val_accuracy: 0.9662
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.9211018e-02
 -6.3189097e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9820 - val_loss: 0.1710 - val_accuracy: 0.9675
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0357223e-02
 -6.7651913e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 390/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1185 - accuracy: 0.9818 - val_loss: 0.1787 - val_accuracy: 0.9659
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.5126770e-02
 -7.3608994e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9804 - val_loss: 0.1715 - val_accuracy: 0.9700
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2706737e-02
 -6.9384746e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1157 - accuracy: 0.9823 - val_loss: 0.1941 - val_accuracy: 0.9648
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.2120574e-02
 -7.0982292e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9815 - val_loss: 0.2003 - val_accuracy: 0.9621
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.6779511e-02
 -6.8187371e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9817 - val_loss: 0.1746 - val_accuracy: 0.9651
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  6.0019657e-02
 -6.8839170e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9810 - val_loss: 0.1796 - val_accuracy: 0.9667
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.9837986e-02
 -6.7362763e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1159 - accuracy: 0.9827 - val_loss: 0.1857 - val_accuracy: 0.9646
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4570634e-02
 -6.3578650e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9808 - val_loss: 0.1936 - val_accuracy: 0.9615
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.3470284e-02
 -5.9525628e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9809 - val_loss: 0.1796 - val_accuracy: 0.9671
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.9755186e-02
 -6.9769964e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1156 - accuracy: 0.9824 - val_loss: 0.1966 - val_accuracy: 0.9616
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0049376e-02
 -7.4785471e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9806 - val_loss: 0.1808 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.7813335e-02
 -6.9377683e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.020554253280615553
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 1. 0. 1.]
 ...
 [1. 0. 0. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.03466311425334778
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.7637
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 0. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.09264636512113889
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.759
tf.Tensor(
[[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.]
 [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.]
 [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]
 [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 167s 12ms/step - loss: 0.1181 - accuracy: 0.9823 - val_loss: 0.1930 - val_accuracy: 0.9625
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.9732041e-02
 -6.7579523e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 402/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1168 - accuracy: 0.9826 - val_loss: 0.1881 - val_accuracy: 0.9634
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.5102511e-02
 -6.8674386e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9817 - val_loss: 0.1785 - val_accuracy: 0.9671
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.2050544e-02
 -6.6917844e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9819 - val_loss: 0.1681 - val_accuracy: 0.9704
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.2026754e-02
 -6.9111072e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9821 - val_loss: 0.1715 - val_accuracy: 0.9668
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.3105371e-02
 -6.0759831e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9822 - val_loss: 0.2088 - val_accuracy: 0.9601
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.4524731e-02
 -7.2963074e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9817 - val_loss: 0.1857 - val_accuracy: 0.9644
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  5.0975461e-02
 -6.8098776e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9814 - val_loss: 0.1783 - val_accuracy: 0.9657
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.7487956e-02
 -5.8443230e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 409/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1192 - accuracy: 0.9815 - val_loss: 0.1746 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.3682195e-02
 -7.1174122e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9815 - val_loss: 0.1910 - val_accuracy: 0.9652
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  4.5653518e-02
 -7.3513329e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9812 - val_loss: 0.1713 - val_accuracy: 0.9672
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.8367353e-02
 -7.3611766e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1139 - accuracy: 0.9827 - val_loss: 0.1893 - val_accuracy: 0.9630
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.2611836e-02
 -7.7581361e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9808 - val_loss: 0.2186 - val_accuracy: 0.9559
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.1390578e-02
 -7.6063685e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1175 - accuracy: 0.9824 - val_loss: 0.1789 - val_accuracy: 0.9646
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.8403139e-02
 -7.3021933e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 415/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1175 - accuracy: 0.9820 - val_loss: 0.1892 - val_accuracy: 0.9614
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.8050827e-02
 -7.3061198e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9810 - val_loss: 0.1779 - val_accuracy: 0.9664
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.6313739e-02
 -7.4836351e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9816 - val_loss: 0.1863 - val_accuracy: 0.9648
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.1568475e-02
 -6.4207986e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9819 - val_loss: 0.1965 - val_accuracy: 0.9600
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.5554439e-02
 -6.8918124e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9809 - val_loss: 0.1872 - val_accuracy: 0.9635
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.8984828e-02
 -6.4084627e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9824 - val_loss: 0.2099 - val_accuracy: 0.9570
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.0490417e-02
 -7.3417574e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9817 - val_loss: 0.1843 - val_accuracy: 0.9660
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.5181176e-02
 -7.2477058e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9817 - val_loss: 0.1764 - val_accuracy: 0.9668
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3996552e-02
 -7.2548978e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9818 - val_loss: 0.1717 - val_accuracy: 0.9668
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.4313317e-02
 -6.1505701e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1158 - accuracy: 0.9826 - val_loss: 0.2082 - val_accuracy: 0.9568
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.1199366e-02
 -5.9796121e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9809 - val_loss: 0.1844 - val_accuracy: 0.9653
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.4957646e-02
 -5.3530514e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1169 - accuracy: 0.9827 - val_loss: 0.1744 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.1539539e-02
 -5.5417050e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1163 - accuracy: 0.9820 - val_loss: 0.1748 - val_accuracy: 0.9665
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.2949910e-02
 -6.3194901e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9820 - val_loss: 0.1850 - val_accuracy: 0.9634
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7509313e-02
 -5.7286132e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9804 - val_loss: 0.1988 - val_accuracy: 0.9590
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7769269e-02
 -6.5292791e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 430/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1190 - accuracy: 0.9813 - val_loss: 0.1776 - val_accuracy: 0.9668
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.2578398e-02
 -6.2684059e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1159 - accuracy: 0.9825 - val_loss: 0.1700 - val_accuracy: 0.9682
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.9915622e-02
 -6.4054996e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1148 - accuracy: 0.9829 - val_loss: 0.1953 - val_accuracy: 0.9636
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7618339e-02
 -6.0588796e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1167 - accuracy: 0.9828 - val_loss: 0.1880 - val_accuracy: 0.9640
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.4674157e-02
 -6.6436954e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9813 - val_loss: 0.1848 - val_accuracy: 0.9640
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.1270795e-02
 -6.9627233e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9822 - val_loss: 0.1983 - val_accuracy: 0.9613
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.1441260e-02
 -7.4110880e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9818 - val_loss: 0.1772 - val_accuracy: 0.9666
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3663381e-02
 -7.0970483e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9814 - val_loss: 0.2047 - val_accuracy: 0.9598
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3029947e-02
 -7.4412450e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 438/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1200 - accuracy: 0.9816 - val_loss: 0.1766 - val_accuracy: 0.9651
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.1444330e-02
 -6.7232549e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9817 - val_loss: 0.1886 - val_accuracy: 0.9648
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.6186859e-02
 -6.8143860e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1177 - accuracy: 0.9823 - val_loss: 0.1949 - val_accuracy: 0.9606
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.6214305e-02
 -7.0450276e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9808 - val_loss: 0.1679 - val_accuracy: 0.9686
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.5156023e-02
 -6.3374937e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9813 - val_loss: 0.1836 - val_accuracy: 0.9664
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7264230e-02
 -6.3370183e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9825 - val_loss: 0.1743 - val_accuracy: 0.9675
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.2392951e-02
 -7.3161647e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9805 - val_loss: 0.1742 - val_accuracy: 0.9666
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.0205844e-02
 -7.5671375e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1173 - accuracy: 0.9820 - val_loss: 0.1949 - val_accuracy: 0.9628
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.6850436e-02
 -7.1346842e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9816 - val_loss: 0.1690 - val_accuracy: 0.9693
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.5708238e-02
 -7.1120195e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9810 - val_loss: 0.1873 - val_accuracy: 0.9659
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.2309197e-02
 -7.0252419e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 448/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1230 - accuracy: 0.9805 - val_loss: 0.1757 - val_accuracy: 0.9667
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.2024939e-02
 -7.0962891e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9817 - val_loss: 0.1992 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.0294169e-02
 -6.9299564e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9813 - val_loss: 0.1573 - val_accuracy: 0.9722
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.6200058e-02
 -6.6038102e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9824 - val_loss: 0.1810 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7808040e-02
 -7.8157604e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9808 - val_loss: 0.1759 - val_accuracy: 0.9669
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.9381875e-02
 -7.0881046e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9809 - val_loss: 0.1792 - val_accuracy: 0.9667
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.6443304e-02
 -6.7262635e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9812 - val_loss: 0.1880 - val_accuracy: 0.9646
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.8382227e-02
 -6.0024992e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9810 - val_loss: 0.1856 - val_accuracy: 0.9661
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.9349731e-02
 -6.7814931e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9814 - val_loss: 0.1703 - val_accuracy: 0.9686
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.6967738e-02
 -6.9297642e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9807 - val_loss: 0.1966 - val_accuracy: 0.9627
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.7382748e-02
 -6.3568488e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9807 - val_loss: 0.1696 - val_accuracy: 0.9681
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.7405368e-02
 -6.4932838e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9819 - val_loss: 0.1816 - val_accuracy: 0.9655
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.2853961e-02
 -6.1906662e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9807 - val_loss: 0.1986 - val_accuracy: 0.9629
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3836767e-02
 -7.1683630e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9813 - val_loss: 0.1737 - val_accuracy: 0.9670
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.9120512e-02
 -6.8445072e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1167 - accuracy: 0.9821 - val_loss: 0.1709 - val_accuracy: 0.9698
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3053484e-02
 -7.1202010e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9814 - val_loss: 0.1865 - val_accuracy: 0.9634
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.5805347e-02
 -6.7788966e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9814 - val_loss: 0.1711 - val_accuracy: 0.9679
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3443112e-02
 -7.1268700e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9812 - val_loss: 0.2403 - val_accuracy: 0.9518
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7160592e-02
 -7.5857066e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9819 - val_loss: 0.1743 - val_accuracy: 0.9659
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.1911489e-02
 -7.7323176e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9810 - val_loss: 0.1992 - val_accuracy: 0.9591
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.3913672e-02
 -8.1514142e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9816 - val_loss: 0.1745 - val_accuracy: 0.9680
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.2820776e-02
 -6.7961380e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9809 - val_loss: 0.2033 - val_accuracy: 0.9585
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.7386025e-02
 -6.9979891e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9822 - val_loss: 0.1931 - val_accuracy: 0.9608
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.2180028e-02
 -7.6714978e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9819 - val_loss: 0.2018 - val_accuracy: 0.9604
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.3887966e-02
 -8.0209203e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9807 - val_loss: 0.1838 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  1.9135147e-02
 -8.2527965e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 473/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1185 - accuracy: 0.9819 - val_loss: 0.1872 - val_accuracy: 0.9645
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.1278309e-02
 -8.5850790e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9811 - val_loss: 0.1793 - val_accuracy: 0.9679
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7943283e-02
 -8.6171664e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 475/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1165 - accuracy: 0.9826 - val_loss: 0.2095 - val_accuracy: 0.9557
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.5875464e-02
 -7.7409700e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9815 - val_loss: 0.1803 - val_accuracy: 0.9644
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3022750e-02
 -7.6050930e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1197 - accuracy: 0.9808 - val_loss: 0.1902 - val_accuracy: 0.9642
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.6207011e-02
 -8.4599718e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9822 - val_loss: 0.1860 - val_accuracy: 0.9630
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7955789e-02
 -7.4234828e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9807 - val_loss: 0.1732 - val_accuracy: 0.9676
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3031896e-02
 -7.4040778e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1163 - accuracy: 0.9818 - val_loss: 0.1710 - val_accuracy: 0.9653
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.9743990e-02
 -7.5337909e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9820 - val_loss: 0.1773 - val_accuracy: 0.9681
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.0275602e-02
 -7.7659838e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9816 - val_loss: 0.1776 - val_accuracy: 0.9678
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.2818519e-02
 -7.1814224e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9817 - val_loss: 0.1830 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.8309593e-02
 -7.9773158e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9799 - val_loss: 0.2179 - val_accuracy: 0.9577
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.0889010e-02
 -7.8810006e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9809 - val_loss: 0.1840 - val_accuracy: 0.9642
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.3815405e-02
 -7.7942654e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1152 - accuracy: 0.9827 - val_loss: 0.2057 - val_accuracy: 0.9595
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.8688289e-02
 -7.4251547e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9813 - val_loss: 0.1830 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.5521819e-02
 -7.9576910e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9816 - val_loss: 0.2209 - val_accuracy: 0.9552
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7744390e-02
 -8.0918208e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9807 - val_loss: 0.1745 - val_accuracy: 0.9675
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.9248513e-02
 -7.8275710e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9810 - val_loss: 0.1869 - val_accuracy: 0.9649
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.6693981e-02
 -8.6062737e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9819 - val_loss: 0.1854 - val_accuracy: 0.9661
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.5839536e-02
 -8.4945716e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1173 - accuracy: 0.9825 - val_loss: 0.1852 - val_accuracy: 0.9650
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.8811285e-02
 -8.1879854e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9817 - val_loss: 0.2073 - val_accuracy: 0.9572
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7949737e-02
 -8.4388733e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1124 - accuracy: 0.9835 - val_loss: 0.1781 - val_accuracy: 0.9654
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.3549622e-02
 -8.9119829e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9816 - val_loss: 0.2077 - val_accuracy: 0.9611
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.7956065e-02
 -7.5247832e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9816 - val_loss: 0.1688 - val_accuracy: 0.9696
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.6694072e-02
 -8.0416314e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9811 - val_loss: 0.1874 - val_accuracy: 0.9656
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.4941919e-02
 -8.7644063e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 498/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1200 - accuracy: 0.9816 - val_loss: 0.1753 - val_accuracy: 0.9673
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  2.8980058e-02
 -8.2324252e-02 -0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 499/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1169 - accuracy: 0.9820 - val_loss: 0.1923 - val_accuracy: 0.9617
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.0615116e-02
 -8.1803173e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 500/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1206 - accuracy: 0.9810 - val_loss: 0.1852 - val_accuracy: 0.9642
[ 2.4301027e-35  4.8300030e-34 -1.5248102e-34 ...  3.4869742e-02
 -8.9520596e-02  0.0000000e+00]
Sparsity at: 0.5306987227648384
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.03729514218866825
Thresholhold 0.07077749073505402
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [1. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.06108436360955238
Thresholhold 0.11014298349618912
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 0. 1.]
 [1. 1. 1. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.11433566734194756
Thresholhold 0.021502435207366943
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
  1/235 [..............................] - ETA: 4:19:51 - loss: 2.8171 - accuracy: 0.1133WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0097s vs `on_train_batch_begin` time: 10.9913s). Check your callbacks.
235/235 [==============================] - 69s 12ms/step - loss: 0.3073 - accuracy: 0.9094 - val_loss: 0.2947 - val_accuracy: 0.9498
[ 0.07077749  0.         -0.06288844 ...  0.22002321  0.15723547
 -0.08000243]
Sparsity at: 0.49854244928625097
Epoch 2/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1084 - accuracy: 0.9692 - val_loss: 0.1111 - val_accuracy: 0.9672
[ 0.07077749  0.         -0.06288844 ...  0.23815425  0.16523206
 -0.09816986]
Sparsity at: 0.49854244928625097
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0649 - accuracy: 0.9819 - val_loss: 0.0929 - val_accuracy: 0.9708
[ 0.07077749  0.         -0.06288844 ...  0.25273883  0.17163971
 -0.1132081 ]
Sparsity at: 0.49854244928625097
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0397 - accuracy: 0.9896 - val_loss: 0.0885 - val_accuracy: 0.9739
[ 0.07077749  0.         -0.06288844 ...  0.26333734  0.17927584
 -0.13006447]
Sparsity at: 0.49854244928625097
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.0831 - val_accuracy: 0.9762
[ 0.07077749  0.         -0.06288844 ...  0.27253237  0.18492188
 -0.14669353]
Sparsity at: 0.49854244928625097
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0149 - accuracy: 0.9976 - val_loss: 0.0834 - val_accuracy: 0.9761
[ 0.07077749  0.         -0.06288844 ...  0.28217748  0.19032665
 -0.16107848]
Sparsity at: 0.49854244928625097
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0088 - accuracy: 0.9987 - val_loss: 0.0834 - val_accuracy: 0.9776
[ 0.07077749  0.         -0.06288844 ...  0.28581628  0.19839114
 -0.16943207]
Sparsity at: 0.49854244928625097
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0064 - accuracy: 0.9992 - val_loss: 0.0871 - val_accuracy: 0.9772
[ 0.07077749  0.         -0.06288844 ...  0.29002464  0.20172007
 -0.17702314]
Sparsity at: 0.49854244928625097
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9994 - val_loss: 0.0853 - val_accuracy: 0.9784
[ 0.07077749  0.         -0.06288844 ...  0.29775524  0.1996231
 -0.18559293]
Sparsity at: 0.49854244928625097
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9997 - val_loss: 0.0885 - val_accuracy: 0.9785
[ 0.07077749  0.         -0.06288844 ...  0.2977176   0.20936799
 -0.19329135]
Sparsity at: 0.49854244928625097
Epoch 11/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0086 - accuracy: 0.9977 - val_loss: 0.1082 - val_accuracy: 0.9723
[ 0.07077749  0.         -0.06288844 ...  0.3031407   0.21410725
 -0.19940211]
Sparsity at: 0.49854244928625097
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0205 - accuracy: 0.9931 - val_loss: 0.1126 - val_accuracy: 0.9716
[ 0.07077749  0.         -0.06288844 ...  0.31711555  0.23267792
 -0.2142889 ]
Sparsity at: 0.49854244928625097
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0858 - val_accuracy: 0.9775
[ 0.07077749  0.         -0.06288844 ...  0.32153633  0.23518695
 -0.22723737]
Sparsity at: 0.49854244928625097
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.0945 - val_accuracy: 0.9770
[ 0.07077749  0.         -0.06288844 ...  0.3177723   0.23601383
 -0.22487037]
Sparsity at: 0.49854244928625097
Epoch 15/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.0833 - val_accuracy: 0.9796
[ 0.07077749  0.         -0.06288844 ...  0.31561244  0.24372697
 -0.23453945]
Sparsity at: 0.49854244928625097
Epoch 16/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.0820 - val_accuracy: 0.9796
[ 0.07077749  0.         -0.06288844 ...  0.32024342  0.24781373
 -0.24281093]
Sparsity at: 0.49854244928625097
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.0841 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.3199674   0.25186288
 -0.24338253]
Sparsity at: 0.49854244928625097
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4033e-04 - accuracy: 1.0000 - val_loss: 0.0784 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.32075918  0.25214246
 -0.24510679]
Sparsity at: 0.49854244928625097
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4197e-04 - accuracy: 1.0000 - val_loss: 0.0777 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.32288677  0.2528428
 -0.24660347]
Sparsity at: 0.49854244928625097
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4686e-04 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.32307908  0.25374904
 -0.24795352]
Sparsity at: 0.49854244928625097
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0902e-04 - accuracy: 1.0000 - val_loss: 0.0792 - val_accuracy: 0.9819
[ 0.07077749  0.         -0.06288844 ...  0.3245389   0.25439408
 -0.24895991]
Sparsity at: 0.49854244928625097
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7221e-04 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.32472652  0.25547686
 -0.250604  ]
Sparsity at: 0.49854244928625097
Epoch 23/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4737e-04 - accuracy: 1.0000 - val_loss: 0.0799 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.32515404  0.25620818
 -0.25184518]
Sparsity at: 0.49854244928625097
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9969 - val_loss: 0.2388 - val_accuracy: 0.9495
[ 0.07077749  0.         -0.06288844 ...  0.3087237   0.26554403
 -0.2538967 ]
Sparsity at: 0.49854244928625097
Epoch 25/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0441 - accuracy: 0.9855 - val_loss: 0.0900 - val_accuracy: 0.9765
[ 0.07077749  0.         -0.06288844 ...  0.34163398  0.2591452
 -0.24381962]
Sparsity at: 0.49854244928625097
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0098 - accuracy: 0.9968 - val_loss: 0.0821 - val_accuracy: 0.9800
[ 0.07077749  0.         -0.06288844 ...  0.34675792  0.27508634
 -0.25312257]
Sparsity at: 0.49854244928625097
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9994 - val_loss: 0.0764 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.34221265  0.2719962
 -0.25716573]
Sparsity at: 0.49854244928625097
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.0746 - val_accuracy: 0.9806
[ 0.07077749  0.         -0.06288844 ...  0.34327203  0.27452973
 -0.25982776]
Sparsity at: 0.49854244928625097
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 6.9485e-04 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.34680638  0.2764082
 -0.2621237 ]
Sparsity at: 0.49854244928625097
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1931e-04 - accuracy: 1.0000 - val_loss: 0.0739 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.34568217  0.27910382
 -0.26209444]
Sparsity at: 0.49854244928625097
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5432e-04 - accuracy: 1.0000 - val_loss: 0.0743 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.34602118  0.28032547
 -0.26345834]
Sparsity at: 0.49854244928625097
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8734e-04 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.3465471   0.28212556
 -0.26467472]
Sparsity at: 0.49854244928625097
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2779e-04 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.3471293   0.283362
 -0.26637486]
Sparsity at: 0.49854244928625097
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9578e-04 - accuracy: 1.0000 - val_loss: 0.0769 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.34866834  0.28551835
 -0.2685639 ]
Sparsity at: 0.49854244928625097
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7019e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.34896877  0.28679556
 -0.26984993]
Sparsity at: 0.49854244928625097
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6588e-04 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.34963942  0.28934515
 -0.27146277]
Sparsity at: 0.49854244928625097
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2536e-04 - accuracy: 1.0000 - val_loss: 0.0788 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.3504736   0.2914797
 -0.27249596]
Sparsity at: 0.49854244928625097
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0944e-04 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.35088098  0.2928936
 -0.27379957]
Sparsity at: 0.49854244928625097
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0990 - val_accuracy: 0.9795
[ 0.07077749  0.         -0.06288844 ...  0.35099474  0.31612152
 -0.275935  ]
Sparsity at: 0.49854244928625097
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0429 - accuracy: 0.9863 - val_loss: 0.1130 - val_accuracy: 0.9731
[ 0.07077749  0.         -0.06288844 ...  0.3463997   0.2818052
 -0.26495454]
Sparsity at: 0.49854244928625097
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0117 - accuracy: 0.9962 - val_loss: 0.0799 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.3494644   0.29963967
 -0.27299172]
Sparsity at: 0.49854244928625097
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0031 - accuracy: 0.9993 - val_loss: 0.0777 - val_accuracy: 0.9819
[ 0.07077749  0.         -0.06288844 ...  0.35379577  0.29911366
 -0.28268498]
Sparsity at: 0.49854244928625097
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.0755 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.35728338  0.3025636
 -0.2842815 ]
Sparsity at: 0.49854244928625097
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1508e-04 - accuracy: 1.0000 - val_loss: 0.0769 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.35861632  0.30346766
 -0.2841287 ]
Sparsity at: 0.49854244928625097
Epoch 45/500
235/235 [==============================] - 3s 13ms/step - loss: 3.8774e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.35919982  0.30602425
 -0.28446555]
Sparsity at: 0.49854244928625097
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1538e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.36036348  0.30720234
 -0.28495345]
Sparsity at: 0.49854244928625097
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4510e-04 - accuracy: 1.0000 - val_loss: 0.0789 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.3620657   0.3077274
 -0.286137  ]
Sparsity at: 0.49854244928625097
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1964e-04 - accuracy: 1.0000 - val_loss: 0.0791 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.36227435  0.3085687
 -0.28639978]
Sparsity at: 0.49854244928625097
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9234e-04 - accuracy: 1.0000 - val_loss: 0.0799 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.364083    0.30841202
 -0.2875514 ]
Sparsity at: 0.49854244928625097
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5624e-04 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.3660537   0.30860677
 -0.28848886]
Sparsity at: 0.49854244928625097
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.04219267063914156
Thresholhold 0.07077749073505402
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.05782828008594976
Thresholhold 0.17557062208652496
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 0. 1. 0.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.30201215466478715
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 210s 12ms/step - loss: 3.4459e-04 - accuracy: 1.0000 - val_loss: 0.0844 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.36873147  0.31173706
 -0.2902761 ]
Sparsity at: 0.6438542449286251
Epoch 52/500
235/235 [==============================] - 3s 12ms/step - loss: 2.6257e-04 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.36943266  0.3147518
 -0.29183933]
Sparsity at: 0.6438542449286251
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8323e-04 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.37166137  0.31441537
 -0.2936888 ]
Sparsity at: 0.6438542449286251
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2806e-04 - accuracy: 1.0000 - val_loss: 0.0840 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.37296653  0.31513628
 -0.2944754 ]
Sparsity at: 0.6438542449286251
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1173e-04 - accuracy: 1.0000 - val_loss: 0.0854 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.37383756  0.31587008
 -0.29723474]
Sparsity at: 0.6438542449286251
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 9.1000e-05 - accuracy: 1.0000 - val_loss: 0.0865 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.37671083  0.31654465
 -0.29745802]
Sparsity at: 0.6438542449286251
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0113 - accuracy: 0.9962 - val_loss: 0.1567 - val_accuracy: 0.9703
[ 0.07077749  0.         -0.06288844 ...  0.37474856  0.33121437
 -0.30526078]
Sparsity at: 0.6438542449286251
Epoch 58/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0081 - accuracy: 0.9974 - val_loss: 0.0972 - val_accuracy: 0.9790
[ 0.07077749  0.         -0.06288844 ...  0.37586954  0.34598792
 -0.29147017]
Sparsity at: 0.6438542449286251
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0901 - val_accuracy: 0.9802
[ 0.07077749  0.         -0.06288844 ...  0.37612247  0.34635046
 -0.2971241 ]
Sparsity at: 0.6438542449286251
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5932e-04 - accuracy: 1.0000 - val_loss: 0.0852 - val_accuracy: 0.9819
[ 0.07077749  0.         -0.06288844 ...  0.3768259   0.34961793
 -0.301167  ]
Sparsity at: 0.6438542449286251
Epoch 61/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5596e-04 - accuracy: 1.0000 - val_loss: 0.0859 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.37654376  0.35318434
 -0.29934832]
Sparsity at: 0.6438542449286251
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9375e-04 - accuracy: 1.0000 - val_loss: 0.0830 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.37706822  0.3546309
 -0.29935822]
Sparsity at: 0.6438542449286251
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1210e-04 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.37555593  0.35534126
 -0.29644278]
Sparsity at: 0.6438542449286251
Epoch 64/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5687e-04 - accuracy: 1.0000 - val_loss: 0.0846 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.37594578  0.35759154
 -0.3014578 ]
Sparsity at: 0.6438542449286251
Epoch 65/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1539e-04 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.37640432  0.35910308
 -0.30048954]
Sparsity at: 0.6438542449286251
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2288e-04 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.3771613   0.36070925
 -0.30131897]
Sparsity at: 0.6438542449286251
Epoch 67/500
235/235 [==============================] - 3s 13ms/step - loss: 8.8050e-05 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.37843913  0.361947
 -0.30179012]
Sparsity at: 0.6438542449286251
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8004e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.37876475  0.36362126
 -0.30456167]
Sparsity at: 0.6438542449286251
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5430e-05 - accuracy: 1.0000 - val_loss: 0.0869 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.37916338  0.36370558
 -0.3050292 ]
Sparsity at: 0.6438542449286251
Epoch 70/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8475e-05 - accuracy: 1.0000 - val_loss: 0.0866 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.3807698   0.36578977
 -0.3066363 ]
Sparsity at: 0.6438542449286251
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3140e-05 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.3825623   0.366857
 -0.30782598]
Sparsity at: 0.6438542449286251
Epoch 72/500
235/235 [==============================] - 3s 13ms/step - loss: 4.0535e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.3832957   0.36860645
 -0.3069975 ]
Sparsity at: 0.6438542449286251
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0946e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.3843137   0.3691921
 -0.306836  ]
Sparsity at: 0.6438542449286251
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4763e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.38429472  0.37089497
 -0.30856842]
Sparsity at: 0.6438542449286251
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9862e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.38466182  0.37157458
 -0.3099805 ]
Sparsity at: 0.6438542449286251
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8850e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.38536263  0.37737125
 -0.3085505 ]
Sparsity at: 0.6438542449286251
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0035 - accuracy: 0.9990 - val_loss: 0.1811 - val_accuracy: 0.9706
[ 0.07077749  0.         -0.06288844 ...  0.37676919  0.3963469
 -0.32958907]
Sparsity at: 0.6438542449286251
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0158 - accuracy: 0.9948 - val_loss: 0.1082 - val_accuracy: 0.9792
[ 0.07077749  0.         -0.06288844 ...  0.3760096   0.34293857
 -0.29264963]
Sparsity at: 0.6438542449286251
Epoch 79/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 0.0939 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.37961665  0.3532812
 -0.3148994 ]
Sparsity at: 0.6438542449286251
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3680e-04 - accuracy: 0.9998 - val_loss: 0.0907 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.37760004  0.3614354
 -0.31973007]
Sparsity at: 0.6438542449286251
Epoch 81/500
235/235 [==============================] - 4s 15ms/step - loss: 3.8103e-04 - accuracy: 0.9999 - val_loss: 0.0897 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.37874147  0.35829222
 -0.32277417]
Sparsity at: 0.6438542449286251
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9900e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.37799653  0.3586325
 -0.32455206]
Sparsity at: 0.6438542449286251
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3243e-04 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.37893268  0.36066502
 -0.3251246 ]
Sparsity at: 0.6438542449286251
Epoch 84/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3508e-04 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.3793772   0.36284816
 -0.3276236 ]
Sparsity at: 0.6438542449286251
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0113e-04 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.37936458  0.36402363
 -0.32850617]
Sparsity at: 0.6438542449286251
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2303e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.37987393  0.36451986
 -0.3287336 ]
Sparsity at: 0.6438542449286251
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 7.0885e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.38061684  0.36586884
 -0.3296575 ]
Sparsity at: 0.6438542449286251
Epoch 88/500
235/235 [==============================] - 3s 13ms/step - loss: 5.5893e-05 - accuracy: 1.0000 - val_loss: 0.0903 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.3814249   0.36651826
 -0.33042783]
Sparsity at: 0.6438542449286251
Epoch 89/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3539e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.38215655  0.3670291
 -0.3317808 ]
Sparsity at: 0.6438542449286251
Epoch 90/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9458e-05 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.3821436   0.3688642
 -0.33242083]
Sparsity at: 0.6438542449286251
Epoch 91/500
235/235 [==============================] - 3s 13ms/step - loss: 4.1969e-05 - accuracy: 1.0000 - val_loss: 0.0914 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.3823596   0.37121758
 -0.3345406 ]
Sparsity at: 0.6438542449286251
Epoch 92/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3222e-05 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.38323575  0.37313348
 -0.33129802]
Sparsity at: 0.6438542449286251
Epoch 93/500
235/235 [==============================] - 3s 13ms/step - loss: 3.8179e-05 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.38393655  0.37499595
 -0.3320159 ]
Sparsity at: 0.6438542449286251
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6538e-05 - accuracy: 1.0000 - val_loss: 0.0914 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.38550237  0.3766713
 -0.33237547]
Sparsity at: 0.6438542449286251
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0168e-05 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.38568348  0.37855065
 -0.33460796]
Sparsity at: 0.6438542449286251
Epoch 96/500
235/235 [==============================] - 4s 15ms/step - loss: 5.8844e-05 - accuracy: 1.0000 - val_loss: 0.0922 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.39347598  0.36659467
 -0.33213443]
Sparsity at: 0.6438542449286251
Epoch 97/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 0.1802 - val_accuracy: 0.9688
[ 0.07077749  0.         -0.06288844 ...  0.38148496  0.36149156
 -0.29069728]
Sparsity at: 0.6438542449286251
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0135 - accuracy: 0.9953 - val_loss: 0.1268 - val_accuracy: 0.9786
[ 0.07077749  0.         -0.06288844 ...  0.36560258  0.38159183
 -0.26631984]
Sparsity at: 0.6438542449286251
Epoch 99/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0031 - accuracy: 0.9989 - val_loss: 0.1021 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.37644556  0.3838251
 -0.29156643]
Sparsity at: 0.6438542449286251
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0051e-04 - accuracy: 0.9999 - val_loss: 0.0980 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.38106593  0.38486505
 -0.29515108]
Sparsity at: 0.6438542449286251
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.06722307808257177
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.10754248362987884
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 0. 1. 0.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.41623584383769696
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 213s 12ms/step - loss: 2.6341e-04 - accuracy: 0.9999 - val_loss: 0.0969 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.38368174  0.38197154
 -0.29806238]
Sparsity at: 0.6438542449286251
Epoch 102/500
235/235 [==============================] - 3s 12ms/step - loss: 1.7092e-04 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.3835571   0.38161218
 -0.29628724]
Sparsity at: 0.6438542449286251
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1827e-04 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.3831986   0.38107622
 -0.29640132]
Sparsity at: 0.6438542449286251
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 9.1059e-05 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.38284224  0.38102615
 -0.29576236]
Sparsity at: 0.6438542449286251
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 8.4946e-05 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.38283852  0.38415575
 -0.29781446]
Sparsity at: 0.6438542449286251
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 6.8894e-05 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.38297352  0.38233295
 -0.29858777]
Sparsity at: 0.6438542449286251
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9435e-04 - accuracy: 0.9999 - val_loss: 0.0964 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.3821489   0.39843214
 -0.29721424]
Sparsity at: 0.6438542449286251
Epoch 108/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3526e-04 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.38278812  0.39267573
 -0.2981504 ]
Sparsity at: 0.6438542449286251
Epoch 109/500
235/235 [==============================] - 3s 13ms/step - loss: 5.5648e-05 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.38365418  0.39176607
 -0.29858717]
Sparsity at: 0.6438542449286251
Epoch 110/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4113e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.38392642  0.39197212
 -0.29836076]
Sparsity at: 0.6438542449286251
Epoch 111/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6855e-04 - accuracy: 0.9999 - val_loss: 0.0997 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.39778674  0.39196154
 -0.29647118]
Sparsity at: 0.6438542449286251
Epoch 112/500
235/235 [==============================] - 3s 13ms/step - loss: 7.0023e-05 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.3989083   0.3929235
 -0.295818  ]
Sparsity at: 0.6438542449286251
Epoch 113/500
235/235 [==============================] - 3s 13ms/step - loss: 9.2178e-05 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.3986461   0.39465642
 -0.3009746 ]
Sparsity at: 0.6438542449286251
Epoch 114/500
235/235 [==============================] - 3s 15ms/step - loss: 5.3501e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.398333    0.39282727
 -0.30284297]
Sparsity at: 0.6438542449286251
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4629e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.39877027  0.39529106
 -0.30762848]
Sparsity at: 0.6438542449286251
Epoch 116/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9848e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.39892462  0.39484367
 -0.30829585]
Sparsity at: 0.6438542449286251
Epoch 117/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7075e-04 - accuracy: 0.9999 - val_loss: 0.1215 - val_accuracy: 0.9804
[ 0.07077749  0.         -0.06288844 ...  0.3930084   0.40372404
 -0.28723773]
Sparsity at: 0.6438542449286251
Epoch 118/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0101 - accuracy: 0.9967 - val_loss: 0.1163 - val_accuracy: 0.9787
[ 0.07077749  0.         -0.06288844 ...  0.4539184   0.3638996
 -0.2888744 ]
Sparsity at: 0.6438542449286251
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.1085 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.450893    0.36389714
 -0.29295906]
Sparsity at: 0.6438542449286251
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3369e-04 - accuracy: 0.9998 - val_loss: 0.1027 - val_accuracy: 0.9819
[ 0.07077749  0.         -0.06288844 ...  0.44421515  0.36622354
 -0.2998491 ]
Sparsity at: 0.6438542449286251
Epoch 121/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5375e-04 - accuracy: 1.0000 - val_loss: 0.1011 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.44285455  0.365546
 -0.29730177]
Sparsity at: 0.6438542449286251
Epoch 122/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7793e-04 - accuracy: 1.0000 - val_loss: 0.1017 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.44159913  0.36866677
 -0.2972661 ]
Sparsity at: 0.6438542449286251
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0178e-04 - accuracy: 0.9999 - val_loss: 0.1001 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.44175816  0.3733503
 -0.29826277]
Sparsity at: 0.6438542449286251
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2276e-05 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.43919426  0.37602684
 -0.30002812]
Sparsity at: 0.6438542449286251
Epoch 125/500
235/235 [==============================] - 3s 13ms/step - loss: 5.7360e-05 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.44050744  0.377806
 -0.30102202]
Sparsity at: 0.6438542449286251
Epoch 126/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7491e-05 - accuracy: 1.0000 - val_loss: 0.0993 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.44049364  0.37859654
 -0.30171373]
Sparsity at: 0.6438542449286251
Epoch 127/500
235/235 [==============================] - 3s 15ms/step - loss: 4.6374e-05 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.4422707   0.37919235
 -0.30189982]
Sparsity at: 0.6438542449286251
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5321e-05 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.44147173  0.3815526
 -0.30184686]
Sparsity at: 0.6438542449286251
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4579e-05 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.4423621   0.3813606
 -0.30428016]
Sparsity at: 0.6438542449286251
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6901e-05 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.44255784  0.38156134
 -0.30440918]
Sparsity at: 0.6438542449286251
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4295e-05 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.44270852  0.38216856
 -0.30469728]
Sparsity at: 0.6438542449286251
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2700e-05 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.44351453  0.38172266
 -0.30575353]
Sparsity at: 0.6438542449286251
Epoch 133/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1284e-05 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.44396913  0.3833651
 -0.30564898]
Sparsity at: 0.6438542449286251
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6412e-05 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.44347018  0.38505036
 -0.3071321 ]
Sparsity at: 0.6438542449286251
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1978e-05 - accuracy: 1.0000 - val_loss: 0.0999 - val_accuracy: 0.9844
[ 0.07077749  0.         -0.06288844 ...  0.44429037  0.38456014
 -0.30760732]
Sparsity at: 0.6438542449286251
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 9.4125e-04 - accuracy: 0.9998 - val_loss: 0.1464 - val_accuracy: 0.9758
[ 0.07077749  0.         -0.06288844 ...  0.44508618  0.4040645
 -0.32586768]
Sparsity at: 0.6438542449286251
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0094 - accuracy: 0.9970 - val_loss: 0.1246 - val_accuracy: 0.9795
[ 0.07077749  0.         -0.06288844 ...  0.44897044  0.3873712
 -0.2606492 ]
Sparsity at: 0.6438542449286251
Epoch 138/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1100 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.44041184  0.3849763
 -0.26310948]
Sparsity at: 0.6438542449286251
Epoch 139/500
235/235 [==============================] - 3s 13ms/step - loss: 4.8657e-04 - accuracy: 0.9999 - val_loss: 0.1045 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.44067708  0.38881075
 -0.2678539 ]
Sparsity at: 0.6438542449286251
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0787e-04 - accuracy: 0.9999 - val_loss: 0.1061 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.43974125  0.3931303
 -0.26572415]
Sparsity at: 0.6438542449286251
Epoch 141/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0663e-04 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.4349618   0.3942723
 -0.26509982]
Sparsity at: 0.6438542449286251
Epoch 142/500
235/235 [==============================] - 3s 13ms/step - loss: 7.3384e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.43337438  0.3943986
 -0.2653886 ]
Sparsity at: 0.6438542449286251
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9849e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.4332794   0.39579764
 -0.26680556]
Sparsity at: 0.6438542449286251
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1499e-05 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.43298098  0.3946838
 -0.26693466]
Sparsity at: 0.6438542449286251
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4263e-05 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.4337667   0.39623588
 -0.26789865]
Sparsity at: 0.6438542449286251
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8312e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.43387002  0.39568287
 -0.2681821 ]
Sparsity at: 0.6438542449286251
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9418e-05 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.43348068  0.39641082
 -0.26838875]
Sparsity at: 0.6438542449286251
Epoch 148/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6631e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.4335408   0.39738753
 -0.2704769 ]
Sparsity at: 0.6438542449286251
Epoch 149/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9353e-05 - accuracy: 1.0000 - val_loss: 0.1046 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.4338756   0.40141135
 -0.2711548 ]
Sparsity at: 0.6438542449286251
Epoch 150/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2505e-05 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.43313697  0.4018448
 -0.27027997]
Sparsity at: 0.6438542449286251
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.12977274198082078
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.17684797241598638
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 0. 1. 0.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.5063692906655213
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 192s 12ms/step - loss: 1.9711e-05 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.4325562   0.4013169
 -0.26893592]
Sparsity at: 0.6438542449286251
Epoch 152/500
235/235 [==============================] - 3s 12ms/step - loss: 1.8089e-05 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.43177983  0.40173927
 -0.26841098]
Sparsity at: 0.6438542449286251
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5922e-05 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.43163145  0.4020789
 -0.26981875]
Sparsity at: 0.6438542449286251
Epoch 154/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2846e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.43198344  0.40297526
 -0.27056345]
Sparsity at: 0.6438542449286251
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3548e-05 - accuracy: 1.0000 - val_loss: 0.1041 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.43220082  0.40511188
 -0.27333638]
Sparsity at: 0.6438542449286251
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5913e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.42471343  0.41534978
 -0.27358025]
Sparsity at: 0.6438542449286251
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3397e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.42897046  0.41030964
 -0.27387643]
Sparsity at: 0.6438542449286251
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0133e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.42896685  0.4109889
 -0.27502912]
Sparsity at: 0.6438542449286251
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 9.2323e-06 - accuracy: 1.0000 - val_loss: 0.1046 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.42870665  0.41161972
 -0.2753282 ]
Sparsity at: 0.6438542449286251
Epoch 160/500
235/235 [==============================] - 3s 13ms/step - loss: 9.0621e-06 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.42918026  0.41240942
 -0.27772447]
Sparsity at: 0.6438542449286251
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3317e-05 - accuracy: 1.0000 - val_loss: 0.1039 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.42979515  0.41485494
 -0.29043663]
Sparsity at: 0.6438542449286251
Epoch 162/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0100 - accuracy: 0.9969 - val_loss: 0.1270 - val_accuracy: 0.9804
[ 0.07077749  0.         -0.06288844 ...  0.40569285  0.41812915
 -0.28165516]
Sparsity at: 0.6438542449286251
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.1043 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.40988055  0.39671564
 -0.27321884]
Sparsity at: 0.6438542449286251
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 8.9830e-04 - accuracy: 0.9998 - val_loss: 0.1013 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.41244492  0.40490937
 -0.27132887]
Sparsity at: 0.6438542449286251
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2412e-04 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.41109297  0.4161535
 -0.27922297]
Sparsity at: 0.6438542449286251
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6101e-04 - accuracy: 0.9999 - val_loss: 0.0995 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.41024056  0.4187012
 -0.2800366 ]
Sparsity at: 0.6438542449286251
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2589e-04 - accuracy: 1.0000 - val_loss: 0.1010 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.4107881   0.41571656
 -0.2822485 ]
Sparsity at: 0.6438542449286251
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 5.7613e-05 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.41041222  0.4169576
 -0.28283638]
Sparsity at: 0.6438542449286251
Epoch 169/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7240e-05 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.41109186  0.41822127
 -0.28227037]
Sparsity at: 0.6438542449286251
Epoch 170/500
235/235 [==============================] - 3s 13ms/step - loss: 4.0423e-05 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.411146    0.41837743
 -0.28253308]
Sparsity at: 0.6438542449286251
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0414e-04 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.40813342  0.41910687
 -0.28150648]
Sparsity at: 0.6438542449286251
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5365e-05 - accuracy: 1.0000 - val_loss: 0.1011 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.40879095  0.420546
 -0.28306764]
Sparsity at: 0.6438542449286251
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1052e-05 - accuracy: 1.0000 - val_loss: 0.1013 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.40963072  0.42073756
 -0.28363436]
Sparsity at: 0.6438542449286251
Epoch 174/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1077e-05 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.41035414  0.4203238
 -0.28594634]
Sparsity at: 0.6438542449286251
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2299e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.41620246  0.41979864
 -0.28628877]
Sparsity at: 0.6438542449286251
Epoch 176/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.1306 - val_accuracy: 0.9788
[ 0.07077749  0.         -0.06288844 ...  0.4287261   0.43044674
 -0.28454828]
Sparsity at: 0.6438542449286251
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0034 - accuracy: 0.9989 - val_loss: 0.1208 - val_accuracy: 0.9793
[ 0.07077749  0.         -0.06288844 ...  0.41354305  0.44962642
 -0.27653372]
Sparsity at: 0.6438542449286251
Epoch 178/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1137 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.4202877   0.43841287
 -0.30516267]
Sparsity at: 0.6438542449286251
Epoch 179/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1182 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.41722384  0.44853187
 -0.31679225]
Sparsity at: 0.6438542449286251
Epoch 180/500
235/235 [==============================] - 3s 13ms/step - loss: 5.1913e-04 - accuracy: 0.9999 - val_loss: 0.1046 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.41334632  0.44889298
 -0.31659558]
Sparsity at: 0.6438542449286251
Epoch 181/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2412e-04 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9843
[ 0.07077749  0.         -0.06288844 ...  0.41383645  0.45054632
 -0.31400543]
Sparsity at: 0.6438542449286251
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2660e-05 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9840
[ 0.07077749  0.         -0.06288844 ...  0.41461122  0.4525701
 -0.31303558]
Sparsity at: 0.6438542449286251
Epoch 183/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7119e-05 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.4145497   0.4541315
 -0.3129061 ]
Sparsity at: 0.6438542449286251
Epoch 184/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9877e-05 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.41420752  0.4545273
 -0.3133016 ]
Sparsity at: 0.6438542449286251
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1548e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.4148391   0.45519605
 -0.31269017]
Sparsity at: 0.6438542449286251
Epoch 186/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0919e-05 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9843
[ 0.07077749  0.         -0.06288844 ...  0.41411188  0.45500895
 -0.31382322]
Sparsity at: 0.6438542449286251
Epoch 187/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0594e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9842
[ 0.07077749  0.         -0.06288844 ...  0.4154502   0.45551774
 -0.3153795 ]
Sparsity at: 0.6438542449286251
Epoch 188/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8583e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9842
[ 0.07077749  0.         -0.06288844 ...  0.4158527   0.45643076
 -0.31650525]
Sparsity at: 0.6438542449286251
Epoch 189/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3416e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9840
[ 0.07077749  0.         -0.06288844 ...  0.41475824  0.45856366
 -0.3165119 ]
Sparsity at: 0.6438542449286251
Epoch 190/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6340e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.414708    0.4591982
 -0.31596485]
Sparsity at: 0.6438542449286251
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3018e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9840
[ 0.07077749  0.         -0.06288844 ...  0.41418225  0.46035087
 -0.31619483]
Sparsity at: 0.6438542449286251
Epoch 192/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3030e-05 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9841
[ 0.07077749  0.         -0.06288844 ...  0.41385266  0.46083876
 -0.31634712]
Sparsity at: 0.6438542449286251
Epoch 193/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1425 - val_accuracy: 0.9805
[ 0.07077749  0.         -0.06288844 ...  0.4148868   0.50099397
 -0.3484055 ]
Sparsity at: 0.6438542449286251
Epoch 194/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0053 - accuracy: 0.9982 - val_loss: 0.1363 - val_accuracy: 0.9787
[ 0.07077749  0.         -0.06288844 ...  0.3772815   0.48598397
 -0.31020325]
Sparsity at: 0.6438542449286251
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9990 - val_loss: 0.1171 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.3883453   0.4851859
 -0.29018384]
Sparsity at: 0.6438542449286251
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2102e-04 - accuracy: 0.9998 - val_loss: 0.1097 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.38811877  0.48641938
 -0.27979183]
Sparsity at: 0.6438542449286251
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9483e-04 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.3876079   0.4885252
 -0.27400103]
Sparsity at: 0.6438542449286251
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5846e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.38931623  0.49022585
 -0.2766188 ]
Sparsity at: 0.6438542449286251
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5873e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.39025378  0.49247432
 -0.2790901 ]
Sparsity at: 0.6438542449286251
Epoch 200/500
235/235 [==============================] - 3s 15ms/step - loss: 3.6910e-05 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.39047423  0.49197137
 -0.2815152 ]
Sparsity at: 0.6438542449286251
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.2130291442727632
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.2545684616607602
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 0. 1. 0.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.5939307740207767
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 200s 12ms/step - loss: 2.9212e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.3904692   0.49139494
 -0.28238073]
Sparsity at: 0.6438542449286251
Epoch 202/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4446e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.39039412  0.49206096
 -0.28326333]
Sparsity at: 0.6438542449286251
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5199e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.39043218  0.49232802
 -0.28503856]
Sparsity at: 0.6438542449286251
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7138e-05 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.39009878  0.49146026
 -0.2855671 ]
Sparsity at: 0.6438542449286251
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1139e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9841
[ 0.07077749  0.         -0.06288844 ...  0.3908654   0.49123633
 -0.2874075 ]
Sparsity at: 0.6438542449286251
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3712e-05 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.39103943  0.49085248
 -0.28703398]
Sparsity at: 0.6438542449286251
Epoch 207/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8747e-05 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.39125374  0.49175456
 -0.28853452]
Sparsity at: 0.6438542449286251
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0475e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.39157817  0.49462217
 -0.2875069 ]
Sparsity at: 0.6438542449286251
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4511e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.3915045   0.4949844
 -0.28831527]
Sparsity at: 0.6438542449286251
Epoch 210/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2441e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.39154157  0.49554268
 -0.2888433 ]
Sparsity at: 0.6438542449286251
Epoch 211/500
235/235 [==============================] - 3s 13ms/step - loss: 7.9455e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.40074992  0.49533212
 -0.29753006]
Sparsity at: 0.6438542449286251
Epoch 212/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.1548 - val_accuracy: 0.9769
[ 0.07077749  0.         -0.06288844 ...  0.35701442  0.49861482
 -0.24383967]
Sparsity at: 0.6438542449286251
Epoch 213/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.1330 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.37795773  0.48154846
 -0.26194903]
Sparsity at: 0.6438542449286251
Epoch 214/500
235/235 [==============================] - 3s 13ms/step - loss: 8.7761e-04 - accuracy: 0.9998 - val_loss: 0.1193 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.3810571   0.4854263
 -0.25863057]
Sparsity at: 0.6438542449286251
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5911e-04 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.38132456  0.4853115
 -0.2627085 ]
Sparsity at: 0.6438542449286251
Epoch 216/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0843e-04 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9840
[ 0.07077749  0.         -0.06288844 ...  0.37695992  0.4856609
 -0.2559537 ]
Sparsity at: 0.6438542449286251
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4507e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.37550426  0.48566157
 -0.25451946]
Sparsity at: 0.6438542449286251
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7194e-05 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9840
[ 0.07077749  0.         -0.06288844 ...  0.37568796  0.48612362
 -0.2563477 ]
Sparsity at: 0.6438542449286251
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7080e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.37734884  0.48647696
 -0.2569758 ]
Sparsity at: 0.6438542449286251
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3274e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.376986    0.48719323
 -0.2579941 ]
Sparsity at: 0.6438542449286251
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3128e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.37665427  0.48761666
 -0.2581275 ]
Sparsity at: 0.6438542449286251
Epoch 222/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8618e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.37653592  0.48648232
 -0.25801423]
Sparsity at: 0.6438542449286251
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4661e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.37647626  0.486854
 -0.25826105]
Sparsity at: 0.6438542449286251
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2345e-05 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.3750439   0.48698926
 -0.25628045]
Sparsity at: 0.6438542449286251
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 5.7811e-04 - accuracy: 0.9999 - val_loss: 0.1197 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.3748217   0.4890028
 -0.25552976]
Sparsity at: 0.6438542449286251
Epoch 226/500
235/235 [==============================] - 4s 16ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.1434 - val_accuracy: 0.9778
[ 0.07077749  0.         -0.06288844 ...  0.38103834  0.49142006
 -0.26042166]
Sparsity at: 0.6438542449286251
Epoch 227/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0021 - accuracy: 0.9992 - val_loss: 0.1317 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.3849364   0.47675553
 -0.2551951 ]
Sparsity at: 0.6438542449286251
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1214 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.38855556  0.47335616
 -0.26963204]
Sparsity at: 0.6438542449286251
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1327e-04 - accuracy: 0.9999 - val_loss: 0.1173 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.38839477  0.46733952
 -0.27733812]
Sparsity at: 0.6438542449286251
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4073e-04 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.39310628  0.45703343
 -0.27612567]
Sparsity at: 0.6438542449286251
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9560e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.38988966  0.4602137
 -0.2755365 ]
Sparsity at: 0.6438542449286251
Epoch 232/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3799e-05 - accuracy: 1.0000 - val_loss: 0.1176 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.39027438  0.45813772
 -0.2751183 ]
Sparsity at: 0.6438542449286251
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4211e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.39016247  0.45815465
 -0.27438223]
Sparsity at: 0.6438542449286251
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0846e-05 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.38945583  0.45795318
 -0.27355203]
Sparsity at: 0.6438542449286251
Epoch 235/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6701e-05 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.38970384  0.4577979
 -0.27353522]
Sparsity at: 0.6438542449286251
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4421e-05 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.38960564  0.4572641
 -0.2729929 ]
Sparsity at: 0.6438542449286251
Epoch 237/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7832e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.38979694  0.4543809
 -0.27341324]
Sparsity at: 0.6438542449286251
Epoch 238/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1619e-05 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.39020014  0.45506215
 -0.2736133 ]
Sparsity at: 0.6438542449286251
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0655e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.3893898   0.4552619
 -0.27426147]
Sparsity at: 0.6438542449286251
Epoch 240/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1301e-05 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9843
[ 0.07077749  0.         -0.06288844 ...  0.39035955  0.45510164
 -0.27393496]
Sparsity at: 0.6438542449286251
Epoch 241/500
235/235 [==============================] - 3s 13ms/step - loss: 9.0875e-06 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9842
[ 0.07077749  0.         -0.06288844 ...  0.3909178   0.4551764
 -0.2743764 ]
Sparsity at: 0.6438542449286251
Epoch 242/500
235/235 [==============================] - 3s 13ms/step - loss: 7.8639e-06 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9840
[ 0.07077749  0.         -0.06288844 ...  0.39118132  0.45605952
 -0.27472818]
Sparsity at: 0.6438542449286251
Epoch 243/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0926e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.39198694  0.45694414
 -0.2849757 ]
Sparsity at: 0.6438542449286251
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9987 - val_loss: 0.1368 - val_accuracy: 0.9806
[ 0.07077749  0.         -0.06288844 ...  0.4253296   0.3963998
 -0.26524222]
Sparsity at: 0.6438542449286251
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1238 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.42534575  0.39230657
 -0.26009858]
Sparsity at: 0.6438542449286251
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4926e-04 - accuracy: 0.9998 - val_loss: 0.1205 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.4332269   0.3912942
 -0.2585145 ]
Sparsity at: 0.6438542449286251
Epoch 247/500
235/235 [==============================] - 3s 13ms/step - loss: 8.4551e-04 - accuracy: 0.9998 - val_loss: 0.1217 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.43267626  0.39063585
 -0.26539943]
Sparsity at: 0.6438542449286251
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4157e-04 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.43384716  0.3947044
 -0.2656532 ]
Sparsity at: 0.6438542449286251
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 9.9042e-05 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.4246617   0.3977846
 -0.26689655]
Sparsity at: 0.6438542449286251
Epoch 250/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8103e-05 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.42411664  0.39739355
 -0.26750943]
Sparsity at: 0.6438542449286251
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.30963061720410323
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.34061425999016137
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 0. 1. 0.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.6983748524054292
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 192s 12ms/step - loss: 4.9065e-05 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.42492878  0.3997808
 -0.27013266]
Sparsity at: 0.6438542449286251
Epoch 252/500
235/235 [==============================] - 3s 13ms/step - loss: 2.8377e-05 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.4247327   0.40152106
 -0.2691469 ]
Sparsity at: 0.6438542449286251
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6001e-05 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.42461964  0.40116614
 -0.2688547 ]
Sparsity at: 0.6438542449286251
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5745e-05 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.42473522  0.40187883
 -0.26851445]
Sparsity at: 0.6438542449286251
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4912e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.42530847  0.40260896
 -0.26883975]
Sparsity at: 0.6438542449286251
Epoch 256/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0771e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.42974523  0.40367523
 -0.26985252]
Sparsity at: 0.6438542449286251
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4663e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.43160442  0.4052325
 -0.27024984]
Sparsity at: 0.6438542449286251
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1255e-05 - accuracy: 1.0000 - val_loss: 0.1128 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.43106678  0.4060231
 -0.27058065]
Sparsity at: 0.6438542449286251
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6011e-04 - accuracy: 0.9999 - val_loss: 0.1307 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.431349    0.40806052
 -0.2707382 ]
Sparsity at: 0.6438542449286251
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3792e-04 - accuracy: 0.9997 - val_loss: 0.1346 - val_accuracy: 0.9800
[ 0.07077749  0.         -0.06288844 ...  0.42398202  0.36041996
 -0.2828116 ]
Sparsity at: 0.6438542449286251
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1278 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.43408975  0.37217593
 -0.29092374]
Sparsity at: 0.6438542449286251
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1302 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.42805     0.39060345
 -0.26494518]
Sparsity at: 0.6438542449286251
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3221e-04 - accuracy: 0.9999 - val_loss: 0.1175 - val_accuracy: 0.9844
[ 0.07077749  0.         -0.06288844 ...  0.42064774  0.40313432
 -0.2770736 ]
Sparsity at: 0.6438542449286251
Epoch 264/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0638e-04 - accuracy: 0.9999 - val_loss: 0.1230 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.44053605  0.39917213
 -0.28096694]
Sparsity at: 0.6438542449286251
Epoch 265/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2431e-04 - accuracy: 0.9999 - val_loss: 0.1219 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.4387658   0.387657
 -0.27950564]
Sparsity at: 0.6438542449286251
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3016e-05 - accuracy: 1.0000 - val_loss: 0.1204 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.4402159   0.39112613
 -0.28342667]
Sparsity at: 0.6438542449286251
Epoch 267/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2334e-04 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9842
[ 0.07077749  0.         -0.06288844 ...  0.44032708  0.39446315
 -0.27689728]
Sparsity at: 0.6438542449286251
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2466e-04 - accuracy: 0.9999 - val_loss: 0.1216 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.43648854  0.40442055
 -0.25954935]
Sparsity at: 0.6438542449286251
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5591e-04 - accuracy: 0.9999 - val_loss: 0.1209 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.4427551   0.39917445
 -0.26554817]
Sparsity at: 0.6438542449286251
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8256e-04 - accuracy: 0.9999 - val_loss: 0.1228 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.44433206  0.3933307
 -0.26152053]
Sparsity at: 0.6438542449286251
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7781e-05 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.4432832   0.3935386
 -0.26007167]
Sparsity at: 0.6438542449286251
Epoch 272/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0423e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.44272903  0.39539567
 -0.2610859 ]
Sparsity at: 0.6438542449286251
Epoch 273/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3203e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9849
[ 0.07077749  0.         -0.06288844 ...  0.44260526  0.3973002
 -0.2608887 ]
Sparsity at: 0.6438542449286251
Epoch 274/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2605e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9845
[ 0.07077749  0.         -0.06288844 ...  0.4423295   0.40323722
 -0.26211402]
Sparsity at: 0.6438542449286251
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0253e-05 - accuracy: 1.0000 - val_loss: 0.1189 - val_accuracy: 0.9837
[ 0.07077749  0.         -0.06288844 ...  0.44157282  0.4069013
 -0.26273268]
Sparsity at: 0.6438542449286251
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2090e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9842
[ 0.07077749  0.         -0.06288844 ...  0.44161204  0.40883613
 -0.26385757]
Sparsity at: 0.6438542449286251
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 9.8449e-06 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9843
[ 0.07077749  0.         -0.06288844 ...  0.44178417  0.41054335
 -0.2646439 ]
Sparsity at: 0.6438542449286251
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6454e-06 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9846
[ 0.07077749  0.         -0.06288844 ...  0.44157726  0.4121817
 -0.26400548]
Sparsity at: 0.6438542449286251
Epoch 279/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0267e-05 - accuracy: 1.0000 - val_loss: 0.1197 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.44189206  0.4180744
 -0.26107502]
Sparsity at: 0.6438542449286251
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5266e-06 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.44111434  0.4190951
 -0.26178277]
Sparsity at: 0.6438542449286251
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6531e-06 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9840
[ 0.07077749  0.         -0.06288844 ...  0.44118953  0.41788945
 -0.26142076]
Sparsity at: 0.6438542449286251
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4231e-06 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9843
[ 0.07077749  0.         -0.06288844 ...  0.44148332  0.4184369
 -0.26185936]
Sparsity at: 0.6438542449286251
Epoch 283/500
235/235 [==============================] - 3s 13ms/step - loss: 6.6650e-06 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9841
[ 0.07077749  0.         -0.06288844 ...  0.4423627   0.4198468
 -0.2611119 ]
Sparsity at: 0.6438542449286251
Epoch 284/500
235/235 [==============================] - 3s 13ms/step - loss: 4.6349e-06 - accuracy: 1.0000 - val_loss: 0.1191 - val_accuracy: 0.9839
[ 0.07077749  0.         -0.06288844 ...  0.44149214  0.4198811
 -0.2614711 ]
Sparsity at: 0.6438542449286251
Epoch 285/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4018e-06 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.44134834  0.42130676
 -0.26364344]
Sparsity at: 0.6438542449286251
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1644e-06 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.44182163  0.42254448
 -0.26432937]
Sparsity at: 0.6438542449286251
Epoch 287/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 0.1627 - val_accuracy: 0.9787
[ 0.07077749  0.         -0.06288844 ...  0.45585608  0.42639595
 -0.2728703 ]
Sparsity at: 0.6438542449286251
Epoch 288/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.1417 - val_accuracy: 0.9801
[ 0.07077749  0.         -0.06288844 ...  0.44064945  0.4511273
 -0.2830773 ]
Sparsity at: 0.6438542449286251
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9995 - val_loss: 0.1288 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.42771792  0.47134545
 -0.2509048 ]
Sparsity at: 0.6438542449286251
Epoch 290/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1385e-04 - accuracy: 0.9999 - val_loss: 0.1254 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.43214378  0.47052953
 -0.2535617 ]
Sparsity at: 0.6438542449286251
Epoch 291/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1395e-04 - accuracy: 1.0000 - val_loss: 0.1255 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.43160203  0.47604507
 -0.25830805]
Sparsity at: 0.6438542449286251
Epoch 292/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3283e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.4304092   0.47559768
 -0.25930053]
Sparsity at: 0.6438542449286251
Epoch 293/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4219e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.43015033  0.475722
 -0.25950754]
Sparsity at: 0.6438542449286251
Epoch 294/500
235/235 [==============================] - 3s 13ms/step - loss: 3.2202e-05 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.42888868  0.47629115
 -0.26025802]
Sparsity at: 0.6438542449286251
Epoch 295/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3475e-05 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.42878622  0.47749227
 -0.26406673]
Sparsity at: 0.6438542449286251
Epoch 296/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7979e-05 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.4304275   0.47730762
 -0.2643415 ]
Sparsity at: 0.6438542449286251
Epoch 297/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4277e-05 - accuracy: 1.0000 - val_loss: 0.1209 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.43063882  0.4772587
 -0.26320407]
Sparsity at: 0.6438542449286251
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2698e-05 - accuracy: 1.0000 - val_loss: 0.1209 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.43029717  0.47591558
 -0.26359656]
Sparsity at: 0.6438542449286251
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2102e-05 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.43078616  0.4761026
 -0.26447618]
Sparsity at: 0.6438542449286251
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1792e-04 - accuracy: 0.9999 - val_loss: 0.1334 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.43216398  0.4799171
 -0.2682751 ]
Sparsity at: 0.6438542449286251
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.4106218309445744
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.42667934705536226
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 0. 1. 0.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.788631870266066
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 205s 12ms/step - loss: 7.3774e-04 - accuracy: 0.9998 - val_loss: 0.1181 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.4281677   0.48296604
 -0.23433328]
Sparsity at: 0.6438542449286251
Epoch 302/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1278 - val_accuracy: 0.9812
[ 0.07077749  0.         -0.06288844 ...  0.43922102  0.4835326
 -0.25486204]
Sparsity at: 0.6438542449286251
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1371 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.4320869   0.477214
 -0.24938615]
Sparsity at: 0.6438542449286251
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 7.0273e-04 - accuracy: 0.9998 - val_loss: 0.1253 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.4374423   0.46683407
 -0.23109311]
Sparsity at: 0.6438542449286251
Epoch 305/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7947e-04 - accuracy: 0.9999 - val_loss: 0.1196 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.43255687  0.4604224
 -0.22897227]
Sparsity at: 0.6438542449286251
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3083e-04 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.4339354   0.4588677
 -0.22998253]
Sparsity at: 0.6438542449286251
Epoch 307/500
235/235 [==============================] - 3s 13ms/step - loss: 2.4891e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.4344025   0.46041006
 -0.23076819]
Sparsity at: 0.6438542449286251
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2725e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.43768525  0.4606189
 -0.23310141]
Sparsity at: 0.6438542449286251
Epoch 309/500
235/235 [==============================] - 4s 15ms/step - loss: 2.0548e-05 - accuracy: 1.0000 - val_loss: 0.1255 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.43832225  0.46108812
 -0.233806  ]
Sparsity at: 0.6438542449286251
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3083e-05 - accuracy: 1.0000 - val_loss: 0.1244 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.43887505  0.46095908
 -0.23401919]
Sparsity at: 0.6438542449286251
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0114e-05 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.4389019   0.46178088
 -0.2342692 ]
Sparsity at: 0.6438542449286251
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0036e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.43866858  0.4610992
 -0.23464577]
Sparsity at: 0.6438542449286251
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 9.1978e-06 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.43865892  0.4603495
 -0.23470102]
Sparsity at: 0.6438542449286251
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2866e-06 - accuracy: 1.0000 - val_loss: 0.1229 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.43725714  0.46107224
 -0.23450291]
Sparsity at: 0.6438542449286251
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 7.0057e-06 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.43781543  0.46155992
 -0.23507413]
Sparsity at: 0.6438542449286251
Epoch 316/500
235/235 [==============================] - 3s 13ms/step - loss: 5.0730e-06 - accuracy: 1.0000 - val_loss: 0.1220 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.43820152  0.4625052
 -0.23585944]
Sparsity at: 0.6438542449286251
Epoch 317/500
235/235 [==============================] - 3s 13ms/step - loss: 8.8828e-06 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.43813828  0.46444348
 -0.23539029]
Sparsity at: 0.6438542449286251
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1764e-05 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.4399239   0.4555099
 -0.23476665]
Sparsity at: 0.6438542449286251
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5185e-05 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9838
[ 0.07077749  0.         -0.06288844 ...  0.43964484  0.46139845
 -0.24377984]
Sparsity at: 0.6438542449286251
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1327 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.44011313  0.46664482
 -0.24851525]
Sparsity at: 0.6438542449286251
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1407 - val_accuracy: 0.9808
[ 0.07077749  0.         -0.06288844 ...  0.42823315  0.45817065
 -0.23862125]
Sparsity at: 0.6438542449286251
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8530e-04 - accuracy: 0.9999 - val_loss: 0.1338 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.4261958   0.4649532
 -0.26518995]
Sparsity at: 0.6438542449286251
Epoch 323/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6246e-04 - accuracy: 0.9999 - val_loss: 0.1324 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.42834544  0.46735
 -0.2662315 ]
Sparsity at: 0.6438542449286251
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 8.4722e-05 - accuracy: 1.0000 - val_loss: 0.1306 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.42952496  0.46396297
 -0.271135  ]
Sparsity at: 0.6438542449286251
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0491e-05 - accuracy: 1.0000 - val_loss: 0.1310 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.42987752  0.4656982
 -0.26958302]
Sparsity at: 0.6438542449286251
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4479e-05 - accuracy: 1.0000 - val_loss: 0.1305 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.4292314   0.46584547
 -0.26956308]
Sparsity at: 0.6438542449286251
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5489e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.42950842  0.46604738
 -0.27050596]
Sparsity at: 0.6438542449286251
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4445e-05 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.4295939   0.46606135
 -0.27090785]
Sparsity at: 0.6438542449286251
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3499e-05 - accuracy: 1.0000 - val_loss: 0.1290 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.42977202  0.46649215
 -0.2704137 ]
Sparsity at: 0.6438542449286251
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1288e-05 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9821
[ 0.07077749  0.         -0.06288844 ...  0.43013737  0.46660137
 -0.26970643]
Sparsity at: 0.6438542449286251
Epoch 331/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4900e-05 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.4300094   0.467166
 -0.26899195]
Sparsity at: 0.6438542449286251
Epoch 332/500
235/235 [==============================] - 3s 13ms/step - loss: 9.4384e-06 - accuracy: 1.0000 - val_loss: 0.1296 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.4301048   0.46699572
 -0.26826382]
Sparsity at: 0.6438542449286251
Epoch 333/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1563e-05 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.43032768  0.4659848
 -0.26888448]
Sparsity at: 0.6438542449286251
Epoch 334/500
235/235 [==============================] - 3s 13ms/step - loss: 7.3359e-06 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.43039677  0.4668266
 -0.26918292]
Sparsity at: 0.6438542449286251
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2065e-05 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.42345542  0.46653143
 -0.26314574]
Sparsity at: 0.6438542449286251
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7870e-05 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.42497975  0.46668223
 -0.26682302]
Sparsity at: 0.6438542449286251
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9646e-05 - accuracy: 1.0000 - val_loss: 0.1280 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.43275326  0.46780232
 -0.26771256]
Sparsity at: 0.6438542449286251
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5351e-06 - accuracy: 1.0000 - val_loss: 0.1277 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.43284553  0.46949047
 -0.2696851 ]
Sparsity at: 0.6438542449286251
Epoch 339/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5111e-04 - accuracy: 1.0000 - val_loss: 0.1278 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.42415938  0.46757483
 -0.27210495]
Sparsity at: 0.6438542449286251
Epoch 340/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1653 - val_accuracy: 0.9780
[ 0.07077749  0.         -0.06288844 ...  0.4275187   0.47547105
 -0.27360895]
Sparsity at: 0.6438542449286251
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9989 - val_loss: 0.1450 - val_accuracy: 0.9803
[ 0.07077749  0.         -0.06288844 ...  0.47187608  0.48324662
 -0.2693155 ]
Sparsity at: 0.6438542449286251
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1389 - val_accuracy: 0.9802
[ 0.07077749  0.         -0.06288844 ...  0.4644055   0.48230964
 -0.271811  ]
Sparsity at: 0.6438542449286251
Epoch 343/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3541e-04 - accuracy: 0.9999 - val_loss: 0.1320 - val_accuracy: 0.9819
[ 0.07077749  0.         -0.06288844 ...  0.46547836  0.47751367
 -0.2646264 ]
Sparsity at: 0.6438542449286251
Epoch 344/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5363e-04 - accuracy: 0.9999 - val_loss: 0.1337 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.46346977  0.48163694
 -0.261578  ]
Sparsity at: 0.6438542449286251
Epoch 345/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7867e-05 - accuracy: 1.0000 - val_loss: 0.1317 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.46416602  0.48120773
 -0.26283646]
Sparsity at: 0.6438542449286251
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3932e-05 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.46824515  0.4789938
 -0.2601951 ]
Sparsity at: 0.6438542449286251
Epoch 347/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7060e-05 - accuracy: 1.0000 - val_loss: 0.1296 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.46632048  0.47913274
 -0.26188332]
Sparsity at: 0.6438542449286251
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2089e-04 - accuracy: 0.9999 - val_loss: 0.1315 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.46590853  0.48487112
 -0.25576824]
Sparsity at: 0.6438542449286251
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9970e-05 - accuracy: 1.0000 - val_loss: 0.1314 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.4661097   0.48450857
 -0.2557809 ]
Sparsity at: 0.6438542449286251
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9277e-05 - accuracy: 1.0000 - val_loss: 0.1307 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.46662214  0.48461914
 -0.25185353]
Sparsity at: 0.6438542449286251
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.5180378987403387
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.5247421684808558
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 0. 1. 0.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.8768825577847039
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 29s 12ms/step - loss: 1.3661e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9823
[ 0.07077749  0.         -0.06288844 ...  0.46659395  0.48465043
 -0.25360987]
Sparsity at: 0.6438542449286251
Epoch 352/500
235/235 [==============================] - 3s 13ms/step - loss: 9.2237e-06 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.46653965  0.4845622
 -0.25327763]
Sparsity at: 0.6438542449286251
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0798e-04 - accuracy: 0.9999 - val_loss: 0.1416 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.46509033  0.47266546
 -0.22879739]
Sparsity at: 0.6438542449286251
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0789e-04 - accuracy: 0.9999 - val_loss: 0.1388 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.47191072  0.46594515
 -0.2337453 ]
Sparsity at: 0.6438542449286251
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 8.9816e-05 - accuracy: 1.0000 - val_loss: 0.1410 - val_accuracy: 0.9812
[ 0.07077749  0.         -0.06288844 ...  0.48601577  0.46847975
 -0.24111599]
Sparsity at: 0.6438542449286251
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6786e-05 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.48619744  0.46819097
 -0.23277113]
Sparsity at: 0.6438542449286251
Epoch 357/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0178e-05 - accuracy: 1.0000 - val_loss: 0.1398 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.4877941   0.46550992
 -0.23399201]
Sparsity at: 0.6438542449286251
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2157e-05 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.486053    0.46680996
 -0.23518828]
Sparsity at: 0.6438542449286251
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4202e-05 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.466299    0.46591148
 -0.23750442]
Sparsity at: 0.6438542449286251
Epoch 360/500
235/235 [==============================] - 3s 13ms/step - loss: 8.8647e-06 - accuracy: 1.0000 - val_loss: 0.1385 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.46845478  0.46588182
 -0.23798071]
Sparsity at: 0.6438542449286251
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1211e-06 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.46868172  0.46605375
 -0.23730879]
Sparsity at: 0.6438542449286251
Epoch 362/500
235/235 [==============================] - 3s 13ms/step - loss: 9.4605e-06 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.46814448  0.46373022
 -0.2369678 ]
Sparsity at: 0.6438542449286251
Epoch 363/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4956e-04 - accuracy: 1.0000 - val_loss: 0.1447 - val_accuracy: 0.9819
[ 0.07077749  0.         -0.06288844 ...  0.45318475  0.46331736
 -0.23964566]
Sparsity at: 0.6438542449286251
Epoch 364/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1704 - val_accuracy: 0.9777
[ 0.07077749  0.         -0.06288844 ...  0.41916114  0.48248497
 -0.16598772]
Sparsity at: 0.6438542449286251
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.1485 - val_accuracy: 0.9790
[ 0.07077749  0.         -0.06288844 ...  0.41373438  0.48888516
 -0.1709411 ]
Sparsity at: 0.6438542449286251
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4066e-04 - accuracy: 0.9997 - val_loss: 0.1445 - val_accuracy: 0.9797
[ 0.07077749  0.         -0.06288844 ...  0.42586762  0.47692442
 -0.20348479]
Sparsity at: 0.6438542449286251
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0443e-04 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9806
[ 0.07077749  0.         -0.06288844 ...  0.42785695  0.47527185
 -0.20298952]
Sparsity at: 0.6438542449286251
Epoch 368/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0094e-04 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9804
[ 0.07077749  0.         -0.06288844 ...  0.42789787  0.47180867
 -0.19704741]
Sparsity at: 0.6438542449286251
Epoch 369/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4511e-05 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9802
[ 0.07077749  0.         -0.06288844 ...  0.42751023  0.46833578
 -0.19928248]
Sparsity at: 0.6438542449286251
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2033e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9803
[ 0.07077749  0.         -0.06288844 ...  0.42841038  0.46903673
 -0.20228204]
Sparsity at: 0.6438542449286251
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6653e-05 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9803
[ 0.07077749  0.         -0.06288844 ...  0.428572    0.46981385
 -0.20135759]
Sparsity at: 0.6438542449286251
Epoch 372/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2338e-05 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9803
[ 0.07077749  0.         -0.06288844 ...  0.42711174  0.47021908
 -0.20005125]
Sparsity at: 0.6438542449286251
Epoch 373/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9105e-05 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.43130633  0.46924168
 -0.20078124]
Sparsity at: 0.6438542449286251
Epoch 374/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0250e-05 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9805
[ 0.07077749  0.         -0.06288844 ...  0.43118167  0.46967822
 -0.20062515]
Sparsity at: 0.6438542449286251
Epoch 375/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1808e-05 - accuracy: 1.0000 - val_loss: 0.1334 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.4308049   0.4694221
 -0.20060432]
Sparsity at: 0.6438542449286251
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1038e-05 - accuracy: 1.0000 - val_loss: 0.1336 - val_accuracy: 0.9806
[ 0.07077749  0.         -0.06288844 ...  0.43051073  0.4698368
 -0.20070715]
Sparsity at: 0.6438542449286251
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0458e-05 - accuracy: 1.0000 - val_loss: 0.1334 - val_accuracy: 0.9803
[ 0.07077749  0.         -0.06288844 ...  0.4305707   0.4701463
 -0.20150976]
Sparsity at: 0.6438542449286251
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1303e-05 - accuracy: 1.0000 - val_loss: 0.1333 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.43102202  0.46972138
 -0.20182481]
Sparsity at: 0.6438542449286251
Epoch 379/500
235/235 [==============================] - 3s 13ms/step - loss: 6.9515e-06 - accuracy: 1.0000 - val_loss: 0.1327 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.43160513  0.4700328
 -0.20369792]
Sparsity at: 0.6438542449286251
Epoch 380/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9113e-06 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.43151382  0.46998864
 -0.20359817]
Sparsity at: 0.6438542449286251
Epoch 381/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1813e-05 - accuracy: 1.0000 - val_loss: 0.1378 - val_accuracy: 0.9806
[ 0.07077749  0.         -0.06288844 ...  0.4322263   0.47535962
 -0.20460725]
Sparsity at: 0.6438542449286251
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7013e-04 - accuracy: 0.9999 - val_loss: 0.1725 - val_accuracy: 0.9782
[ 0.07077749  0.         -0.06288844 ...  0.4302846   0.48686263
 -0.20454825]
Sparsity at: 0.6438542449286251
Epoch 383/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0034 - accuracy: 0.9989 - val_loss: 0.1712 - val_accuracy: 0.9775
[ 0.07077749  0.         -0.06288844 ...  0.39539206  0.43208456
 -0.2051205 ]
Sparsity at: 0.6438542449286251
Epoch 384/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1324 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.40214753  0.43324134
 -0.1946687 ]
Sparsity at: 0.6438542449286251
Epoch 385/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9623e-04 - accuracy: 0.9999 - val_loss: 0.1311 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.4049466   0.4399004
 -0.20298763]
Sparsity at: 0.6438542449286251
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0819e-04 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.40500405  0.44757605
 -0.20154642]
Sparsity at: 0.6438542449286251
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7583e-05 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.40602788  0.44834328
 -0.20254017]
Sparsity at: 0.6438542449286251
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2887e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.4065044   0.44870567
 -0.2025381 ]
Sparsity at: 0.6438542449286251
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5620e-05 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.40729937  0.44836706
 -0.20325677]
Sparsity at: 0.6438542449286251
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4521e-05 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.40679067  0.44526297
 -0.20275857]
Sparsity at: 0.6438542449286251
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0119e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.40689707  0.44598022
 -0.20324397]
Sparsity at: 0.6438542449286251
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4813e-05 - accuracy: 1.0000 - val_loss: 0.1269 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.40675643  0.44590604
 -0.20351996]
Sparsity at: 0.6438542449286251
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0206e-05 - accuracy: 1.0000 - val_loss: 0.1276 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.40645823  0.4469996
 -0.2040516 ]
Sparsity at: 0.6438542449286251
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6769e-05 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.4068002   0.44782612
 -0.20421644]
Sparsity at: 0.6438542449286251
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1687e-04 - accuracy: 0.9998 - val_loss: 0.1388 - val_accuracy: 0.9806
[ 0.07077749  0.         -0.06288844 ...  0.41350037  0.45085502
 -0.20095164]
Sparsity at: 0.6438542449286251
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1443 - val_accuracy: 0.9806
[ 0.07077749  0.         -0.06288844 ...  0.42426047  0.4495752
 -0.21863194]
Sparsity at: 0.6438542449286251
Epoch 397/500
235/235 [==============================] - 3s 13ms/step - loss: 3.6669e-04 - accuracy: 0.9999 - val_loss: 0.1444 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.41690397  0.44819644
 -0.20617718]
Sparsity at: 0.6438542449286251
Epoch 398/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1887e-04 - accuracy: 0.9999 - val_loss: 0.1432 - val_accuracy: 0.9800
[ 0.07077749  0.         -0.06288844 ...  0.4240171   0.47011942
 -0.19697481]
Sparsity at: 0.6438542449286251
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 8.4680e-05 - accuracy: 1.0000 - val_loss: 0.1407 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.42079747  0.4556221
 -0.19788358]
Sparsity at: 0.6438542449286251
Epoch 400/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2196e-04 - accuracy: 1.0000 - val_loss: 0.1459 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.42416036  0.45677388
 -0.19431537]
Sparsity at: 0.6438542449286251
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.6046858922077618
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 0. 0.]
 ...
 [0. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.5921665441971413
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458667
tf.Tensor(
[[1. 0. 0. ... 0. 0. 1.]
 [1. 0. 1. ... 1. 0. 0.]
 [0. 1. 0. ... 0. 1. 0.]
 ...
 [0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 1. 0. 0.]
 [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.931536803178993
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.112
tf.Tensor(
[[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 28s 12ms/step - loss: 1.7671e-04 - accuracy: 0.9999 - val_loss: 0.1450 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.42883876  0.460115
 -0.20056944]
Sparsity at: 0.6438542449286251
Epoch 402/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1376e-05 - accuracy: 1.0000 - val_loss: 0.1465 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.42334628  0.46117985
 -0.19611774]
Sparsity at: 0.6438542449286251
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4371e-04 - accuracy: 0.9999 - val_loss: 0.1562 - val_accuracy: 0.9799
[ 0.07077749  0.         -0.06288844 ...  0.42438868  0.46356696
 -0.18928646]
Sparsity at: 0.6438542449286251
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1700 - val_accuracy: 0.9783
[ 0.07077749  0.         -0.06288844 ...  0.42911965  0.47714487
 -0.20401563]
Sparsity at: 0.6438542449286251
Epoch 405/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1476 - val_accuracy: 0.9804
[ 0.07077749  0.         -0.06288844 ...  0.41569626  0.46163276
 -0.17767999]
Sparsity at: 0.6438542449286251
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6914e-04 - accuracy: 0.9998 - val_loss: 0.1450 - val_accuracy: 0.9804
[ 0.07077749  0.         -0.06288844 ...  0.42138007  0.4512545
 -0.18764167]
Sparsity at: 0.6438542449286251
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1204e-04 - accuracy: 0.9999 - val_loss: 0.1434 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.40479785  0.4521429
 -0.18671976]
Sparsity at: 0.6438542449286251
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0221e-05 - accuracy: 1.0000 - val_loss: 0.1415 - val_accuracy: 0.9803
[ 0.07077749  0.         -0.06288844 ...  0.40421534  0.44902214
 -0.18592589]
Sparsity at: 0.6438542449286251
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4284e-05 - accuracy: 1.0000 - val_loss: 0.1430 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.40451697  0.44817033
 -0.18559672]
Sparsity at: 0.6438542449286251
Epoch 410/500
235/235 [==============================] - 3s 13ms/step - loss: 1.8784e-05 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.4057004   0.44790584
 -0.18604806]
Sparsity at: 0.6438542449286251
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5021e-05 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.40531087  0.44686997
 -0.18296719]
Sparsity at: 0.6438542449286251
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3355e-04 - accuracy: 0.9999 - val_loss: 0.1420 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.4057958   0.44341525
 -0.19059905]
Sparsity at: 0.6438542449286251
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0402e-05 - accuracy: 1.0000 - val_loss: 0.1453 - val_accuracy: 0.9805
[ 0.07077749  0.         -0.06288844 ...  0.4055903   0.44498333
 -0.19386262]
Sparsity at: 0.6438542449286251
Epoch 414/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5241e-05 - accuracy: 1.0000 - val_loss: 0.1436 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.40623528  0.44614255
 -0.19156435]
Sparsity at: 0.6438542449286251
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 9.1947e-06 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.40646112  0.44667435
 -0.19052126]
Sparsity at: 0.6438542449286251
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7740e-05 - accuracy: 1.0000 - val_loss: 0.1434 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.40671727  0.4460235
 -0.19220746]
Sparsity at: 0.6438542449286251
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2593e-06 - accuracy: 1.0000 - val_loss: 0.1439 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.4070784   0.4466219
 -0.19278365]
Sparsity at: 0.6438542449286251
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4189e-06 - accuracy: 1.0000 - val_loss: 0.1432 - val_accuracy: 0.9812
[ 0.07077749  0.         -0.06288844 ...  0.40830868  0.4472023
 -0.19253796]
Sparsity at: 0.6438542449286251
Epoch 419/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9361e-06 - accuracy: 1.0000 - val_loss: 0.1425 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.40846536  0.44806784
 -0.19334066]
Sparsity at: 0.6438542449286251
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4575e-04 - accuracy: 0.9999 - val_loss: 0.1505 - val_accuracy: 0.9799
[ 0.07077749  0.         -0.06288844 ...  0.40843773  0.4544167
 -0.1932362 ]
Sparsity at: 0.6438542449286251
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6182e-04 - accuracy: 0.9997 - val_loss: 0.1614 - val_accuracy: 0.9793
[ 0.07077749  0.         -0.06288844 ...  0.40887386  0.46649918
 -0.18420753]
Sparsity at: 0.6438542449286251
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1478 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.4075573   0.4631219
 -0.234869  ]
Sparsity at: 0.6438542449286251
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 9.9960e-04 - accuracy: 0.9997 - val_loss: 0.1415 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.42500836  0.48527256
 -0.23199649]
Sparsity at: 0.6438542449286251
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0986e-04 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.4240685   0.48286787
 -0.23159134]
Sparsity at: 0.6438542449286251
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8230e-05 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.42435187  0.47024783
 -0.23339601]
Sparsity at: 0.6438542449286251
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4382e-05 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.4254036   0.47141415
 -0.23339866]
Sparsity at: 0.6438542449286251
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9048e-05 - accuracy: 1.0000 - val_loss: 0.1370 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.42557776  0.47151312
 -0.233947  ]
Sparsity at: 0.6438542449286251
Epoch 428/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5389e-05 - accuracy: 1.0000 - val_loss: 0.1361 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.426025    0.47207215
 -0.2338087 ]
Sparsity at: 0.6438542449286251
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3780e-05 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.4258118   0.4734469
 -0.23397428]
Sparsity at: 0.6438542449286251
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9460e-04 - accuracy: 0.9999 - val_loss: 0.1471 - val_accuracy: 0.9812
[ 0.07077749  0.         -0.06288844 ...  0.40847072  0.47838572
 -0.24468292]
Sparsity at: 0.6438542449286251
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6179e-04 - accuracy: 0.9999 - val_loss: 0.1464 - val_accuracy: 0.9805
[ 0.07077749  0.         -0.06288844 ...  0.4202029   0.51112926
 -0.26185438]
Sparsity at: 0.6438542449286251
Epoch 432/500
235/235 [==============================] - 3s 13ms/step - loss: 9.7000e-05 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.4285372   0.50693166
 -0.26521748]
Sparsity at: 0.6438542449286251
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5968e-05 - accuracy: 1.0000 - val_loss: 0.1436 - val_accuracy: 0.9805
[ 0.07077749  0.         -0.06288844 ...  0.42772502  0.5059608
 -0.2641646 ]
Sparsity at: 0.6438542449286251
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3589e-05 - accuracy: 1.0000 - val_loss: 0.1422 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.42882875  0.5068606
 -0.26437652]
Sparsity at: 0.6438542449286251
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7081e-06 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.42939907  0.50940645
 -0.2664193 ]
Sparsity at: 0.6438542449286251
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3343e-05 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.43300053  0.50744265
 -0.26468062]
Sparsity at: 0.6438542449286251
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7602e-06 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9818
[ 0.07077749  0.         -0.06288844 ...  0.4330128   0.5102368
 -0.2643186 ]
Sparsity at: 0.6438542449286251
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 8.4737e-06 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.43244988  0.5100613
 -0.26775876]
Sparsity at: 0.6438542449286251
Epoch 439/500
235/235 [==============================] - 3s 13ms/step - loss: 6.0817e-06 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.4324163   0.5092308
 -0.26980644]
Sparsity at: 0.6438542449286251
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0981e-06 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 0.9806
[ 0.07077749  0.         -0.06288844 ...  0.4332692   0.5097835
 -0.27161813]
Sparsity at: 0.6438542449286251
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3565e-06 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.43204084  0.5098135
 -0.26897103]
Sparsity at: 0.6438542449286251
Epoch 442/500
235/235 [==============================] - 3s 13ms/step - loss: 4.2442e-06 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.4317629   0.5095644
 -0.26920965]
Sparsity at: 0.6438542449286251
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7558e-06 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.4324276   0.5076834
 -0.26943263]
Sparsity at: 0.6438542449286251
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0799e-06 - accuracy: 1.0000 - val_loss: 0.1380 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.43212533  0.5075647
 -0.27002066]
Sparsity at: 0.6438542449286251
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4847e-06 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.43259212  0.50799346
 -0.27029562]
Sparsity at: 0.6438542449286251
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8583e-06 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.432728    0.5079018
 -0.270239  ]
Sparsity at: 0.6438542449286251
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5473e-04 - accuracy: 0.9999 - val_loss: 0.1573 - val_accuracy: 0.9802
[ 0.07077749  0.         -0.06288844 ...  0.43423587  0.52683544
 -0.28600466]
Sparsity at: 0.6438542449286251
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1624 - val_accuracy: 0.9805
[ 0.07077749  0.         -0.06288844 ...  0.4222902   0.5550759
 -0.23964843]
Sparsity at: 0.6438542449286251
Epoch 449/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1419 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.43641293  0.53332084
 -0.23893799]
Sparsity at: 0.6438542449286251
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5286e-04 - accuracy: 0.9999 - val_loss: 0.1427 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.43350193  0.53451693
 -0.24491473]
Sparsity at: 0.6438542449286251
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7702e-04 - accuracy: 1.0000 - val_loss: 0.1383 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.43703312  0.5321885
 -0.24372284]
Sparsity at: 0.6438542449286251
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5816e-05 - accuracy: 1.0000 - val_loss: 0.1383 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.43172985  0.53457236
 -0.2358054 ]
Sparsity at: 0.6438542449286251
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2468e-05 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9826
[ 0.07077749  0.         -0.06288844 ...  0.43833405  0.5338179
 -0.24821232]
Sparsity at: 0.6438542449286251
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8369e-05 - accuracy: 1.0000 - val_loss: 0.1400 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.43917143  0.5337681
 -0.24877685]
Sparsity at: 0.6438542449286251
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9688e-05 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.44050607  0.5342633
 -0.24977319]
Sparsity at: 0.6438542449286251
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2061e-05 - accuracy: 1.0000 - val_loss: 0.1385 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.4451866   0.53496736
 -0.25838077]
Sparsity at: 0.6438542449286251
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3137e-05 - accuracy: 1.0000 - val_loss: 0.1386 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.4448861   0.5349371
 -0.2579346 ]
Sparsity at: 0.6438542449286251
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 9.4595e-05 - accuracy: 0.9999 - val_loss: 0.1403 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.46686864  0.53097445
 -0.2587954 ]
Sparsity at: 0.6438542449286251
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6467e-05 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.46903396  0.53116775
 -0.25846112]
Sparsity at: 0.6438542449286251
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5165e-05 - accuracy: 1.0000 - val_loss: 0.1385 - val_accuracy: 0.9829
[ 0.07077749  0.         -0.06288844 ...  0.47039732  0.5387912
 -0.2660806 ]
Sparsity at: 0.6438542449286251
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6324e-05 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9827
[ 0.07077749  0.         -0.06288844 ...  0.47311032  0.5427032
 -0.26373452]
Sparsity at: 0.6438542449286251
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2443e-04 - accuracy: 0.9999 - val_loss: 0.1487 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.48527578  0.5629291
 -0.27793238]
Sparsity at: 0.6438542449286251
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5323e-04 - accuracy: 0.9998 - val_loss: 0.1529 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.48391157  0.5419184
 -0.26525414]
Sparsity at: 0.6438542449286251
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0819e-04 - accuracy: 0.9999 - val_loss: 0.1535 - val_accuracy: 0.9822
[ 0.07077749  0.         -0.06288844 ...  0.48714978  0.5409793
 -0.27558827]
Sparsity at: 0.6438542449286251
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7454e-04 - accuracy: 0.9999 - val_loss: 0.1499 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.48920706  0.54246724
 -0.27865174]
Sparsity at: 0.6438542449286251
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0713e-04 - accuracy: 1.0000 - val_loss: 0.1465 - val_accuracy: 0.9820
[ 0.07077749  0.         -0.06288844 ...  0.48981076  0.5471252
 -0.28176633]
Sparsity at: 0.6438542449286251
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 9.2876e-05 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9824
[ 0.07077749  0.         -0.06288844 ...  0.4918682   0.54582596
 -0.277838  ]
Sparsity at: 0.6438542449286251
Epoch 468/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6254e-04 - accuracy: 0.9999 - val_loss: 0.1452 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.4890232   0.5469762
 -0.27492696]
Sparsity at: 0.6438542449286251
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9610e-04 - accuracy: 0.9999 - val_loss: 0.1432 - val_accuracy: 0.9825
[ 0.07077749  0.         -0.06288844 ...  0.50556743  0.54385644
 -0.30041164]
Sparsity at: 0.6438542449286251
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9387e-05 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9830
[ 0.07077749  0.         -0.06288844 ...  0.5069458   0.5450763
 -0.29973534]
Sparsity at: 0.6438542449286251
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1957e-05 - accuracy: 1.0000 - val_loss: 0.1425 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.50785327  0.5534707
 -0.29901218]
Sparsity at: 0.6438542449286251
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4169e-06 - accuracy: 1.0000 - val_loss: 0.1418 - val_accuracy: 0.9836
[ 0.07077749  0.         -0.06288844 ...  0.5084165   0.5527857
 -0.29904526]
Sparsity at: 0.6438542449286251
Epoch 473/500
235/235 [==============================] - 3s 13ms/step - loss: 8.3160e-06 - accuracy: 1.0000 - val_loss: 0.1413 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.51033986  0.5537472
 -0.29901275]
Sparsity at: 0.6438542449286251
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7215e-06 - accuracy: 1.0000 - val_loss: 0.1409 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.5107096   0.55317456
 -0.29785022]
Sparsity at: 0.6438542449286251
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6373e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9828
[ 0.07077749  0.         -0.06288844 ...  0.51090926  0.5474651
 -0.29760975]
Sparsity at: 0.6438542449286251
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5527e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9831
[ 0.07077749  0.         -0.06288844 ...  0.51027447  0.5470878
 -0.28574395]
Sparsity at: 0.6438542449286251
Epoch 477/500
235/235 [==============================] - 3s 13ms/step - loss: 8.2826e-06 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.5091418   0.54859954
 -0.28612548]
Sparsity at: 0.6438542449286251
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8347e-06 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9832
[ 0.07077749  0.         -0.06288844 ...  0.5088205   0.5439255
 -0.28585672]
Sparsity at: 0.6438542449286251
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5194e-06 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9833
[ 0.07077749  0.         -0.06288844 ...  0.50934625  0.54437894
 -0.28515556]
Sparsity at: 0.6438542449286251
Epoch 480/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4359e-06 - accuracy: 1.0000 - val_loss: 0.1358 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.50964963  0.5447102
 -0.2852954 ]
Sparsity at: 0.6438542449286251
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3368e-06 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9835
[ 0.07077749  0.         -0.06288844 ...  0.50971174  0.5436817
 -0.28485602]
Sparsity at: 0.6438542449286251
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3325e-06 - accuracy: 1.0000 - val_loss: 0.1377 - val_accuracy: 0.9834
[ 0.07077749  0.         -0.06288844 ...  0.50753284  0.5417522
 -0.28370687]
Sparsity at: 0.6438542449286251
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6525e-04 - accuracy: 0.9999 - val_loss: 0.1501 - val_accuracy: 0.9810
[ 0.07077749  0.         -0.06288844 ...  0.4957314   0.5467635
 -0.27302957]
Sparsity at: 0.6438542449286251
Epoch 484/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1854 - val_accuracy: 0.9790
[ 0.07077749  0.         -0.06288844 ...  0.4920212   0.5643803
 -0.31576407]
Sparsity at: 0.6438542449286251
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1602 - val_accuracy: 0.9807
[ 0.07077749  0.         -0.06288844 ...  0.47613388  0.5374216
 -0.2904787 ]
Sparsity at: 0.6438542449286251
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0086e-04 - accuracy: 0.9999 - val_loss: 0.1543 - val_accuracy: 0.9812
[ 0.07077749  0.         -0.06288844 ...  0.47618225  0.5218313
 -0.2959403 ]
Sparsity at: 0.6438542449286251
Epoch 487/500
235/235 [==============================] - 3s 13ms/step - loss: 4.1989e-05 - accuracy: 1.0000 - val_loss: 0.1523 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.47708914  0.5256967
 -0.29647672]
Sparsity at: 0.6438542449286251
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0401e-05 - accuracy: 1.0000 - val_loss: 0.1565 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.47980717  0.52705085
 -0.29965132]
Sparsity at: 0.6438542449286251
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4800e-05 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9812
[ 0.07077749  0.         -0.06288844 ...  0.4796992   0.52809614
 -0.29820043]
Sparsity at: 0.6438542449286251
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3399e-05 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9811
[ 0.07077749  0.         -0.06288844 ...  0.47978666  0.52830267
 -0.29754686]
Sparsity at: 0.6438542449286251
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2346e-05 - accuracy: 1.0000 - val_loss: 0.1535 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.47993326  0.52759266
 -0.29760388]
Sparsity at: 0.6438542449286251
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 8.6654e-06 - accuracy: 1.0000 - val_loss: 0.1534 - val_accuracy: 0.9816
[ 0.07077749  0.         -0.06288844 ...  0.48044232  0.5275732
 -0.3002672 ]
Sparsity at: 0.6438542449286251
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3905e-06 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.48052284  0.5272193
 -0.29992884]
Sparsity at: 0.6438542449286251
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7102e-06 - accuracy: 1.0000 - val_loss: 0.1525 - val_accuracy: 0.9817
[ 0.07077749  0.         -0.06288844 ...  0.48059866  0.52658325
 -0.30104485]
Sparsity at: 0.6438542449286251
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6726e-06 - accuracy: 1.0000 - val_loss: 0.1527 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.48027962  0.5266189
 -0.30040017]
Sparsity at: 0.6438542449286251
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9878e-05 - accuracy: 1.0000 - val_loss: 0.1538 - val_accuracy: 0.9813
[ 0.07077749  0.         -0.06288844 ...  0.47981068  0.52152103
 -0.29219696]
Sparsity at: 0.6438542449286251
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4740e-05 - accuracy: 1.0000 - val_loss: 0.1570 - val_accuracy: 0.9815
[ 0.07077749  0.         -0.06288844 ...  0.47968468  0.5151563
 -0.2904566 ]
Sparsity at: 0.6438542449286251
Epoch 498/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0186e-06 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9808
[ 0.07077749  0.         -0.06288844 ...  0.47794333  0.5176247
 -0.29083908]
Sparsity at: 0.6438542449286251
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9125e-06 - accuracy: 1.0000 - val_loss: 0.1559 - val_accuracy: 0.9814
[ 0.07077749  0.         -0.06288844 ...  0.47900727  0.5184995
 -0.29067016]
Sparsity at: 0.6438542449286251
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1888e-06 - accuracy: 1.0000 - val_loss: 0.1551 - val_accuracy: 0.9809
[ 0.07077749  0.         -0.06288844 ...  0.47838292  0.52062374
 -0.28904247]
Sparsity at: 0.6438542449286251
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.042060211300849915
Thresholhold 0.08002246171236038
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.08791892230510712
Thresholhold 0.1589777022600174
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10110617056488991
Thresholhold 0.0191974937915802
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 59:03 - loss: 4.2897 - accuracy: 0.0742WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0094s vs `on_train_batch_begin` time: 2.4542s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 1.7465 - accuracy: 0.8285 - val_loss: 1.1040 - val_accuracy: 0.8917
[-7.4224058e-06  0.0000000e+00  2.2883007e-06 ...  9.1420211e-02
  1.8523102e-01  2.6269946e-03]
Sparsity at: 0.4915671942060086
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0418 - accuracy: 0.8901 - val_loss: 0.9803 - val_accuracy: 0.8950
[5.5615214e-11 0.0000000e+00 1.8172165e-12 ... 7.1047172e-02 1.9423500e-01
 3.5472132e-02]
Sparsity at: 0.4915671942060086
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9832 - accuracy: 0.8912 - val_loss: 0.9537 - val_accuracy: 0.8953
[-2.0745168e-16  0.0000000e+00  7.2925599e-17 ...  6.2335417e-02
  2.0030734e-01  5.1703487e-02]
Sparsity at: 0.4915671942060086
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9653 - accuracy: 0.8908 - val_loss: 0.9417 - val_accuracy: 0.8954
[1.9640492e-22 0.0000000e+00 1.8779860e-22 ... 6.1129581e-02 2.0704530e-01
 6.1727054e-02]
Sparsity at: 0.4915671942060086
Epoch 5/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9558 - accuracy: 0.8909 - val_loss: 0.9346 - val_accuracy: 0.8957
[-4.5747796e-27  0.0000000e+00  4.8975609e-28 ...  6.3969858e-02
  2.1535599e-01  7.0888698e-02]
Sparsity at: 0.4915671942060086
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9499 - accuracy: 0.8909 - val_loss: 0.9286 - val_accuracy: 0.8968
[1.8158581e-32 0.0000000e+00 4.2263033e-33 ... 6.8268828e-02 2.2460929e-01
 7.9503991e-02]
Sparsity at: 0.4915671942060086
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9452 - accuracy: 0.8913 - val_loss: 0.9252 - val_accuracy: 0.8969
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.0614852e-02
  2.3499019e-01  8.8251404e-02]
Sparsity at: 0.4915671942060086
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9417 - accuracy: 0.8914 - val_loss: 0.9221 - val_accuracy: 0.8970
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.0677243e-02
  2.4471071e-01  9.6470110e-02]
Sparsity at: 0.4915671942060086
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9389 - accuracy: 0.8916 - val_loss: 0.9191 - val_accuracy: 0.8982
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  6.88669160e-02
  2.53230780e-01  1.03944845e-01]
Sparsity at: 0.4915671942060086
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9366 - accuracy: 0.8916 - val_loss: 0.9171 - val_accuracy: 0.8984
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.5937124e-02
  2.6004481e-01  1.1106949e-01]
Sparsity at: 0.4915671942060086
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9346 - accuracy: 0.8917 - val_loss: 0.9157 - val_accuracy: 0.8976
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  6.23908453e-02
  2.65252769e-01  1.17580526e-01]
Sparsity at: 0.4915671942060086
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9330 - accuracy: 0.8917 - val_loss: 0.9139 - val_accuracy: 0.8982
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  5.88212200e-02
  2.68747896e-01  1.23253986e-01]
Sparsity at: 0.4915671942060086
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9316 - accuracy: 0.8917 - val_loss: 0.9126 - val_accuracy: 0.8986
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4867674e-02
  2.7098715e-01  1.2849534e-01]
Sparsity at: 0.4915671942060086
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9302 - accuracy: 0.8922 - val_loss: 0.9113 - val_accuracy: 0.8987
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.0561082e-02
  2.7167642e-01  1.3325092e-01]
Sparsity at: 0.4915671942060086
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9292 - accuracy: 0.8919 - val_loss: 0.9103 - val_accuracy: 0.8989
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.6809115e-02
  2.7069014e-01  1.3743837e-01]
Sparsity at: 0.4915671942060086
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9282 - accuracy: 0.8920 - val_loss: 0.9094 - val_accuracy: 0.8992
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.3219432e-02
  2.6908079e-01  1.4089060e-01]
Sparsity at: 0.4915671942060086
Epoch 17/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9273 - accuracy: 0.8921 - val_loss: 0.9085 - val_accuracy: 0.8991
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.0329464e-02
  2.6668891e-01  1.4359517e-01]
Sparsity at: 0.4915671942060086
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9265 - accuracy: 0.8922 - val_loss: 0.9076 - val_accuracy: 0.8994
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.7875161e-02
  2.6358977e-01  1.4594245e-01]
Sparsity at: 0.4915671942060086
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9257 - accuracy: 0.8925 - val_loss: 0.9073 - val_accuracy: 0.8995
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.6208257e-02
  2.5999558e-01  1.4792636e-01]
Sparsity at: 0.4915671942060086
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9251 - accuracy: 0.8924 - val_loss: 0.9067 - val_accuracy: 0.8997
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.4922738e-02
  2.5580740e-01  1.4946622e-01]
Sparsity at: 0.4915671942060086
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9248 - accuracy: 0.8927 - val_loss: 0.9064 - val_accuracy: 0.8992
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.3873215e-02
  2.5143060e-01  1.5102518e-01]
Sparsity at: 0.4915671942060086
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9242 - accuracy: 0.8929 - val_loss: 0.9057 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.3269815e-02
  2.4688014e-01  1.5208051e-01]
Sparsity at: 0.4915671942060086
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9237 - accuracy: 0.8928 - val_loss: 0.9056 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.2733459e-02
  2.4219456e-01  1.5286732e-01]
Sparsity at: 0.4915671942060086
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9235 - accuracy: 0.8929 - val_loss: 0.9049 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.2235209e-02
  2.3766281e-01  1.5349254e-01]
Sparsity at: 0.4915671942060086
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9230 - accuracy: 0.8929 - val_loss: 0.9047 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.1609174e-02
  2.3317803e-01  1.5377440e-01]
Sparsity at: 0.4915671942060086
Epoch 26/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9226 - accuracy: 0.8929 - val_loss: 0.9044 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.0940700e-02
  2.2863853e-01  1.5369570e-01]
Sparsity at: 0.4915671942060086
Epoch 27/500
235/235 [==============================] - 2s 10ms/step - loss: 0.9223 - accuracy: 0.8929 - val_loss: 0.9042 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.0517349e-02
  2.2397560e-01  1.5312962e-01]
Sparsity at: 0.4915671942060086
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9218 - accuracy: 0.8931 - val_loss: 0.9035 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.9502772e-02
  2.1947739e-01  1.5237187e-01]
Sparsity at: 0.4915671942060086
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9214 - accuracy: 0.8931 - val_loss: 0.9030 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.8751884e-02
  2.1532147e-01  1.5125379e-01]
Sparsity at: 0.4915671942060086
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9212 - accuracy: 0.8928 - val_loss: 0.9029 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.7599463e-02
  2.1130493e-01  1.5000422e-01]
Sparsity at: 0.4915671942060086
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9209 - accuracy: 0.8930 - val_loss: 0.9024 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.6702497e-02
  2.0790255e-01  1.4849132e-01]
Sparsity at: 0.4915671942060086
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9205 - accuracy: 0.8928 - val_loss: 0.9022 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.5605327e-02
  2.0461538e-01  1.4743079e-01]
Sparsity at: 0.4915671942060086
Epoch 33/500
235/235 [==============================] - 2s 10ms/step - loss: 0.9204 - accuracy: 0.8930 - val_loss: 0.9021 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.4730243e-02
  2.0169476e-01  1.4624277e-01]
Sparsity at: 0.4915671942060086
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9201 - accuracy: 0.8930 - val_loss: 0.9017 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.3780614e-02
  1.9894122e-01  1.4527246e-01]
Sparsity at: 0.4915671942060086
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9198 - accuracy: 0.8931 - val_loss: 0.9019 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.3090370e-02
  1.9644079e-01  1.4442804e-01]
Sparsity at: 0.4915671942060086
Epoch 36/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9196 - accuracy: 0.8933 - val_loss: 0.9014 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.2501219e-02
  1.9398968e-01  1.4362805e-01]
Sparsity at: 0.4915671942060086
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9195 - accuracy: 0.8931 - val_loss: 0.9017 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.2063103e-02
  1.9154590e-01  1.4326109e-01]
Sparsity at: 0.4915671942060086
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9194 - accuracy: 0.8931 - val_loss: 0.9012 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1750258e-02
  1.8923843e-01  1.4279950e-01]
Sparsity at: 0.4915671942060086
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9191 - accuracy: 0.8932 - val_loss: 0.9010 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1524100e-02
  1.8683176e-01  1.4245489e-01]
Sparsity at: 0.4915671942060086
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9189 - accuracy: 0.8934 - val_loss: 0.9009 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1551613e-02
  1.8502434e-01  1.4205465e-01]
Sparsity at: 0.4915671942060086
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9187 - accuracy: 0.8931 - val_loss: 0.9007 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1379322e-02
  1.8288174e-01  1.4197750e-01]
Sparsity at: 0.4915671942060086
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9184 - accuracy: 0.8931 - val_loss: 0.9007 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1247327e-02
  1.8119808e-01  1.4184292e-01]
Sparsity at: 0.4915671942060086
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9184 - accuracy: 0.8932 - val_loss: 0.9003 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1291398e-02
  1.7937386e-01  1.4194813e-01]
Sparsity at: 0.4915671942060086
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9182 - accuracy: 0.8934 - val_loss: 0.9004 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1375123e-02
  1.7775756e-01  1.4197592e-01]
Sparsity at: 0.4915671942060086
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9181 - accuracy: 0.8935 - val_loss: 0.9003 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1589611e-02
  1.7608888e-01  1.4180830e-01]
Sparsity at: 0.4915671942060086
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9180 - accuracy: 0.8934 - val_loss: 0.9001 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1664469e-02
  1.7469986e-01  1.4188744e-01]
Sparsity at: 0.4915671942060086
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9178 - accuracy: 0.8934 - val_loss: 0.9001 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.1791795e-02
  1.7320327e-01  1.4204505e-01]
Sparsity at: 0.4915671942060086
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9176 - accuracy: 0.8937 - val_loss: 0.9001 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.2034558e-02
  1.7148156e-01  1.4219946e-01]
Sparsity at: 0.4915671942060086
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9178 - accuracy: 0.8932 - val_loss: 0.8999 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.2316854e-02
  1.7012836e-01  1.4213602e-01]
Sparsity at: 0.4915671942060086
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9176 - accuracy: 0.8935 - val_loss: 0.8997 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.2561373e-02
  1.6851225e-01  1.4229941e-01]
Sparsity at: 0.4915671942060086
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.0005102794590691739
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.00805874168673859
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.1238041130145664
Thresholhold -0.10793383419513702
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 56s 7ms/step - loss: 0.9175 - accuracy: 0.8935 - val_loss: 0.8999 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.2841696e-02
  1.6711740e-01  1.4248542e-01]
Sparsity at: 0.4915671942060086
Epoch 52/500
235/235 [==============================] - 2s 7ms/step - loss: 0.9173 - accuracy: 0.8934 - val_loss: 0.8995 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.3355966e-02
  1.6539448e-01  1.4270639e-01]
Sparsity at: 0.4915671942060086
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9173 - accuracy: 0.8938 - val_loss: 0.8997 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.3922671e-02
  1.6419682e-01  1.4265877e-01]
Sparsity at: 0.4915671942060086
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9172 - accuracy: 0.8935 - val_loss: 0.8994 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.4451721e-02
  1.6248077e-01  1.4277595e-01]
Sparsity at: 0.4915671942060086
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9172 - accuracy: 0.8936 - val_loss: 0.8992 - val_accuracy: 0.8995
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.5058696e-02
  1.6076016e-01  1.4284347e-01]
Sparsity at: 0.4915671942060086
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9172 - accuracy: 0.8938 - val_loss: 0.8992 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.5745422e-02
  1.5912677e-01  1.4298648e-01]
Sparsity at: 0.4915671942060086
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9170 - accuracy: 0.8938 - val_loss: 0.8991 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.6421098e-02
  1.5728304e-01  1.4314385e-01]
Sparsity at: 0.4915671942060086
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9169 - accuracy: 0.8936 - val_loss: 0.8990 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.7213555e-02
  1.5579453e-01  1.4329554e-01]
Sparsity at: 0.4915671942060086
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9169 - accuracy: 0.8939 - val_loss: 0.8993 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.7971216e-02
  1.5399654e-01  1.4318551e-01]
Sparsity at: 0.4915671942060086
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9168 - accuracy: 0.8938 - val_loss: 0.8992 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.8614761e-02
  1.5222545e-01  1.4332433e-01]
Sparsity at: 0.4915671942060086
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9167 - accuracy: 0.8940 - val_loss: 0.8990 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  2.9336343e-02
  1.5033232e-01  1.4333005e-01]
Sparsity at: 0.4915671942060086
Epoch 62/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9168 - accuracy: 0.8938 - val_loss: 0.8990 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.0037440e-02
  1.4820041e-01  1.4336869e-01]
Sparsity at: 0.4915671942060086
Epoch 63/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9167 - accuracy: 0.8940 - val_loss: 0.8991 - val_accuracy: 0.8995
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.0450746e-02
  1.4645797e-01  1.4333220e-01]
Sparsity at: 0.4915671942060086
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9166 - accuracy: 0.8937 - val_loss: 0.8987 - val_accuracy: 0.8997
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.0955391e-02
  1.4440919e-01  1.4345954e-01]
Sparsity at: 0.4915671942060086
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9166 - accuracy: 0.8938 - val_loss: 0.8989 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.1472702e-02
  1.4255746e-01  1.4328423e-01]
Sparsity at: 0.4915671942060086
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9166 - accuracy: 0.8937 - val_loss: 0.8990 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.1696469e-02
  1.4054219e-01  1.4322244e-01]
Sparsity at: 0.4915671942060086
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9165 - accuracy: 0.8942 - val_loss: 0.8989 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.1921782e-02
  1.3864966e-01  1.4319298e-01]
Sparsity at: 0.4915671942060086
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9165 - accuracy: 0.8939 - val_loss: 0.8987 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.2319475e-02
  1.3678779e-01  1.4280958e-01]
Sparsity at: 0.4915671942060086
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9165 - accuracy: 0.8940 - val_loss: 0.8985 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.2560021e-02
  1.3485317e-01  1.4266312e-01]
Sparsity at: 0.4915671942060086
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9163 - accuracy: 0.8942 - val_loss: 0.8987 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.2889519e-02
  1.3281493e-01  1.4248903e-01]
Sparsity at: 0.4915671942060086
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9163 - accuracy: 0.8942 - val_loss: 0.8984 - val_accuracy: 0.9000
[ 4.405955e-34  0.000000e+00 -5.467231e-34 ...  3.315715e-02  1.309211e-01
  1.422019e-01]
Sparsity at: 0.4915671942060086
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9164 - accuracy: 0.8941 - val_loss: 0.8985 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.3424366e-02
  1.2922679e-01  1.4211817e-01]
Sparsity at: 0.4915671942060086
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9163 - accuracy: 0.8942 - val_loss: 0.8987 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.3670563e-02
  1.2736526e-01  1.4198361e-01]
Sparsity at: 0.4915671942060086
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9162 - accuracy: 0.8943 - val_loss: 0.8983 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.4102872e-02
  1.2522779e-01  1.4181899e-01]
Sparsity at: 0.4915671942060086
Epoch 75/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9161 - accuracy: 0.8943 - val_loss: 0.8984 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.4536675e-02
  1.2339666e-01  1.4156477e-01]
Sparsity at: 0.4915671942060086
Epoch 76/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9161 - accuracy: 0.8940 - val_loss: 0.8986 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.5162527e-02
  1.2148662e-01  1.4147080e-01]
Sparsity at: 0.4915671942060086
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9162 - accuracy: 0.8942 - val_loss: 0.8981 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.5776358e-02
  1.1968360e-01  1.4125264e-01]
Sparsity at: 0.4915671942060086
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9160 - accuracy: 0.8942 - val_loss: 0.8980 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.6509026e-02
  1.1783002e-01  1.4099756e-01]
Sparsity at: 0.4915671942060086
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9160 - accuracy: 0.8940 - val_loss: 0.8984 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  3.7313934e-02
  1.1603254e-01  1.4076975e-01]
Sparsity at: 0.4915671942060086
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9160 - accuracy: 0.8938 - val_loss: 0.8984 - val_accuracy: 0.9007
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  3.83617580e-02
  1.14589624e-01  1.40581444e-01]
Sparsity at: 0.4915671942060086
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9160 - accuracy: 0.8940 - val_loss: 0.8983 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  3.92619260e-02
  1.12972766e-01  1.40384123e-01]
Sparsity at: 0.4915671942060086
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9161 - accuracy: 0.8940 - val_loss: 0.8980 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.0344629e-02
  1.1153361e-01  1.3995382e-01]
Sparsity at: 0.4915671942060086
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9158 - accuracy: 0.8943 - val_loss: 0.8981 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.1570574e-02
  1.1008904e-01  1.3963601e-01]
Sparsity at: 0.4915671942060086
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9160 - accuracy: 0.8938 - val_loss: 0.8982 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.2847008e-02
  1.0862957e-01  1.3937561e-01]
Sparsity at: 0.4915671942060086
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9159 - accuracy: 0.8939 - val_loss: 0.8981 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.4043295e-02
  1.0718821e-01  1.3918826e-01]
Sparsity at: 0.4915671942060086
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8941 - val_loss: 0.8981 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.5151532e-02
  1.0569327e-01  1.3914025e-01]
Sparsity at: 0.4915671942060086
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8941 - val_loss: 0.8981 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.6313912e-02
  1.0428562e-01  1.3902450e-01]
Sparsity at: 0.4915671942060086
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8942 - val_loss: 0.8980 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.7505684e-02
  1.0255472e-01  1.3881934e-01]
Sparsity at: 0.4915671942060086
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8940 - val_loss: 0.8979 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.8411474e-02
  1.0088680e-01  1.3875610e-01]
Sparsity at: 0.4915671942060086
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9157 - accuracy: 0.8942 - val_loss: 0.8981 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  4.9720909e-02
  9.9165075e-02  1.3855000e-01]
Sparsity at: 0.4915671942060086
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8942 - val_loss: 0.8980 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.0411943e-02
  9.7206965e-02  1.3853054e-01]
Sparsity at: 0.4915671942060086
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9157 - accuracy: 0.8940 - val_loss: 0.8979 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.1577576e-02
  9.5272563e-02  1.3853586e-01]
Sparsity at: 0.4915671942060086
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9157 - accuracy: 0.8942 - val_loss: 0.8981 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2476063e-02
  9.3192004e-02  1.3842435e-01]
Sparsity at: 0.4915671942060086
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9155 - accuracy: 0.8942 - val_loss: 0.8979 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3037953e-02
  9.0897918e-02  1.3851030e-01]
Sparsity at: 0.4915671942060086
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9155 - accuracy: 0.8944 - val_loss: 0.8977 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3738654e-02
  8.8585638e-02  1.3832237e-01]
Sparsity at: 0.4915671942060086
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9156 - accuracy: 0.8942 - val_loss: 0.8978 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4518390e-02
  8.5926108e-02  1.3840148e-01]
Sparsity at: 0.4915671942060086
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9155 - accuracy: 0.8940 - val_loss: 0.8978 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5047587e-02
  8.3290830e-02  1.3842909e-01]
Sparsity at: 0.4915671942060086
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9154 - accuracy: 0.8943 - val_loss: 0.8978 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5457834e-02
  8.0594443e-02  1.3855600e-01]
Sparsity at: 0.4915671942060086
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9153 - accuracy: 0.8943 - val_loss: 0.8976 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5932533e-02
  7.7634662e-02  1.3872516e-01]
Sparsity at: 0.4915671942060086
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9152 - accuracy: 0.8941 - val_loss: 0.8977 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6139152e-02
  7.4969448e-02  1.3880105e-01]
Sparsity at: 0.4915671942060086
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.003496363039474343
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.026177641198420032
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.14994421318460383
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 64s 7ms/step - loss: 0.9154 - accuracy: 0.8942 - val_loss: 0.8976 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6228217e-02
  7.2127163e-02  1.3891976e-01]
Sparsity at: 0.4915671942060086
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 0.9153 - accuracy: 0.8941 - val_loss: 0.8978 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6473296e-02
  6.9552585e-02  1.3908984e-01]
Sparsity at: 0.4915671942060086
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9153 - accuracy: 0.8939 - val_loss: 0.8974 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6642517e-02
  6.6670500e-02  1.3917950e-01]
Sparsity at: 0.4915671942060086
Epoch 104/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9150 - accuracy: 0.8945 - val_loss: 0.8974 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6675714e-02
  6.4042822e-02  1.3935557e-01]
Sparsity at: 0.4915671942060086
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9151 - accuracy: 0.8941 - val_loss: 0.8974 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6579717e-02
  6.1519664e-02  1.3959299e-01]
Sparsity at: 0.4915671942060086
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9151 - accuracy: 0.8943 - val_loss: 0.8974 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6554638e-02
  5.9072301e-02  1.3962172e-01]
Sparsity at: 0.4915671942060086
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8942 - val_loss: 0.8974 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6636218e-02
  5.6812480e-02  1.3992283e-01]
Sparsity at: 0.4915671942060086
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9150 - accuracy: 0.8942 - val_loss: 0.8976 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6323677e-02
  5.4395251e-02  1.4008930e-01]
Sparsity at: 0.4915671942060086
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8945 - val_loss: 0.8977 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6222729e-02
  5.1991522e-02  1.4038815e-01]
Sparsity at: 0.4915671942060086
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8943 - val_loss: 0.8973 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6267820e-02
  4.9938705e-02  1.4042601e-01]
Sparsity at: 0.4915671942060086
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8942 - val_loss: 0.8974 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6054946e-02
  4.7927070e-02  1.4041717e-01]
Sparsity at: 0.4915671942060086
Epoch 112/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9149 - accuracy: 0.8943 - val_loss: 0.8973 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5910639e-02
  4.5790393e-02  1.4071932e-01]
Sparsity at: 0.4915671942060086
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8944 - val_loss: 0.8974 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5817746e-02
  4.3888167e-02  1.4085016e-01]
Sparsity at: 0.4915671942060086
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9148 - accuracy: 0.8941 - val_loss: 0.8973 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5553839e-02
  4.2069864e-02  1.4092915e-01]
Sparsity at: 0.4915671942060086
Epoch 115/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9148 - accuracy: 0.8946 - val_loss: 0.8972 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5578399e-02
  4.0228777e-02  1.4111276e-01]
Sparsity at: 0.4915671942060086
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9148 - accuracy: 0.8943 - val_loss: 0.8974 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5304684e-02
  3.8610008e-02  1.4145373e-01]
Sparsity at: 0.4915671942060086
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9148 - accuracy: 0.8942 - val_loss: 0.8973 - val_accuracy: 0.9007
[ 4.405955e-34  0.000000e+00 -5.467231e-34 ...  5.525994e-02  3.685068e-02
  1.415453e-01]
Sparsity at: 0.4915671942060086
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9148 - accuracy: 0.8944 - val_loss: 0.8973 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5075899e-02
  3.5304777e-02  1.4175044e-01]
Sparsity at: 0.4915671942060086
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9147 - accuracy: 0.8942 - val_loss: 0.8975 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4805469e-02
  3.3861585e-02  1.4188705e-01]
Sparsity at: 0.4915671942060086
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8942 - val_loss: 0.8974 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4606687e-02
  3.2525089e-02  1.4203407e-01]
Sparsity at: 0.4915671942060086
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9147 - accuracy: 0.8945 - val_loss: 0.8973 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4283284e-02
  3.0963762e-02  1.4215362e-01]
Sparsity at: 0.4915671942060086
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9147 - accuracy: 0.8944 - val_loss: 0.8974 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4055661e-02
  2.9866846e-02  1.4221992e-01]
Sparsity at: 0.4915671942060086
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9147 - accuracy: 0.8945 - val_loss: 0.8972 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3807549e-02
  2.8449036e-02  1.4236511e-01]
Sparsity at: 0.4915671942060086
Epoch 124/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9147 - accuracy: 0.8946 - val_loss: 0.8971 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3616352e-02
  2.7272563e-02  1.4242846e-01]
Sparsity at: 0.4915671942060086
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8943 - val_loss: 0.8969 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3571083e-02
  2.6139388e-02  1.4234842e-01]
Sparsity at: 0.4915671942060086
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9146 - accuracy: 0.8945 - val_loss: 0.8973 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3282775e-02
  2.5216475e-02  1.4247350e-01]
Sparsity at: 0.4915671942060086
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8950 - val_loss: 0.8972 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3037070e-02
  2.4307204e-02  1.4261262e-01]
Sparsity at: 0.4915671942060086
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9147 - accuracy: 0.8946 - val_loss: 0.8973 - val_accuracy: 0.9014
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2930929e-02
  2.3172643e-02  1.4243282e-01]
Sparsity at: 0.4915671942060086
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8947 - val_loss: 0.8971 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2724473e-02
  2.2376856e-02  1.4244777e-01]
Sparsity at: 0.4915671942060086
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9145 - accuracy: 0.8945 - val_loss: 0.8973 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2686278e-02
  2.1544902e-02  1.4248012e-01]
Sparsity at: 0.4915671942060086
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8944 - val_loss: 0.8971 - val_accuracy: 0.9015
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2656170e-02
  2.0726932e-02  1.4237295e-01]
Sparsity at: 0.4915671942060086
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9145 - accuracy: 0.8948 - val_loss: 0.8972 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2697714e-02
  2.0059209e-02  1.4218418e-01]
Sparsity at: 0.4915671942060086
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9145 - accuracy: 0.8946 - val_loss: 0.8973 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2635152e-02
  1.9424204e-02  1.4221667e-01]
Sparsity at: 0.4915671942060086
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8943 - val_loss: 0.8971 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2809089e-02
  1.8891726e-02  1.4193559e-01]
Sparsity at: 0.4915671942060086
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9145 - accuracy: 0.8946 - val_loss: 0.8972 - val_accuracy: 0.9019
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2871533e-02
  1.8544432e-02  1.4174108e-01]
Sparsity at: 0.4915671942060086
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9144 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.2870534e-02
  1.7880075e-02  1.4165451e-01]
Sparsity at: 0.4915671942060086
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9145 - accuracy: 0.8947 - val_loss: 0.8973 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3061351e-02
  1.7460840e-02  1.4147684e-01]
Sparsity at: 0.4915671942060086
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9143 - accuracy: 0.8947 - val_loss: 0.8969 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3087540e-02
  1.7038411e-02  1.4126882e-01]
Sparsity at: 0.4915671942060086
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9143 - accuracy: 0.8949 - val_loss: 0.8972 - val_accuracy: 0.9014
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3198859e-02
  1.6827943e-02  1.4101893e-01]
Sparsity at: 0.4915671942060086
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9143 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9016
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3431567e-02
  1.6531272e-02  1.4078459e-01]
Sparsity at: 0.4915671942060086
Epoch 141/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9144 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3468995e-02
  1.6275905e-02  1.4055875e-01]
Sparsity at: 0.4915671942060086
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9143 - accuracy: 0.8949 - val_loss: 0.8970 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3741660e-02
  1.6146135e-02  1.4027785e-01]
Sparsity at: 0.4915671942060086
Epoch 143/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9142 - accuracy: 0.8948 - val_loss: 0.8972 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.3963501e-02
  1.6009208e-02  1.3991924e-01]
Sparsity at: 0.4915671942060086
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9144 - accuracy: 0.8948 - val_loss: 0.8971 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4272965e-02
  1.5897870e-02  1.3955982e-01]
Sparsity at: 0.4915671942060086
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9143 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4359101e-02
  1.6110793e-02  1.3910906e-01]
Sparsity at: 0.4915671942060086
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9143 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4433070e-02
  1.6081057e-02  1.3880812e-01]
Sparsity at: 0.4915671942060086
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9142 - accuracy: 0.8950 - val_loss: 0.8968 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.4956954e-02
  1.6246514e-02  1.3822816e-01]
Sparsity at: 0.4915671942060086
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9142 - accuracy: 0.8949 - val_loss: 0.8971 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5231649e-02
  1.6460659e-02  1.3785164e-01]
Sparsity at: 0.4915671942060086
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9142 - accuracy: 0.8948 - val_loss: 0.8972 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5452947e-02
  1.6793819e-02  1.3730325e-01]
Sparsity at: 0.4915671942060086
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9142 - accuracy: 0.8949 - val_loss: 0.8971 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.5724651e-02
  1.7057544e-02  1.3676870e-01]
Sparsity at: 0.4915671942060086
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.011272059238444654
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.047124729318109404
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.17600121149900705
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 62s 7ms/step - loss: 0.9141 - accuracy: 0.8947 - val_loss: 0.8968 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6054145e-02
  1.7686464e-02  1.3615209e-01]
Sparsity at: 0.4915671942060086
Epoch 152/500
235/235 [==============================] - 2s 7ms/step - loss: 0.9142 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6345563e-02
  1.8396201e-02  1.3549916e-01]
Sparsity at: 0.4915671942060086
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9141 - accuracy: 0.8947 - val_loss: 0.8968 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.6786802e-02
  1.8911283e-02  1.3490120e-01]
Sparsity at: 0.4915671942060086
Epoch 154/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9140 - accuracy: 0.8948 - val_loss: 0.8970 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.7108004e-02
  1.9623943e-02  1.3436319e-01]
Sparsity at: 0.4915671942060086
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9141 - accuracy: 0.8947 - val_loss: 0.8970 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.7645764e-02
  2.0516187e-02  1.3354596e-01]
Sparsity at: 0.4915671942060086
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.8133692e-02
  2.1392504e-02  1.3299420e-01]
Sparsity at: 0.4915671942060086
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9141 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.8592647e-02
  2.2362266e-02  1.3245267e-01]
Sparsity at: 0.4915671942060086
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8969 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.9214044e-02
  2.3606356e-02  1.3185970e-01]
Sparsity at: 0.4915671942060086
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  5.9788074e-02
  2.4953702e-02  1.3130470e-01]
Sparsity at: 0.4915671942060086
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8967 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.0508434e-02
  2.6316468e-02  1.3071729e-01]
Sparsity at: 0.4915671942060086
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8947 - val_loss: 0.8970 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.1161157e-02
  2.7862763e-02  1.3014981e-01]
Sparsity at: 0.4915671942060086
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.1715826e-02
  2.9348249e-02  1.2966530e-01]
Sparsity at: 0.4915671942060086
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8969 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.2499858e-02
  3.1057177e-02  1.2923661e-01]
Sparsity at: 0.4915671942060086
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8947 - val_loss: 0.8967 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.3296326e-02
  3.2548439e-02  1.2889753e-01]
Sparsity at: 0.4915671942060086
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8947 - val_loss: 0.8966 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.4017601e-02
  3.4157705e-02  1.2861647e-01]
Sparsity at: 0.4915671942060086
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9139 - accuracy: 0.8950 - val_loss: 0.8969 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.4807944e-02
  3.5969306e-02  1.2825690e-01]
Sparsity at: 0.4915671942060086
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9139 - accuracy: 0.8948 - val_loss: 0.8971 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.5431245e-02
  3.7737332e-02  1.2796830e-01]
Sparsity at: 0.4915671942060086
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8947 - val_loss: 0.8969 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.6317514e-02
  3.9380427e-02  1.2777643e-01]
Sparsity at: 0.4915671942060086
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.7055143e-02
  4.1161571e-02  1.2774237e-01]
Sparsity at: 0.4915671942060086
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9137 - accuracy: 0.8951 - val_loss: 0.8965 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.7728989e-02
  4.2971790e-02  1.2749957e-01]
Sparsity at: 0.4915671942060086
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9139 - accuracy: 0.8948 - val_loss: 0.8969 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.8571813e-02
  4.4474203e-02  1.2742308e-01]
Sparsity at: 0.4915671942060086
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9137 - accuracy: 0.8950 - val_loss: 0.8967 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  6.9410764e-02
  4.6099026e-02  1.2742662e-01]
Sparsity at: 0.4915671942060086
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8950 - val_loss: 0.8968 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.0111036e-02
  4.7776178e-02  1.2732854e-01]
Sparsity at: 0.4915671942060086
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9137 - accuracy: 0.8949 - val_loss: 0.8966 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.0877142e-02
  4.9108785e-02  1.2724498e-01]
Sparsity at: 0.4915671942060086
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9137 - accuracy: 0.8952 - val_loss: 0.8963 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.1631581e-02
  5.0725736e-02  1.2713771e-01]
Sparsity at: 0.4915671942060086
Epoch 176/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9138 - accuracy: 0.8949 - val_loss: 0.8966 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.2293870e-02
  5.2178476e-02  1.2712021e-01]
Sparsity at: 0.4915671942060086
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9136 - accuracy: 0.8949 - val_loss: 0.8964 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.3063195e-02
  5.3607959e-02  1.2722716e-01]
Sparsity at: 0.4915671942060086
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8947 - val_loss: 0.8964 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.3784754e-02
  5.4916345e-02  1.2724178e-01]
Sparsity at: 0.4915671942060086
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9137 - accuracy: 0.8951 - val_loss: 0.8965 - val_accuracy: 0.9005
[ 4.405955e-34  0.000000e+00 -5.467231e-34 ...  7.458435e-02  5.620430e-02
  1.273499e-01]
Sparsity at: 0.4915671942060086
Epoch 180/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8948 - val_loss: 0.8964 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.5433187e-02
  5.7797886e-02  1.2731324e-01]
Sparsity at: 0.4915671942060086
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9136 - accuracy: 0.8949 - val_loss: 0.8964 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.6093622e-02
  5.8917698e-02  1.2761958e-01]
Sparsity at: 0.4915671942060086
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9136 - accuracy: 0.8949 - val_loss: 0.8966 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.6895744e-02
  6.0469348e-02  1.2761772e-01]
Sparsity at: 0.4915671942060086
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9135 - accuracy: 0.8948 - val_loss: 0.8964 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.7709392e-02
  6.1416328e-02  1.2761122e-01]
Sparsity at: 0.4915671942060086
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8949 - val_loss: 0.8963 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.8367203e-02
  6.2364485e-02  1.2780271e-01]
Sparsity at: 0.4915671942060086
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8949 - val_loss: 0.8965 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  7.9302244e-02
  6.3584983e-02  1.2775308e-01]
Sparsity at: 0.4915671942060086
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8948 - val_loss: 0.8963 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.0140516e-02
  6.4463928e-02  1.2790546e-01]
Sparsity at: 0.4915671942060086
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9135 - accuracy: 0.8949 - val_loss: 0.8963 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.0962680e-02
  6.5393746e-02  1.2793745e-01]
Sparsity at: 0.4915671942060086
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9136 - accuracy: 0.8946 - val_loss: 0.8964 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.1871778e-02
  6.6387676e-02  1.2778683e-01]
Sparsity at: 0.4915671942060086
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8948 - val_loss: 0.8964 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.2790025e-02
  6.7185014e-02  1.2786651e-01]
Sparsity at: 0.4915671942060086
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9134 - accuracy: 0.8948 - val_loss: 0.8961 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.3899997e-02
  6.7903578e-02  1.2774087e-01]
Sparsity at: 0.4915671942060086
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9134 - accuracy: 0.8946 - val_loss: 0.8961 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.4890626e-02
  6.8697125e-02  1.2766592e-01]
Sparsity at: 0.4915671942060086
Epoch 192/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9134 - accuracy: 0.8947 - val_loss: 0.8960 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.5895844e-02
  6.9202006e-02  1.2756491e-01]
Sparsity at: 0.4915671942060086
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9133 - accuracy: 0.8946 - val_loss: 0.8960 - val_accuracy: 0.9011
[ 4.405955e-34  0.000000e+00 -5.467231e-34 ...  8.709497e-02  6.971825e-02
  1.274270e-01]
Sparsity at: 0.4915671942060086
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9133 - accuracy: 0.8948 - val_loss: 0.8962 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.8182107e-02
  7.0219204e-02  1.2749338e-01]
Sparsity at: 0.4915671942060086
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9132 - accuracy: 0.8946 - val_loss: 0.8960 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  8.9228794e-02
  7.0718624e-02  1.2731257e-01]
Sparsity at: 0.4915671942060086
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9133 - accuracy: 0.8947 - val_loss: 0.8959 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.0439849e-02
  7.0813298e-02  1.2718016e-01]
Sparsity at: 0.4915671942060086
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9131 - accuracy: 0.8949 - val_loss: 0.8958 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.1443099e-02
  7.1120374e-02  1.2709697e-01]
Sparsity at: 0.4915671942060086
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9132 - accuracy: 0.8946 - val_loss: 0.8959 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.2552729e-02
  7.1526386e-02  1.2699056e-01]
Sparsity at: 0.4915671942060086
Epoch 199/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9130 - accuracy: 0.8951 - val_loss: 0.8960 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.3717016e-02
  7.1717285e-02  1.2683336e-01]
Sparsity at: 0.4915671942060086
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9132 - accuracy: 0.8948 - val_loss: 0.8959 - val_accuracy: 0.9008
[ 4.405955e-34  0.000000e+00 -5.467231e-34 ...  9.467285e-02  7.201058e-02
  1.266109e-01]
Sparsity at: 0.4915671942060086
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.02142395043733636
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.07333939304789183
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.20022020719093092
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 65s 7ms/step - loss: 0.9130 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.5643237e-02
  7.2388552e-02  1.2626694e-01]
Sparsity at: 0.4915671942060086
Epoch 202/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9131 - accuracy: 0.8948 - val_loss: 0.8960 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.6175291e-02
  7.2803080e-02  1.2633914e-01]
Sparsity at: 0.4915671942060086
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9131 - accuracy: 0.8947 - val_loss: 0.8959 - val_accuracy: 0.9014
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.6814089e-02
  7.3377401e-02  1.2611035e-01]
Sparsity at: 0.4915671942060086
Epoch 204/500
235/235 [==============================] - 2s 10ms/step - loss: 0.9130 - accuracy: 0.8946 - val_loss: 0.8960 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.7325146e-02
  7.3983535e-02  1.2609383e-01]
Sparsity at: 0.4915671942060086
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9130 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.8106608e-02
  7.4429415e-02  1.2567812e-01]
Sparsity at: 0.4915671942060086
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9130 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9012
[ 4.405955e-34  0.000000e+00 -5.467231e-34 ...  9.853930e-02  7.494602e-02
  1.254700e-01]
Sparsity at: 0.4915671942060086
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9016
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.8895095e-02
  7.5431503e-02  1.2535639e-01]
Sparsity at: 0.4915671942060086
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.9103086e-02
  7.5900957e-02  1.2523769e-01]
Sparsity at: 0.4915671942060086
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.9522829e-02
  7.6486997e-02  1.2509894e-01]
Sparsity at: 0.4915671942060086
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  9.9636301e-02
  7.6907367e-02  1.2499323e-01]
Sparsity at: 0.4915671942060086
Epoch 211/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9130 - accuracy: 0.8946 - val_loss: 0.8958 - val_accuracy: 0.9016
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  9.98834074e-02
  7.73103684e-02  1.24766596e-01]
Sparsity at: 0.4915671942060086
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9013
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  9.99359787e-02
  7.76677951e-02  1.24696344e-01]
Sparsity at: 0.4915671942060086
Epoch 213/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9129 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0002642e-01
  7.8020290e-02  1.2447584e-01]
Sparsity at: 0.4915671942060086
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8948 - val_loss: 0.8958 - val_accuracy: 0.9014
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0027917e-01
  7.8364708e-02  1.2443490e-01]
Sparsity at: 0.4915671942060086
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8949 - val_loss: 0.8957 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.00300886e-01
  7.87917227e-02  1.24289595e-01]
Sparsity at: 0.4915671942060086
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9015
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0065176e-01
  7.9192631e-02  1.2408627e-01]
Sparsity at: 0.4915671942060086
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.00588962e-01
  7.93458298e-02  1.24147326e-01]
Sparsity at: 0.4915671942060086
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9015
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0052017e-01
  7.9670958e-02  1.2412000e-01]
Sparsity at: 0.4915671942060086
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0066597e-01
  8.0094285e-02  1.2397536e-01]
Sparsity at: 0.4915671942060086
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.00808665e-01
  8.05042386e-02  1.23935439e-01]
Sparsity at: 0.4915671942060086
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9129 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9016
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0085063e-01
  8.0734372e-02  1.2387672e-01]
Sparsity at: 0.4915671942060086
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9011
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.00862525e-01
  8.12308267e-02  1.23742156e-01]
Sparsity at: 0.4915671942060086
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0105536e-01
  8.1420235e-02  1.2378503e-01]
Sparsity at: 0.4915671942060086
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8945 - val_loss: 0.8956 - val_accuracy: 0.9016
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0093520e-01
  8.1815019e-02  1.2359646e-01]
Sparsity at: 0.4915671942060086
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8958 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0098380e-01
  8.2336642e-02  1.2359604e-01]
Sparsity at: 0.4915671942060086
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0101691e-01
  8.2467802e-02  1.2371648e-01]
Sparsity at: 0.4915671942060086
Epoch 227/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9012
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01043351e-01
  8.28581974e-02  1.23542406e-01]
Sparsity at: 0.4915671942060086
Epoch 228/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9014
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0108567e-01
  8.3382219e-02  1.2341244e-01]
Sparsity at: 0.4915671942060086
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8945 - val_loss: 0.8953 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0112931e-01
  8.3609983e-02  1.2342407e-01]
Sparsity at: 0.4915671942060086
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0124653e-01
  8.4036008e-02  1.2346753e-01]
Sparsity at: 0.4915671942060086
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9015
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0099008e-01
  8.4256522e-02  1.2343711e-01]
Sparsity at: 0.4915671942060086
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0116735e-01
  8.4691726e-02  1.2329346e-01]
Sparsity at: 0.4915671942060086
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8945 - val_loss: 0.8955 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0120549e-01
  8.5029110e-02  1.2320056e-01]
Sparsity at: 0.4915671942060086
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9013
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01291597e-01
  8.54547918e-02  1.23075925e-01]
Sparsity at: 0.4915671942060086
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8954 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0128099e-01
  8.5901454e-02  1.2312485e-01]
Sparsity at: 0.4915671942060086
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9012
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01410605e-01
  8.63594040e-02  1.23156317e-01]
Sparsity at: 0.4915671942060086
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9014
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0127239e-01
  8.6523212e-02  1.2310546e-01]
Sparsity at: 0.4915671942060086
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8950 - val_loss: 0.8954 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0131819e-01
  8.7081045e-02  1.2302668e-01]
Sparsity at: 0.4915671942060086
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9007
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01451673e-01
  8.74959975e-02  1.23043574e-01]
Sparsity at: 0.4915671942060086
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9129 - accuracy: 0.8945 - val_loss: 0.8955 - val_accuracy: 0.9015
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0131886e-01
  8.7783560e-02  1.2300963e-01]
Sparsity at: 0.4915671942060086
Epoch 241/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0142950e-01
  8.8123880e-02  1.2293459e-01]
Sparsity at: 0.4915671942060086
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8954 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0151721e-01
  8.8582717e-02  1.2286842e-01]
Sparsity at: 0.4915671942060086
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9011
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01410329e-01
  8.89054686e-02  1.22809015e-01]
Sparsity at: 0.4915671942060086
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01508334e-01
  8.91791210e-02  1.22680791e-01]
Sparsity at: 0.4915671942060086
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9011
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01475626e-01
  8.96460265e-02  1.22691609e-01]
Sparsity at: 0.4915671942060086
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0157552e-01
  8.9875020e-02  1.2268874e-01]
Sparsity at: 0.4915671942060086
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0180477e-01
  9.0367652e-02  1.2257202e-01]
Sparsity at: 0.4915671942060086
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8944 - val_loss: 0.8957 - val_accuracy: 0.9015
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01682864e-01
  9.07195807e-02  1.22651353e-01]
Sparsity at: 0.4915671942060086
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8958 - val_accuracy: 0.9011
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01685569e-01
  9.11051631e-02  1.22737736e-01]
Sparsity at: 0.4915671942060086
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.01910874e-01
  9.16255713e-02  1.22500703e-01]
Sparsity at: 0.4915671942060086
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.03567400540859422
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.10840489712948642
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.2288291585369837
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 62s 7ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0193768e-01
  9.1981292e-02  1.2230841e-01]
Sparsity at: 0.4915671942060086
Epoch 252/500
235/235 [==============================] - 2s 7ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0206948e-01
  9.2079706e-02  1.2229652e-01]
Sparsity at: 0.4915671942060086
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9013
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.02129921e-01
  9.26473439e-02  1.22345895e-01]
Sparsity at: 0.4915671942060086
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0225308e-01
  9.2909552e-02  1.2225984e-01]
Sparsity at: 0.4915671942060086
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0231085e-01
  9.3630522e-02  1.2209938e-01]
Sparsity at: 0.4915671942060086
Epoch 256/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.02329940e-01
  9.38648432e-02  1.22281715e-01]
Sparsity at: 0.4915671942060086
Epoch 257/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0247041e-01
  9.4468139e-02  1.2212482e-01]
Sparsity at: 0.4915671942060086
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0255352e-01
  9.4962858e-02  1.2207687e-01]
Sparsity at: 0.4915671942060086
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8958 - val_accuracy: 0.9014
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0262582e-01
  9.5325835e-02  1.2199159e-01]
Sparsity at: 0.4915671942060086
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8953 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0286401e-01
  9.5675878e-02  1.2184068e-01]
Sparsity at: 0.4915671942060086
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9012
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.02975592e-01
  9.63489413e-02  1.21847324e-01]
Sparsity at: 0.4915671942060086
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0299555e-01
  9.6655533e-02  1.2181367e-01]
Sparsity at: 0.4915671942060086
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9012
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0316072e-01
  9.7041480e-02  1.2177449e-01]
Sparsity at: 0.4915671942060086
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0326288e-01
  9.7469062e-02  1.2172752e-01]
Sparsity at: 0.4915671942060086
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0324152e-01
  9.7889498e-02  1.2173858e-01]
Sparsity at: 0.4915671942060086
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9009
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.03435636e-01
  9.84170362e-02  1.21615730e-01]
Sparsity at: 0.4915671942060086
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0370015e-01
  9.8744400e-02  1.2147219e-01]
Sparsity at: 0.4915671942060086
Epoch 268/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8958 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.03738651e-01
  9.92396399e-02  1.21449396e-01]
Sparsity at: 0.4915671942060086
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0385159e-01
  9.9807292e-02  1.2134983e-01]
Sparsity at: 0.4915671942060086
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8949 - val_loss: 0.8957 - val_accuracy: 0.9009
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.04043342e-01
  1.00297436e-01  1.21235147e-01]
Sparsity at: 0.4915671942060086
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9009
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.04139872e-01
  1.00725181e-01  1.21209964e-01]
Sparsity at: 0.4915671942060086
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9011
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.04165480e-01
  1.01247355e-01  1.21191636e-01]
Sparsity at: 0.4915671942060086
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0432126e-01
  1.0163329e-01  1.2100831e-01]
Sparsity at: 0.4915671942060086
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0444409e-01
  1.0228456e-01  1.2087474e-01]
Sparsity at: 0.4915671942060086
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0457735e-01
  1.0251811e-01  1.2098795e-01]
Sparsity at: 0.4915671942060086
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0482736e-01
  1.0305520e-01  1.2075957e-01]
Sparsity at: 0.4915671942060086
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.04805402e-01
  1.03636585e-01  1.20822832e-01]
Sparsity at: 0.4915671942060086
Epoch 278/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.05027564e-01
  1.04056209e-01  1.20698892e-01]
Sparsity at: 0.4915671942060086
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0508188e-01
  1.0448680e-01  1.2060012e-01]
Sparsity at: 0.4915671942060086
Epoch 280/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8958 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0514281e-01
  1.0482825e-01  1.2069760e-01]
Sparsity at: 0.4915671942060086
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9009
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.05385736e-01
  1.05611682e-01  1.20322876e-01]
Sparsity at: 0.4915671942060086
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9012
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.05653800e-01
  1.06095769e-01  1.20253384e-01]
Sparsity at: 0.4915671942060086
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0561259e-01
  1.0639681e-01  1.2029745e-01]
Sparsity at: 0.4915671942060086
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8953 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.05671056e-01
  1.07204944e-01  1.20195039e-01]
Sparsity at: 0.4915671942060086
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.05923966e-01
  1.07656084e-01  1.20007195e-01]
Sparsity at: 0.4915671942060086
Epoch 286/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0596972e-01
  1.0785523e-01  1.2013594e-01]
Sparsity at: 0.4915671942060086
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0609634e-01
  1.0846401e-01  1.1990883e-01]
Sparsity at: 0.4915671942060086
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9005
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.06263541e-01
  1.09087124e-01  1.19762152e-01]
Sparsity at: 0.4915671942060086
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0631212e-01
  1.0936940e-01  1.1964775e-01]
Sparsity at: 0.4915671942060086
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9009
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.06415585e-01
  1.09851293e-01  1.19658023e-01]
Sparsity at: 0.4915671942060086
Epoch 291/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9005
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.06537998e-01
  1.10336587e-01  1.19548656e-01]
Sparsity at: 0.4915671942060086
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.06684104e-01
  1.10930219e-01  1.19353995e-01]
Sparsity at: 0.4915671942060086
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0679175e-01
  1.1107905e-01  1.1925575e-01]
Sparsity at: 0.4915671942060086
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.06935956e-01
  1.11754924e-01  1.19206458e-01]
Sparsity at: 0.4915671942060086
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0706040e-01
  1.1210678e-01  1.1920955e-01]
Sparsity at: 0.4915671942060086
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.07080966e-01
  1.12800702e-01  1.19147345e-01]
Sparsity at: 0.4915671942060086
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.07189715e-01
  1.13190606e-01  1.19042031e-01]
Sparsity at: 0.4915671942060086
Epoch 298/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0742079e-01
  1.1358772e-01  1.1894304e-01]
Sparsity at: 0.4915671942060086
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0737643e-01
  1.1401457e-01  1.1899469e-01]
Sparsity at: 0.4915671942060086
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8950 - val_loss: 0.8956 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0752595e-01
  1.1449818e-01  1.1894162e-01]
Sparsity at: 0.4915671942060086
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.051622290096579704
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.147530256526327
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.2550637599594445
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 60s 7ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.07758895e-01
  1.15061432e-01  1.18676923e-01]
Sparsity at: 0.4915671942060086
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 0.9127 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.07817464e-01
  1.15632489e-01  1.18681155e-01]
Sparsity at: 0.4915671942060086
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0803751e-01
  1.1613247e-01  1.1854644e-01]
Sparsity at: 0.4915671942060086
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.07999966e-01
  1.16644174e-01  1.18464328e-01]
Sparsity at: 0.4915671942060086
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08318292e-01
  1.17018491e-01  1.18299626e-01]
Sparsity at: 0.4915671942060086
Epoch 306/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0838743e-01
  1.1762427e-01  1.1830912e-01]
Sparsity at: 0.4915671942060086
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0837681e-01
  1.1816705e-01  1.1830547e-01]
Sparsity at: 0.4915671942060086
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0871011e-01
  1.1861624e-01  1.1824350e-01]
Sparsity at: 0.4915671942060086
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9007
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08843051e-01
  1.19321309e-01  1.18006304e-01]
Sparsity at: 0.4915671942060086
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08809650e-01
  1.19755886e-01  1.17996745e-01]
Sparsity at: 0.4915671942060086
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0885896e-01
  1.2025493e-01  1.1791449e-01]
Sparsity at: 0.4915671942060086
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9007
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09169096e-01
  1.20747305e-01  1.17802642e-01]
Sparsity at: 0.4915671942060086
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09158628e-01
  1.21064760e-01  1.17603354e-01]
Sparsity at: 0.4915671942060086
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8949 - val_loss: 0.8954 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09486565e-01
  1.21527143e-01  1.17681272e-01]
Sparsity at: 0.4915671942060086
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0946308e-01
  1.2224540e-01  1.1752497e-01]
Sparsity at: 0.4915671942060086
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8958 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09518081e-01
  1.22784026e-01  1.17358431e-01]
Sparsity at: 0.4915671942060086
Epoch 317/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09624304e-01
  1.23014234e-01  1.17214233e-01]
Sparsity at: 0.4915671942060086
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09744117e-01
  1.23686805e-01  1.17059000e-01]
Sparsity at: 0.4915671942060086
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0969191e-01
  1.2420504e-01  1.1713395e-01]
Sparsity at: 0.4915671942060086
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9009
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09801918e-01
  1.24639109e-01  1.16956644e-01]
Sparsity at: 0.4915671942060086
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1000070e-01
  1.2523420e-01  1.1696241e-01]
Sparsity at: 0.4915671942060086
Epoch 322/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8953 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1020854e-01
  1.2572798e-01  1.1684162e-01]
Sparsity at: 0.4915671942060086
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9124 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9013
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1023498e-01
  1.2618518e-01  1.1676814e-01]
Sparsity at: 0.4915671942060086
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8949 - val_loss: 0.8953 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1037652e-01
  1.2673737e-01  1.1659724e-01]
Sparsity at: 0.4915671942060086
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8945 - val_loss: 0.8956 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1037357e-01
  1.2729448e-01  1.1649353e-01]
Sparsity at: 0.4915671942060086
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8954 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1064157e-01
  1.2759124e-01  1.1649286e-01]
Sparsity at: 0.4915671942060086
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8945 - val_loss: 0.8956 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.10592104e-01
  1.28157154e-01  1.16475947e-01]
Sparsity at: 0.4915671942060086
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9010
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1068795e-01
  1.2858152e-01  1.1633609e-01]
Sparsity at: 0.4915671942060086
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1090289e-01
  1.2913407e-01  1.1637783e-01]
Sparsity at: 0.4915671942060086
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1086357e-01
  1.2948421e-01  1.1623690e-01]
Sparsity at: 0.4915671942060086
Epoch 331/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9007
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.10820495e-01
  1.29991978e-01  1.16205022e-01]
Sparsity at: 0.4915671942060086
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8954 - val_accuracy: 0.9009
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.10960357e-01
  1.30195558e-01  1.15987465e-01]
Sparsity at: 0.4915671942060086
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1086346e-01
  1.3063511e-01  1.1604190e-01]
Sparsity at: 0.4915671942060086
Epoch 334/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9126 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11039855e-01
  1.31049052e-01  1.15849726e-01]
Sparsity at: 0.4915671942060086
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9005
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11139596e-01
  1.31488129e-01  1.15869932e-01]
Sparsity at: 0.4915671942060086
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8953 - val_accuracy: 0.9009
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11132160e-01
  1.31560966e-01  1.15874745e-01]
Sparsity at: 0.4915671942060086
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1119732e-01
  1.3194750e-01  1.1579317e-01]
Sparsity at: 0.4915671942060086
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9011
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1132523e-01
  1.3218549e-01  1.1586948e-01]
Sparsity at: 0.4915671942060086
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1147601e-01
  1.3257197e-01  1.1571090e-01]
Sparsity at: 0.4915671942060086
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9124 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1138895e-01
  1.3285929e-01  1.1575279e-01]
Sparsity at: 0.4915671942060086
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1134855e-01
  1.3309039e-01  1.1576089e-01]
Sparsity at: 0.4915671942060086
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1156576e-01
  1.3345060e-01  1.1556832e-01]
Sparsity at: 0.4915671942060086
Epoch 343/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1170391e-01
  1.3358210e-01  1.1569683e-01]
Sparsity at: 0.4915671942060086
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8945 - val_loss: 0.8955 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1187138e-01
  1.3370660e-01  1.1573568e-01]
Sparsity at: 0.4915671942060086
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9010
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11819856e-01
  1.34035230e-01  1.15782276e-01]
Sparsity at: 0.4915671942060086
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9124 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11917645e-01
  1.34417862e-01  1.15639441e-01]
Sparsity at: 0.4915671942060086
Epoch 347/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1198719e-01
  1.3452066e-01  1.1570860e-01]
Sparsity at: 0.4915671942060086
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1218482e-01
  1.3460460e-01  1.1563653e-01]
Sparsity at: 0.4915671942060086
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9126 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12264901e-01
  1.34759083e-01  1.15506575e-01]
Sparsity at: 0.4915671942060086
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1247416e-01
  1.3507682e-01  1.1557629e-01]
Sparsity at: 0.4915671942060086
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.06464418657801829
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.17392093214396276
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.2729203704960028
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 61s 7ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12512529e-01
  1.35305703e-01  1.15505144e-01]
Sparsity at: 0.4915671942060086
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 0.9124 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1249515e-01
  1.3555111e-01  1.1568865e-01]
Sparsity at: 0.4915671942060086
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1251154e-01
  1.3596642e-01  1.1554975e-01]
Sparsity at: 0.4915671942060086
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8953 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11760825e-01
  1.36150450e-01  1.15658097e-01]
Sparsity at: 0.4915671942060086
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8953 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1092494e-01
  1.3685745e-01  1.1561877e-01]
Sparsity at: 0.4915671942060086
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9124 - accuracy: 0.8945 - val_loss: 0.8955 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1037842e-01
  1.3755278e-01  1.1556570e-01]
Sparsity at: 0.4915671942060086
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9123 - accuracy: 0.8947 - val_loss: 0.8952 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0954408e-01
  1.3820693e-01  1.1617465e-01]
Sparsity at: 0.4915671942060086
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9123 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9009
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0918721e-01
  1.3900797e-01  1.1646126e-01]
Sparsity at: 0.4915671942060086
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9124 - accuracy: 0.8945 - val_loss: 0.8953 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0886470e-01
  1.3915515e-01  1.1706115e-01]
Sparsity at: 0.4915671942060086
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9123 - accuracy: 0.8944 - val_loss: 0.8953 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0861417e-01
  1.3923559e-01  1.1778208e-01]
Sparsity at: 0.4915671942060086
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9123 - accuracy: 0.8949 - val_loss: 0.8954 - val_accuracy: 0.9003
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08452991e-01
  1.39544755e-01  1.18494906e-01]
Sparsity at: 0.4915671942060086
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8946 - val_loss: 0.8952 - val_accuracy: 0.9005
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08494103e-01
  1.39522269e-01  1.19152054e-01]
Sparsity at: 0.4915671942060086
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8950 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08533636e-01
  1.39399260e-01  1.19898595e-01]
Sparsity at: 0.4915671942060086
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8950 - val_loss: 0.8953 - val_accuracy: 0.9002
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08377568e-01
  1.39133841e-01  1.20760426e-01]
Sparsity at: 0.4915671942060086
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8950 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0847210e-01
  1.3908175e-01  1.2122020e-01]
Sparsity at: 0.4915671942060086
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8951 - val_accuracy: 0.9003
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08458422e-01
  1.38681337e-01  1.21884786e-01]
Sparsity at: 0.4915671942060086
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8948 - val_loss: 0.8951 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0864188e-01
  1.3845906e-01  1.2266414e-01]
Sparsity at: 0.4915671942060086
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0867066e-01
  1.3789645e-01  1.2318372e-01]
Sparsity at: 0.4915671942060086
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8951 - val_accuracy: 0.9008
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08703814e-01
  1.37773827e-01  1.23803139e-01]
Sparsity at: 0.4915671942060086
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9123 - accuracy: 0.8944 - val_loss: 0.8951 - val_accuracy: 0.9006
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0864734e-01
  1.3748807e-01  1.2438256e-01]
Sparsity at: 0.4915671942060086
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8948 - val_loss: 0.8950 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0881177e-01
  1.3706860e-01  1.2507282e-01]
Sparsity at: 0.4915671942060086
Epoch 372/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8951 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0877953e-01
  1.3662153e-01  1.2551926e-01]
Sparsity at: 0.4915671942060086
Epoch 373/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08853444e-01
  1.36136845e-01  1.26017019e-01]
Sparsity at: 0.4915671942060086
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8950 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0883986e-01
  1.3574101e-01  1.2662289e-01]
Sparsity at: 0.4915671942060086
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8948 - val_loss: 0.8952 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0883737e-01
  1.3554654e-01  1.2709263e-01]
Sparsity at: 0.4915671942060086
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8945 - val_loss: 0.8951 - val_accuracy: 0.9008
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0889638e-01
  1.3493958e-01  1.2757799e-01]
Sparsity at: 0.4915671942060086
Epoch 377/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0902181e-01
  1.3455978e-01  1.2791066e-01]
Sparsity at: 0.4915671942060086
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8948 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0900174e-01
  1.3396764e-01  1.2835996e-01]
Sparsity at: 0.4915671942060086
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8951 - val_accuracy: 0.9007
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08929954e-01
  1.33672997e-01  1.28733471e-01]
Sparsity at: 0.4915671942060086
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8949 - val_loss: 0.8949 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0900377e-01
  1.3313754e-01  1.2911968e-01]
Sparsity at: 0.4915671942060086
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8943 - val_loss: 0.8949 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0926257e-01
  1.3266259e-01  1.2954485e-01]
Sparsity at: 0.4915671942060086
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0911893e-01
  1.3231327e-01  1.2982981e-01]
Sparsity at: 0.4915671942060086
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0913721e-01
  1.3168851e-01  1.3027328e-01]
Sparsity at: 0.4915671942060086
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.9005
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08965725e-01
  1.31370366e-01  1.30558357e-01]
Sparsity at: 0.4915671942060086
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8947 - val_loss: 0.8948 - val_accuracy: 0.9007
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0908806e-01
  1.3100716e-01  1.3083160e-01]
Sparsity at: 0.4915671942060086
Epoch 386/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8947 - val_loss: 0.8950 - val_accuracy: 0.9005
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09178655e-01
  1.30139247e-01  1.31272107e-01]
Sparsity at: 0.4915671942060086
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8948 - val_accuracy: 0.9005
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09355226e-01
  1.29827857e-01  1.31579965e-01]
Sparsity at: 0.4915671942060086
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8948 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0929737e-01
  1.2937720e-01  1.3174893e-01]
Sparsity at: 0.4915671942060086
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8948 - val_loss: 0.8951 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09133005e-01
  1.29096195e-01  1.32233441e-01]
Sparsity at: 0.4915671942060086
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9122 - accuracy: 0.8944 - val_loss: 0.8949 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0928974e-01
  1.2864800e-01  1.3234875e-01]
Sparsity at: 0.4915671942060086
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8943 - val_loss: 0.8949 - val_accuracy: 0.9002
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09321244e-01
  1.28218085e-01  1.32692069e-01]
Sparsity at: 0.4915671942060086
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8949 - val_loss: 0.8950 - val_accuracy: 0.9005
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0941839e-01
  1.2815791e-01  1.3303331e-01]
Sparsity at: 0.4915671942060086
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9005
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09406024e-01
  1.27596900e-01  1.33089453e-01]
Sparsity at: 0.4915671942060086
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8944 - val_loss: 0.8951 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0937726e-01
  1.2706323e-01  1.3348898e-01]
Sparsity at: 0.4915671942060086
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0944096e-01
  1.2684163e-01  1.3357668e-01]
Sparsity at: 0.4915671942060086
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8948 - val_loss: 0.8950 - val_accuracy: 0.9001
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09369345e-01
  1.26328841e-01  1.33864865e-01]
Sparsity at: 0.4915671942060086
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8952 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0933044e-01
  1.2582973e-01  1.3420562e-01]
Sparsity at: 0.4915671942060086
Epoch 398/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0932508e-01
  1.2561536e-01  1.3422437e-01]
Sparsity at: 0.4915671942060086
Epoch 399/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8949 - val_loss: 0.8948 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0955639e-01
  1.2529141e-01  1.3427687e-01]
Sparsity at: 0.4915671942060086
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8944 - val_loss: 0.8950 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0962675e-01
  1.2508957e-01  1.3459891e-01]
Sparsity at: 0.4915671942060086
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.07383941830103513
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.18945745993156926
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.28026493361681304
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 60s 7ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0950876e-01
  1.2468032e-01  1.3476837e-01]
Sparsity at: 0.4915671942060086
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 0.9121 - accuracy: 0.8944 - val_loss: 0.8947 - val_accuracy: 0.9001
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09496504e-01
  1.24136597e-01  1.35057911e-01]
Sparsity at: 0.4915671942060086
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8946 - val_loss: 0.8948 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0942746e-01
  1.2391216e-01  1.3511725e-01]
Sparsity at: 0.4915671942060086
Epoch 404/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0947028e-01
  1.2344939e-01  1.3551536e-01]
Sparsity at: 0.4915671942060086
Epoch 405/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9002
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09548874e-01
  1.23096377e-01  1.35503218e-01]
Sparsity at: 0.4915671942060086
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0957327e-01
  1.2285333e-01  1.3573907e-01]
Sparsity at: 0.4915671942060086
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0974342e-01
  1.2252984e-01  1.3586804e-01]
Sparsity at: 0.4915671942060086
Epoch 408/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8950 - val_accuracy: 0.9002
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09529182e-01
  1.22167945e-01  1.36147320e-01]
Sparsity at: 0.4915671942060086
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8950 - val_accuracy: 0.9001
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09413795e-01
  1.21915489e-01  1.36264831e-01]
Sparsity at: 0.4915671942060086
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8944 - val_loss: 0.8950 - val_accuracy: 0.9004
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0943275e-01
  1.2133632e-01  1.3632475e-01]
Sparsity at: 0.4915671942060086
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8944 - val_loss: 0.8948 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0952730e-01
  1.2110749e-01  1.3671531e-01]
Sparsity at: 0.4915671942060086
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8949 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0959575e-01
  1.2089787e-01  1.3675423e-01]
Sparsity at: 0.4915671942060086
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8945 - val_loss: 0.8951 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0964599e-01
  1.2085825e-01  1.3678238e-01]
Sparsity at: 0.4915671942060086
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.8996
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09560207e-01
  1.20630205e-01  1.37018502e-01]
Sparsity at: 0.4915671942060086
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8942 - val_loss: 0.8949 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0963194e-01
  1.2019518e-01  1.3712516e-01]
Sparsity at: 0.4915671942060086
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.8999
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09601811e-01
  1.19841814e-01  1.37319714e-01]
Sparsity at: 0.4915671942060086
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8943 - val_loss: 0.8949 - val_accuracy: 0.8999
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09643474e-01
  1.19666681e-01  1.37418300e-01]
Sparsity at: 0.4915671942060086
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.9003
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09552771e-01
  1.19338654e-01  1.37683675e-01]
Sparsity at: 0.4915671942060086
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.9006
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09576553e-01
  1.19024254e-01  1.37618184e-01]
Sparsity at: 0.4915671942060086
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8947 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0933009e-01
  1.1889714e-01  1.3758585e-01]
Sparsity at: 0.4915671942060086
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8944 - val_loss: 0.8948 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0902565e-01
  1.1865483e-01  1.3757011e-01]
Sparsity at: 0.4915671942060086
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8944 - val_loss: 0.8948 - val_accuracy: 0.9003
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08818509e-01
  1.18420124e-01  1.37709722e-01]
Sparsity at: 0.4915671942060086
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8950 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0855886e-01
  1.1823586e-01  1.3765867e-01]
Sparsity at: 0.4915671942060086
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8948 - val_accuracy: 0.9002
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08361080e-01
  1.17880754e-01  1.37751326e-01]
Sparsity at: 0.4915671942060086
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8946 - val_loss: 0.8947 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0831153e-01
  1.1752823e-01  1.3778915e-01]
Sparsity at: 0.4915671942060086
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8944 - val_loss: 0.8947 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0801212e-01
  1.1744972e-01  1.3776982e-01]
Sparsity at: 0.4915671942060086
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.8998
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08038604e-01
  1.17239520e-01  1.37915060e-01]
Sparsity at: 0.4915671942060086
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8946 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0797276e-01
  1.1678814e-01  1.3793471e-01]
Sparsity at: 0.4915671942060086
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8942 - val_loss: 0.8947 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0784755e-01
  1.1667929e-01  1.3808033e-01]
Sparsity at: 0.4915671942060086
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8946 - val_loss: 0.8947 - val_accuracy: 0.8996
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.07891709e-01
  1.16176106e-01  1.38276950e-01]
Sparsity at: 0.4915671942060086
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8941 - val_loss: 0.8947 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0777372e-01
  1.1622778e-01  1.3832834e-01]
Sparsity at: 0.4915671942060086
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8947 - val_loss: 0.8949 - val_accuracy: 0.8997
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.07734695e-01
  1.15933731e-01  1.38471559e-01]
Sparsity at: 0.4915671942060086
Epoch 433/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8943 - val_loss: 0.8946 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0767491e-01
  1.1549077e-01  1.3848276e-01]
Sparsity at: 0.4915671942060086
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8944 - val_loss: 0.8946 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0776126e-01
  1.1534305e-01  1.3877386e-01]
Sparsity at: 0.4915671942060086
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8947 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0782883e-01
  1.1485077e-01  1.3878444e-01]
Sparsity at: 0.4915671942060086
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.9000
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0795109e-01
  1.1452932e-01  1.3872072e-01]
Sparsity at: 0.4915671942060086
Epoch 437/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8943 - val_loss: 0.8943 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0794974e-01
  1.1419043e-01  1.3889796e-01]
Sparsity at: 0.4915671942060086
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8948 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0776338e-01
  1.1398324e-01  1.3911685e-01]
Sparsity at: 0.4915671942060086
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8948 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0796861e-01
  1.1360106e-01  1.3928226e-01]
Sparsity at: 0.4915671942060086
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.9002
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08137324e-01
  1.13366283e-01  1.39259845e-01]
Sparsity at: 0.4915671942060086
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8946 - val_loss: 0.8946 - val_accuracy: 0.8996
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08071759e-01
  1.13258064e-01  1.39275387e-01]
Sparsity at: 0.4915671942060086
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8944 - val_loss: 0.8945 - val_accuracy: 0.8991
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0808915e-01
  1.1299156e-01  1.3927774e-01]
Sparsity at: 0.4915671942060086
Epoch 443/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8946 - val_accuracy: 0.9003
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0823208e-01
  1.1257302e-01  1.3942721e-01]
Sparsity at: 0.4915671942060086
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.8998
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08243436e-01
  1.12515368e-01  1.39504388e-01]
Sparsity at: 0.4915671942060086
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8945 - val_loss: 0.8948 - val_accuracy: 0.8995
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08294249e-01
  1.12046905e-01  1.39681369e-01]
Sparsity at: 0.4915671942060086
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.8996
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08254306e-01
  1.12071477e-01  1.39662579e-01]
Sparsity at: 0.4915671942060086
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0854838e-01
  1.1184277e-01  1.3973156e-01]
Sparsity at: 0.4915671942060086
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8947 - val_loss: 0.8944 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0864089e-01
  1.1178386e-01  1.3980819e-01]
Sparsity at: 0.4915671942060086
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0882367e-01
  1.1164524e-01  1.3964541e-01]
Sparsity at: 0.4915671942060086
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0877126e-01
  1.1150129e-01  1.3984288e-01]
Sparsity at: 0.4915671942060086
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8947 - val_loss: 0.8946 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0871526e-01
  1.1149391e-01  1.3994870e-01]
Sparsity at: 0.4915671942060086
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.9000
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.08914539e-01
  1.11582175e-01  1.39858678e-01]
Sparsity at: 0.4915671942060086
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0904176e-01
  1.1136688e-01  1.4003323e-01]
Sparsity at: 0.4915671942060086
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.8998
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09146819e-01
  1.11343555e-01  1.40185088e-01]
Sparsity at: 0.4915671942060086
Epoch 455/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8944 - val_loss: 0.8943 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0924067e-01
  1.1119902e-01  1.4008564e-01]
Sparsity at: 0.4915671942060086
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8945 - val_loss: 0.8944 - val_accuracy: 0.9000
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09411955e-01
  1.11466885e-01  1.40002072e-01]
Sparsity at: 0.4915671942060086
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8947 - val_loss: 0.8944 - val_accuracy: 0.9000
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09446436e-01
  1.11576110e-01  1.40069053e-01]
Sparsity at: 0.4915671942060086
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0947785e-01
  1.1183558e-01  1.4019898e-01]
Sparsity at: 0.4915671942060086
Epoch 459/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8947 - val_loss: 0.8944 - val_accuracy: 0.9002
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0984030e-01
  1.1168176e-01  1.4006375e-01]
Sparsity at: 0.4915671942060086
Epoch 460/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8944 - val_loss: 0.8948 - val_accuracy: 0.8997
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.0979279e-01
  1.1197060e-01  1.4004262e-01]
Sparsity at: 0.4915671942060086
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8948 - val_loss: 0.8947 - val_accuracy: 0.8999
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09773651e-01
  1.12278104e-01  1.39997974e-01]
Sparsity at: 0.4915671942060086
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8947 - val_loss: 0.8943 - val_accuracy: 0.9001
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09965064e-01
  1.12150334e-01  1.39866471e-01]
Sparsity at: 0.4915671942060086
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8947 - val_loss: 0.8946 - val_accuracy: 0.8995
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.09918565e-01
  1.12313867e-01  1.39846548e-01]
Sparsity at: 0.4915671942060086
Epoch 464/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8945 - val_loss: 0.8945 - val_accuracy: 0.9001
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1017323e-01
  1.1265886e-01  1.3969204e-01]
Sparsity at: 0.4915671942060086
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8946 - val_loss: 0.8946 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1024426e-01
  1.1284759e-01  1.3957731e-01]
Sparsity at: 0.4915671942060086
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.8999
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.10267915e-01
  1.13078974e-01  1.39402643e-01]
Sparsity at: 0.4915671942060086
Epoch 467/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9116 - accuracy: 0.8945 - val_loss: 0.8946 - val_accuracy: 0.8997
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.10373557e-01
  1.13316454e-01  1.39337704e-01]
Sparsity at: 0.4915671942060086
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8944 - val_loss: 0.8945 - val_accuracy: 0.8997
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1035977e-01
  1.1347250e-01  1.3934255e-01]
Sparsity at: 0.4915671942060086
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8945 - val_loss: 0.8943 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1046026e-01
  1.1387812e-01  1.3936655e-01]
Sparsity at: 0.4915671942060086
Epoch 470/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9116 - accuracy: 0.8945 - val_loss: 0.8942 - val_accuracy: 0.8995
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1065581e-01
  1.1401110e-01  1.3887058e-01]
Sparsity at: 0.4915671942060086
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8948 - val_loss: 0.8944 - val_accuracy: 0.8997
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.10689074e-01
  1.14183478e-01  1.38991609e-01]
Sparsity at: 0.4915671942060086
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8999
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1066421e-01
  1.1480149e-01  1.3878663e-01]
Sparsity at: 0.4915671942060086
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8949 - val_loss: 0.8945 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1086204e-01
  1.1478627e-01  1.3863562e-01]
Sparsity at: 0.4915671942060086
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8949 - val_loss: 0.8943 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1096316e-01
  1.1493969e-01  1.3853990e-01]
Sparsity at: 0.4915671942060086
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8943 - val_loss: 0.8942 - val_accuracy: 0.8996
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.10968165e-01
  1.15184277e-01  1.38480559e-01]
Sparsity at: 0.4915671942060086
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8992
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11093074e-01
  1.15326643e-01  1.38397470e-01]
Sparsity at: 0.4915671942060086
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8945 - val_loss: 0.8943 - val_accuracy: 0.8995
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1120243e-01
  1.1535202e-01  1.3834369e-01]
Sparsity at: 0.4915671942060086
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8947 - val_loss: 0.8943 - val_accuracy: 0.9004
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11401595e-01
  1.15486659e-01  1.38229296e-01]
Sparsity at: 0.4915671942060086
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9113 - accuracy: 0.8948 - val_loss: 0.8943 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1129933e-01
  1.1570214e-01  1.3817190e-01]
Sparsity at: 0.4915671942060086
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8946 - val_loss: 0.8941 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1154682e-01
  1.1579528e-01  1.3817000e-01]
Sparsity at: 0.4915671942060086
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8945 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1166912e-01
  1.1582640e-01  1.3809885e-01]
Sparsity at: 0.4915671942060086
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8947 - val_loss: 0.8942 - val_accuracy: 0.8995
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1177162e-01
  1.1585528e-01  1.3792144e-01]
Sparsity at: 0.4915671942060086
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8949 - val_loss: 0.8944 - val_accuracy: 0.8997
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1153146e-01
  1.1595198e-01  1.3794558e-01]
Sparsity at: 0.4915671942060086
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8949 - val_loss: 0.8946 - val_accuracy: 0.8995
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1186957e-01
  1.1604665e-01  1.3788256e-01]
Sparsity at: 0.4915671942060086
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8946 - val_loss: 0.8943 - val_accuracy: 0.8995
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.11905605e-01
  1.16103694e-01  1.37739092e-01]
Sparsity at: 0.4915671942060086
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8943 - val_loss: 0.8944 - val_accuracy: 0.8996
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1206704e-01
  1.1605492e-01  1.3771498e-01]
Sparsity at: 0.4915671942060086
Epoch 487/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9115 - accuracy: 0.8946 - val_loss: 0.8943 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1225508e-01
  1.1603306e-01  1.3756871e-01]
Sparsity at: 0.4915671942060086
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8942 - val_accuracy: 0.8997
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1232754e-01
  1.1599693e-01  1.3772751e-01]
Sparsity at: 0.4915671942060086
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8946 - val_loss: 0.8943 - val_accuracy: 0.8997
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1223139e-01
  1.1623563e-01  1.3775641e-01]
Sparsity at: 0.4915671942060086
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8948 - val_loss: 0.8945 - val_accuracy: 0.8999
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12159371e-01
  1.16325974e-01  1.37708455e-01]
Sparsity at: 0.4915671942060086
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8946 - val_loss: 0.8941 - val_accuracy: 0.8997
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12295575e-01
  1.16299257e-01  1.37415767e-01]
Sparsity at: 0.4915671942060086
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8948 - val_loss: 0.8945 - val_accuracy: 0.8997
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12389036e-01
  1.16058737e-01  1.37529224e-01]
Sparsity at: 0.4915671942060086
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8946 - val_loss: 0.8942 - val_accuracy: 0.8999
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12513818e-01
  1.16293915e-01  1.37334928e-01]
Sparsity at: 0.4915671942060086
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8945 - val_accuracy: 0.8999
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12554282e-01
  1.16330415e-01  1.37311578e-01]
Sparsity at: 0.4915671942060086
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8943 - val_accuracy: 0.8997
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1249406e-01
  1.1613330e-01  1.3743313e-01]
Sparsity at: 0.4915671942060086
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8944 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1252463e-01
  1.1630807e-01  1.3735846e-01]
Sparsity at: 0.4915671942060086
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8949 - val_loss: 0.8944 - val_accuracy: 0.8997
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12515874e-01
  1.16349913e-01  1.37368605e-01]
Sparsity at: 0.4915671942060086
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9113 - accuracy: 0.8946 - val_loss: 0.8942 - val_accuracy: 0.8995
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12613365e-01
  1.16213813e-01  1.37095481e-01]
Sparsity at: 0.4915671942060086
Epoch 499/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9113 - accuracy: 0.8949 - val_loss: 0.8943 - val_accuracy: 0.8998
[ 4.4059549e-34  0.0000000e+00 -5.4672311e-34 ...  1.1262289e-01
  1.1653216e-01  1.3706270e-01]
Sparsity at: 0.4915671942060086
Epoch 500/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9115 - accuracy: 0.8947 - val_loss: 0.8943 - val_accuracy: 0.8998
[ 4.40595486e-34  0.00000000e+00 -5.46723111e-34 ...  1.12605706e-01
  1.16307721e-01  1.37032583e-01]
Sparsity at: 0.4915671942060086
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.042060211300849915
Thresholhold 0.08002246171236038
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.08791892230510712
Thresholhold 0.1589777022600174
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 0. 1. 0.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 1. 1.]
 ...
 [0. 1. 0. ... 0. 1. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10110617056488991
Thresholhold 0.0191974937915802
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 59:22 - loss: 2.3111 - accuracy: 0.0742WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0053s vs `on_train_batch_begin` time: 2.4754s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 0.5980 - accuracy: 0.8456 - val_loss: 0.2966 - val_accuracy: 0.9151
[ 0.08002246  0.         -0.07110295 ...  0.15403095  0.22507241
 -0.04287057]
Sparsity at: 0.4915671942060086
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2695 - accuracy: 0.9220 - val_loss: 0.2337 - val_accuracy: 0.9325
[ 0.08002246  0.         -0.07110295 ...  0.18079707  0.2367594
 -0.03975654]
Sparsity at: 0.4915671942060086
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2170 - accuracy: 0.9369 - val_loss: 0.1958 - val_accuracy: 0.9406
[ 0.08002246  0.         -0.07110295 ...  0.19941992  0.24121058
 -0.04119756]
Sparsity at: 0.4915671942060086
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1823 - accuracy: 0.9467 - val_loss: 0.1699 - val_accuracy: 0.9495
[ 0.08002246  0.         -0.07110295 ...  0.21053828  0.2435588
 -0.04173013]
Sparsity at: 0.4915671942060086
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1569 - accuracy: 0.9541 - val_loss: 0.1508 - val_accuracy: 0.9551
[ 0.08002246  0.         -0.07110295 ...  0.2170271   0.24542262
 -0.04122256]
Sparsity at: 0.4915671942060086
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1375 - accuracy: 0.9602 - val_loss: 0.1370 - val_accuracy: 0.9578
[ 0.08002246  0.         -0.07110295 ...  0.22122398  0.24742967
 -0.04063053]
Sparsity at: 0.4915671942060086
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1219 - accuracy: 0.9647 - val_loss: 0.1263 - val_accuracy: 0.9614
[ 0.08002246  0.         -0.07110295 ...  0.22468105  0.25019264
 -0.0401718 ]
Sparsity at: 0.4915671942060086
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1089 - accuracy: 0.9682 - val_loss: 0.1181 - val_accuracy: 0.9639
[ 0.08002246  0.         -0.07110295 ...  0.22849211  0.25324175
 -0.04001823]
Sparsity at: 0.4915671942060086
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0981 - accuracy: 0.9715 - val_loss: 0.1116 - val_accuracy: 0.9654
[ 0.08002246  0.         -0.07110295 ...  0.23292     0.25692385
 -0.0407346 ]
Sparsity at: 0.4915671942060086
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0889 - accuracy: 0.9740 - val_loss: 0.1065 - val_accuracy: 0.9675
[ 0.08002246  0.         -0.07110295 ...  0.23783354  0.26033744
 -0.04115634]
Sparsity at: 0.4915671942060086
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0806 - accuracy: 0.9766 - val_loss: 0.1024 - val_accuracy: 0.9689
[ 0.08002246  0.         -0.07110295 ...  0.24293017  0.2644032
 -0.04193686]
Sparsity at: 0.4915671942060086
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0737 - accuracy: 0.9788 - val_loss: 0.0994 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  0.24819818  0.26818782
 -0.04247066]
Sparsity at: 0.4915671942060086
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0675 - accuracy: 0.9807 - val_loss: 0.0971 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.253902    0.27221018
 -0.04262   ]
Sparsity at: 0.4915671942060086
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0620 - accuracy: 0.9824 - val_loss: 0.0955 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.25923094  0.27618933
 -0.04229878]
Sparsity at: 0.4915671942060086
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0569 - accuracy: 0.9836 - val_loss: 0.0944 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.26531258  0.28025642
 -0.04180551]
Sparsity at: 0.4915671942060086
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0523 - accuracy: 0.9850 - val_loss: 0.0938 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.27090555  0.28489769
 -0.04055597]
Sparsity at: 0.4915671942060086
Epoch 17/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0480 - accuracy: 0.9860 - val_loss: 0.0938 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.27674052  0.29051438
 -0.03956193]
Sparsity at: 0.4915671942060086
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0442 - accuracy: 0.9877 - val_loss: 0.0941 - val_accuracy: 0.9718
[ 0.08002246  0.         -0.07110295 ...  0.28304893  0.2961979
 -0.03777971]
Sparsity at: 0.4915671942060086
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0406 - accuracy: 0.9890 - val_loss: 0.0943 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.28919673  0.3019713
 -0.03591988]
Sparsity at: 0.4915671942060086
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0373 - accuracy: 0.9900 - val_loss: 0.0953 - val_accuracy: 0.9718
[ 0.08002246  0.         -0.07110295 ...  0.29516745  0.308648
 -0.03338408]
Sparsity at: 0.4915671942060086
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0342 - accuracy: 0.9913 - val_loss: 0.0961 - val_accuracy: 0.9720
[ 0.08002246  0.         -0.07110295 ...  0.3017227   0.31511888
 -0.030502  ]
Sparsity at: 0.4915671942060086
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0313 - accuracy: 0.9922 - val_loss: 0.0973 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.308174    0.32197502
 -0.02749309]
Sparsity at: 0.4915671942060086
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0285 - accuracy: 0.9930 - val_loss: 0.0986 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.31502944  0.32921323
 -0.02383372]
Sparsity at: 0.4915671942060086
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0261 - accuracy: 0.9940 - val_loss: 0.1003 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.32229623  0.33611634
 -0.0204662 ]
Sparsity at: 0.4915671942060086
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0237 - accuracy: 0.9948 - val_loss: 0.1017 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  0.32807782  0.343554
 -0.01632345]
Sparsity at: 0.4915671942060086
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0216 - accuracy: 0.9954 - val_loss: 0.1033 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.33514717  0.35138047
 -0.01284597]
Sparsity at: 0.4915671942060086
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0196 - accuracy: 0.9959 - val_loss: 0.1054 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.34274822  0.3586219
 -0.00920124]
Sparsity at: 0.4915671942060086
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0177 - accuracy: 0.9966 - val_loss: 0.1067 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  0.34905848  0.36611503
 -0.00547521]
Sparsity at: 0.4915671942060086
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.1085 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  0.3567542   0.37312636
 -0.0015449 ]
Sparsity at: 0.4915671942060086
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0144 - accuracy: 0.9976 - val_loss: 0.1105 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  0.36298195  0.38019937
  0.00272609]
Sparsity at: 0.4915671942060086
Epoch 31/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0130 - accuracy: 0.9982 - val_loss: 0.1131 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  0.37064958  0.38756934
  0.00717586]
Sparsity at: 0.4915671942060086
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0116 - accuracy: 0.9985 - val_loss: 0.1156 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.37758613  0.39457095
  0.01137318]
Sparsity at: 0.4915671942060086
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0105 - accuracy: 0.9987 - val_loss: 0.1178 - val_accuracy: 0.9699
[ 0.08002246  0.         -0.07110295 ...  0.38473612  0.4013706
  0.01473436]
Sparsity at: 0.4915671942060086
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0095 - accuracy: 0.9990 - val_loss: 0.1205 - val_accuracy: 0.9697
[ 0.08002246  0.         -0.07110295 ...  0.39330837  0.40836734
  0.01899385]
Sparsity at: 0.4915671942060086
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0085 - accuracy: 0.9992 - val_loss: 0.1236 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  0.40130016  0.4156744
  0.02317327]
Sparsity at: 0.4915671942060086
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0076 - accuracy: 0.9994 - val_loss: 0.1263 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  0.40796375  0.42280975
  0.02714473]
Sparsity at: 0.4915671942060086
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0068 - accuracy: 0.9995 - val_loss: 0.1302 - val_accuracy: 0.9701
[ 0.08002246  0.         -0.07110295 ...  0.41650733  0.4315623
  0.03071887]
Sparsity at: 0.4915671942060086
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0061 - accuracy: 0.9996 - val_loss: 0.1345 - val_accuracy: 0.9702
[ 0.08002246  0.         -0.07110295 ...  0.42501035  0.4403443
  0.03356542]
Sparsity at: 0.4915671942060086
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9997 - val_loss: 0.1387 - val_accuracy: 0.9697
[ 0.08002246  0.         -0.07110295 ...  0.43323556  0.44742095
  0.0359411 ]
Sparsity at: 0.4915671942060086
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0051 - accuracy: 0.9997 - val_loss: 0.1397 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.44202086  0.4532475
  0.03814758]
Sparsity at: 0.4915671942060086
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0049 - accuracy: 0.9995 - val_loss: 0.1436 - val_accuracy: 0.9695
[ 0.08002246  0.         -0.07110295 ...  0.4509198   0.45937124
  0.04281923]
Sparsity at: 0.4915671942060086
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0049 - accuracy: 0.9995 - val_loss: 0.1422 - val_accuracy: 0.9702
[ 0.08002246  0.         -0.07110295 ...  0.46041158  0.46822894
  0.0464743 ]
Sparsity at: 0.4915671942060086
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0045 - accuracy: 0.9996 - val_loss: 0.1527 - val_accuracy: 0.9671
[ 0.08002246  0.         -0.07110295 ...  0.46471274  0.46575364
  0.05543198]
Sparsity at: 0.4915671942060086
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9995 - val_loss: 0.1563 - val_accuracy: 0.9679
[ 0.08002246  0.         -0.07110295 ...  0.47271535  0.467069
  0.05703241]
Sparsity at: 0.4915671942060086
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0045 - accuracy: 0.9994 - val_loss: 0.1625 - val_accuracy: 0.9688
[ 0.08002246  0.         -0.07110295 ...  0.47844338  0.47392547
  0.04568287]
Sparsity at: 0.4915671942060086
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9994 - val_loss: 0.1774 - val_accuracy: 0.9656
[ 0.08002246  0.         -0.07110295 ...  0.48285082  0.47367832
  0.05655241]
Sparsity at: 0.4915671942060086
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.1754 - val_accuracy: 0.9645
[ 0.08002246  0.         -0.07110295 ...  0.49553707  0.47806528
  0.05026842]
Sparsity at: 0.4915671942060086
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0038 - accuracy: 0.9995 - val_loss: 0.1563 - val_accuracy: 0.9681
[ 0.08002246  0.         -0.07110295 ...  0.49351195  0.47230536
  0.05889468]
Sparsity at: 0.4915671942060086
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0026 - accuracy: 0.9998 - val_loss: 0.1564 - val_accuracy: 0.9701
[ 0.08002246  0.         -0.07110295 ...  0.49957392  0.47800484
  0.0625774 ]
Sparsity at: 0.4915671942060086
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.1501 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  0.5012927   0.47947577
  0.06831842]
Sparsity at: 0.4915671942060086
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.06554365864303513
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.13429796234413516
Thresholhold 0.3532998859882355
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 1. 0. ... 0. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.5239421559155915
Thresholhold -0.0007533457246609032
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 50s 7ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1527 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  0.50778925  0.48476535
  0.06988464]
Sparsity at: 0.5116013948497854
Epoch 52/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  0.5114411   0.48745537
  0.07546558]
Sparsity at: 0.5116013948497854
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  0.51805764  0.49229187
  0.08141425]
Sparsity at: 0.5116013948497854
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.5221192   0.50078255
  0.08632594]
Sparsity at: 0.5116013948497854
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1571 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.5253793   0.50655377
  0.09191508]
Sparsity at: 0.5116013948497854
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5513e-04 - accuracy: 1.0000 - val_loss: 0.1576 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.5312357   0.5126764
  0.09732085]
Sparsity at: 0.5116013948497854
Epoch 57/500
235/235 [==============================] - 2s 9ms/step - loss: 8.3535e-04 - accuracy: 1.0000 - val_loss: 0.1587 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  0.53652817  0.5181625
  0.10230128]
Sparsity at: 0.5116013948497854
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 7.3349e-04 - accuracy: 1.0000 - val_loss: 0.1605 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.54235137  0.5236728
  0.10680497]
Sparsity at: 0.5116013948497854
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 6.4975e-04 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  0.54803544  0.52893156
  0.11131476]
Sparsity at: 0.5116013948497854
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7474e-04 - accuracy: 1.0000 - val_loss: 0.1635 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  0.5543503   0.53421384
  0.11474145]
Sparsity at: 0.5116013948497854
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1033e-04 - accuracy: 1.0000 - val_loss: 0.1651 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  0.5610127   0.53964263
  0.11801929]
Sparsity at: 0.5116013948497854
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5406e-04 - accuracy: 1.0000 - val_loss: 0.1666 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  0.5668852   0.54505545
  0.12125406]
Sparsity at: 0.5116013948497854
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0410e-04 - accuracy: 1.0000 - val_loss: 0.1689 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.5729607   0.550516
  0.12463471]
Sparsity at: 0.5116013948497854
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6155e-04 - accuracy: 1.0000 - val_loss: 0.1705 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  0.58009094  0.55629396
  0.12719645]
Sparsity at: 0.5116013948497854
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2144e-04 - accuracy: 1.0000 - val_loss: 0.1727 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  0.5867501   0.5620342
  0.1299306 ]
Sparsity at: 0.5116013948497854
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8675e-04 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.59356534  0.5676849
  0.13260476]
Sparsity at: 0.5116013948497854
Epoch 67/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5653e-04 - accuracy: 1.0000 - val_loss: 0.1767 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.6006754   0.5733052
  0.13497421]
Sparsity at: 0.5116013948497854
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3003e-04 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  0.6070699   0.5795346
  0.1372382 ]
Sparsity at: 0.5116013948497854
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0508e-04 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  0.61442304  0.5850866
  0.13928041]
Sparsity at: 0.5116013948497854
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8378e-04 - accuracy: 1.0000 - val_loss: 0.1825 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  0.6211853   0.5907173
  0.1409962 ]
Sparsity at: 0.5116013948497854
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6437e-04 - accuracy: 1.0000 - val_loss: 0.1853 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.6286395   0.5968385
  0.14332537]
Sparsity at: 0.5116013948497854
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4672e-04 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.6361831   0.6029562
  0.14485931]
Sparsity at: 0.5116013948497854
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3159e-04 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.64391124  0.60869485
  0.14657554]
Sparsity at: 0.5116013948497854
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1872e-04 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.6505655   0.61451125
  0.14875573]
Sparsity at: 0.5116013948497854
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0560e-04 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  0.657837    0.620975
  0.15077709]
Sparsity at: 0.5116013948497854
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3634e-05 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  0.6653088   0.62722665
  0.15233386]
Sparsity at: 0.5116013948497854
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3785e-05 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  0.6724663   0.63315266
  0.15393059]
Sparsity at: 0.5116013948497854
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 7.4851e-05 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  0.68032986  0.63895845
  0.15584745]
Sparsity at: 0.5116013948497854
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6860e-05 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  0.68747383  0.6452678
  0.15746363]
Sparsity at: 0.5116013948497854
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9809e-05 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.6952295   0.65169376
  0.15860666]
Sparsity at: 0.5116013948497854
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3311e-05 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  0.70221764  0.65778714
  0.16044375]
Sparsity at: 0.5116013948497854
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7491e-05 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  0.7097111   0.66405624
  0.16228974]
Sparsity at: 0.5116013948497854
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2425e-05 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  0.7176315   0.67037684
  0.16367453]
Sparsity at: 0.5116013948497854
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7760e-05 - accuracy: 1.0000 - val_loss: 0.2157 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.7250413   0.67672855
  0.1650769 ]
Sparsity at: 0.5116013948497854
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3581e-05 - accuracy: 1.0000 - val_loss: 0.2182 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  0.73262995  0.6834498
  0.16692896]
Sparsity at: 0.5116013948497854
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9862e-05 - accuracy: 1.0000 - val_loss: 0.2211 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.74092406  0.68995535
  0.16816261]
Sparsity at: 0.5116013948497854
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6664e-05 - accuracy: 1.0000 - val_loss: 0.2238 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  0.7484569   0.69624
  0.16927885]
Sparsity at: 0.5116013948497854
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3840e-05 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.75560665  0.70245093
  0.17066649]
Sparsity at: 0.5116013948497854
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 2.1244e-05 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.7634249   0.7090837
  0.17212826]
Sparsity at: 0.5116013948497854
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8752e-05 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.77092737  0.7158673
  0.17326224]
Sparsity at: 0.5116013948497854
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6699e-05 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.7786712   0.7223492
  0.1749871 ]
Sparsity at: 0.5116013948497854
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4937e-05 - accuracy: 1.0000 - val_loss: 0.2364 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  0.7859144   0.72874236
  0.1763072 ]
Sparsity at: 0.5116013948497854
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3169e-05 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  0.7936984   0.7352431
  0.17779565]
Sparsity at: 0.5116013948497854
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1734e-05 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  0.80148876  0.74207425
  0.17868333]
Sparsity at: 0.5116013948497854
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0447e-05 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  0.80902225  0.74880123
  0.17986132]
Sparsity at: 0.5116013948497854
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3253e-06 - accuracy: 1.0000 - val_loss: 0.2467 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  0.8164374   0.754989
  0.18077394]
Sparsity at: 0.5116013948497854
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3214e-06 - accuracy: 1.0000 - val_loss: 0.2497 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  0.82393587  0.7616106
  0.1824721 ]
Sparsity at: 0.5116013948497854
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3960e-06 - accuracy: 1.0000 - val_loss: 0.2529 - val_accuracy: 0.9702
[ 0.08002246  0.         -0.07110295 ...  0.8313027   0.76837736
  0.18384735]
Sparsity at: 0.5116013948497854
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5732e-06 - accuracy: 1.0000 - val_loss: 0.2552 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  0.8388487   0.77439874
  0.18466987]
Sparsity at: 0.5116013948497854
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8743e-06 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9701
[ 0.08002246  0.         -0.07110295 ...  0.8455984   0.78082407
  0.18604793]
Sparsity at: 0.5116013948497854
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.14119782226718947
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.2951456754509607
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 1. 0. ... 0. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.9428782204331725
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 5.2214e-06 - accuracy: 1.0000 - val_loss: 0.2613 - val_accuracy: 0.9701
[ 0.08002246  0.         -0.07110295 ...  0.8535893   0.7869235
  0.1867662 ]
Sparsity at: 0.5116013948497854
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 4.6418e-06 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  0.8598836   0.7933987
  0.18839444]
Sparsity at: 0.5116013948497854
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1878e-06 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  0.86643565  0.79961586
  0.1906055 ]
Sparsity at: 0.5116013948497854
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7254e-06 - accuracy: 1.0000 - val_loss: 0.2689 - val_accuracy: 0.9701
[ 0.08002246  0.         -0.07110295 ...  0.87315595  0.80611664
  0.19233303]
Sparsity at: 0.5116013948497854
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3195e-06 - accuracy: 1.0000 - val_loss: 0.2722 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.8804346   0.8124888
  0.19401895]
Sparsity at: 0.5116013948497854
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0109e-06 - accuracy: 1.0000 - val_loss: 0.2751 - val_accuracy: 0.9701
[ 0.08002246  0.         -0.07110295 ...  0.88775516  0.8187922
  0.19613324]
Sparsity at: 0.5116013948497854
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6619e-06 - accuracy: 1.0000 - val_loss: 0.2801 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  0.89448494  0.8246865
  0.19801609]
Sparsity at: 0.5116013948497854
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0271 - accuracy: 0.9938 - val_loss: 0.2829 - val_accuracy: 0.9679
[ 0.08002246  0.         -0.07110295 ...  0.9267318   0.8015158
  0.14386694]
Sparsity at: 0.5116013948497854
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9990 - val_loss: 0.2737 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.9299757   0.79526436
  0.14478196]
Sparsity at: 0.5116013948497854
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2106e-04 - accuracy: 0.9998 - val_loss: 0.2736 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  0.9338108   0.7981049
  0.1419741 ]
Sparsity at: 0.5116013948497854
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5939e-04 - accuracy: 0.9999 - val_loss: 0.2693 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.94307846  0.7971686
  0.13921237]
Sparsity at: 0.5116013948497854
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1742e-04 - accuracy: 1.0000 - val_loss: 0.2689 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  0.94218606  0.7976946
  0.14185008]
Sparsity at: 0.5116013948497854
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 8.0115e-05 - accuracy: 1.0000 - val_loss: 0.2698 - val_accuracy: 0.9719
[ 0.08002246  0.         -0.07110295 ...  0.9395287   0.7991111
  0.14276811]
Sparsity at: 0.5116013948497854
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7200e-05 - accuracy: 1.0000 - val_loss: 0.2693 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9387769   0.7995772
  0.14285561]
Sparsity at: 0.5116013948497854
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1744e-05 - accuracy: 1.0000 - val_loss: 0.2691 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.93819857  0.79975736
  0.14293657]
Sparsity at: 0.5116013948497854
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8436e-05 - accuracy: 1.0000 - val_loss: 0.2690 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.93768185  0.7998711
  0.14300315]
Sparsity at: 0.5116013948497854
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5915e-05 - accuracy: 1.0000 - val_loss: 0.2689 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.93719524  0.7999583
  0.14305682]
Sparsity at: 0.5116013948497854
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3831e-05 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.93673116  0.8000387
  0.1431102 ]
Sparsity at: 0.5116013948497854
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2076e-05 - accuracy: 1.0000 - val_loss: 0.2687 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.93628556  0.80011916
  0.14315969]
Sparsity at: 0.5116013948497854
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0527e-05 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.9358458   0.8002066
  0.14321253]
Sparsity at: 0.5116013948497854
Epoch 121/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9156e-05 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.93542665  0.80030555
  0.14327054]
Sparsity at: 0.5116013948497854
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7923e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.93500954  0.80041975
  0.14333855]
Sparsity at: 0.5116013948497854
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6817e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.9346228   0.8005531
  0.14340968]
Sparsity at: 0.5116013948497854
Epoch 124/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5788e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9342495   0.8007027
  0.14349501]
Sparsity at: 0.5116013948497854
Epoch 125/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4842e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.93390036  0.80087936
  0.14359081]
Sparsity at: 0.5116013948497854
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3980e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.9335786   0.8010782
  0.14369519]
Sparsity at: 0.5116013948497854
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3155e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.93325156  0.80130696
  0.1438232 ]
Sparsity at: 0.5116013948497854
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2396e-05 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9329664   0.8015576
  0.1439584 ]
Sparsity at: 0.5116013948497854
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1682e-05 - accuracy: 1.0000 - val_loss: 0.2687 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9327074   0.80185014
  0.1441163 ]
Sparsity at: 0.5116013948497854
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1014e-05 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.93249404  0.8021757
  0.14428182]
Sparsity at: 0.5116013948497854
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0388e-05 - accuracy: 1.0000 - val_loss: 0.2689 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.9323132   0.80254304
  0.1444581 ]
Sparsity at: 0.5116013948497854
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7966e-06 - accuracy: 1.0000 - val_loss: 0.2690 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9321492   0.8029356
  0.1446675 ]
Sparsity at: 0.5116013948497854
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 9.2398e-06 - accuracy: 1.0000 - val_loss: 0.2692 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.9320471   0.80336416
  0.14489844]
Sparsity at: 0.5116013948497854
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 8.7020e-06 - accuracy: 1.0000 - val_loss: 0.2693 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.93198174  0.8038309
  0.14515086]
Sparsity at: 0.5116013948497854
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2041e-06 - accuracy: 1.0000 - val_loss: 0.2695 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9319843   0.80432844
  0.14541622]
Sparsity at: 0.5116013948497854
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7269e-06 - accuracy: 1.0000 - val_loss: 0.2697 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9320231   0.8048629
  0.14571424]
Sparsity at: 0.5116013948497854
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2813e-06 - accuracy: 1.0000 - val_loss: 0.2699 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  0.9320844   0.8054465
  0.14604397]
Sparsity at: 0.5116013948497854
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8543e-06 - accuracy: 1.0000 - val_loss: 0.2702 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9321975   0.80607814
  0.14639887]
Sparsity at: 0.5116013948497854
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 6.4584e-06 - accuracy: 1.0000 - val_loss: 0.2704 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9323722   0.8067439
  0.14677651]
Sparsity at: 0.5116013948497854
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0757e-06 - accuracy: 1.0000 - val_loss: 0.2707 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9326314   0.80745
  0.14717764]
Sparsity at: 0.5116013948497854
Epoch 141/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7205e-06 - accuracy: 1.0000 - val_loss: 0.2710 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.93291247  0.8082084
  0.14764148]
Sparsity at: 0.5116013948497854
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3746e-06 - accuracy: 1.0000 - val_loss: 0.2713 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.93325144  0.8090063
  0.14810437]
Sparsity at: 0.5116013948497854
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0533e-06 - accuracy: 1.0000 - val_loss: 0.2717 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.9336556   0.809865
  0.1486166 ]
Sparsity at: 0.5116013948497854
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7488e-06 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.9340755   0.81080574
  0.14916632]
Sparsity at: 0.5116013948497854
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4558e-06 - accuracy: 1.0000 - val_loss: 0.2724 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.9346113   0.81177986
  0.14973277]
Sparsity at: 0.5116013948497854
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1818e-06 - accuracy: 1.0000 - val_loss: 0.2728 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.93520796  0.81283444
  0.15031265]
Sparsity at: 0.5116013948497854
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9265e-06 - accuracy: 1.0000 - val_loss: 0.2732 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  0.9358406   0.81393677
  0.15097463]
Sparsity at: 0.5116013948497854
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6872e-06 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.93653786  0.8151299
  0.1516893 ]
Sparsity at: 0.5116013948497854
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4513e-06 - accuracy: 1.0000 - val_loss: 0.2740 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9372964   0.81636536
  0.15243332]
Sparsity at: 0.5116013948497854
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2351e-06 - accuracy: 1.0000 - val_loss: 0.2745 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9382226   0.8176982
  0.15318665]
Sparsity at: 0.5116013948497854
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.23946041399091111
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.42941963463627175
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 1. 0. ... 0. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 1.170436173033778
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 3.0310e-06 - accuracy: 1.0000 - val_loss: 0.2750 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.9391556   0.81912357
  0.15400468]
Sparsity at: 0.5116013948497854
Epoch 152/500
235/235 [==============================] - 2s 7ms/step - loss: 2.8373e-06 - accuracy: 1.0000 - val_loss: 0.2755 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.94017947  0.8206563
  0.1548881 ]
Sparsity at: 0.5116013948497854
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6569e-06 - accuracy: 1.0000 - val_loss: 0.2761 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.94130427  0.8222832
  0.15577006]
Sparsity at: 0.5116013948497854
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4858e-06 - accuracy: 1.0000 - val_loss: 0.2767 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.9423819   0.82402843
  0.15677132]
Sparsity at: 0.5116013948497854
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3227e-06 - accuracy: 1.0000 - val_loss: 0.2772 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.94367987  0.8258381
  0.15772903]
Sparsity at: 0.5116013948497854
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1712e-06 - accuracy: 1.0000 - val_loss: 0.2779 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.9450337   0.8278068
  0.15874985]
Sparsity at: 0.5116013948497854
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0256e-06 - accuracy: 1.0000 - val_loss: 0.2786 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.94649535  0.82989043
  0.1598039 ]
Sparsity at: 0.5116013948497854
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8905e-06 - accuracy: 1.0000 - val_loss: 0.2794 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.94806296  0.8320509
  0.16090116]
Sparsity at: 0.5116013948497854
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7644e-06 - accuracy: 1.0000 - val_loss: 0.2802 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.9497346   0.8343659
  0.16204314]
Sparsity at: 0.5116013948497854
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6440e-06 - accuracy: 1.0000 - val_loss: 0.2810 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  0.9515984   0.83680654
  0.1631748 ]
Sparsity at: 0.5116013948497854
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5311e-06 - accuracy: 1.0000 - val_loss: 0.2819 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.95351386  0.8394058
  0.16434117]
Sparsity at: 0.5116013948497854
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4255e-06 - accuracy: 1.0000 - val_loss: 0.2828 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9554875   0.8420406
  0.16552164]
Sparsity at: 0.5116013948497854
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3258e-06 - accuracy: 1.0000 - val_loss: 0.2838 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.95776176  0.844837
  0.16675669]
Sparsity at: 0.5116013948497854
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2314e-06 - accuracy: 1.0000 - val_loss: 0.2848 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9601047   0.8477735
  0.1679596 ]
Sparsity at: 0.5116013948497854
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1423e-06 - accuracy: 1.0000 - val_loss: 0.2858 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.96256465  0.8507928
  0.16923088]
Sparsity at: 0.5116013948497854
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0607e-06 - accuracy: 1.0000 - val_loss: 0.2870 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9652188   0.8539904
  0.17048721]
Sparsity at: 0.5116013948497854
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 9.8366e-07 - accuracy: 1.0000 - val_loss: 0.2882 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9678429   0.8572516
  0.17173943]
Sparsity at: 0.5116013948497854
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0995e-07 - accuracy: 1.0000 - val_loss: 0.2893 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.97074324  0.8606325
  0.17297731]
Sparsity at: 0.5116013948497854
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 8.4401e-07 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9736803   0.864152
  0.17425396]
Sparsity at: 0.5116013948497854
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8079e-07 - accuracy: 1.0000 - val_loss: 0.2919 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.9768014   0.86780673
  0.1754415 ]
Sparsity at: 0.5116013948497854
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2241e-07 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9800908   0.87148595
  0.17674567]
Sparsity at: 0.5116013948497854
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6570e-07 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.98353326  0.8753691
  0.17802022]
Sparsity at: 0.5116013948497854
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1541e-07 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  0.9868986   0.87927747
  0.17916375]
Sparsity at: 0.5116013948497854
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7008e-07 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  0.99047256  0.8833411
  0.18042998]
Sparsity at: 0.5116013948497854
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2609e-07 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  0.9940986   0.8874302
  0.18169056]
Sparsity at: 0.5116013948497854
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8362e-07 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  0.99780285  0.89153147
  0.18288834]
Sparsity at: 0.5116013948497854
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4767e-07 - accuracy: 1.0000 - val_loss: 0.3014 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  1.0014399   0.89560425
  0.1840831 ]
Sparsity at: 0.5116013948497854
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1291e-07 - accuracy: 1.0000 - val_loss: 0.3029 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  1.0052856   0.8998426
  0.18532866]
Sparsity at: 0.5116013948497854
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7990e-07 - accuracy: 1.0000 - val_loss: 0.3042 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  1.0090594   0.904079
  0.18654358]
Sparsity at: 0.5116013948497854
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5086e-07 - accuracy: 1.0000 - val_loss: 0.3056 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  1.0128978   0.90836895
  0.18773304]
Sparsity at: 0.5116013948497854
Epoch 181/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2338e-07 - accuracy: 1.0000 - val_loss: 0.3070 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  1.0165602   0.9124882
  0.18892589]
Sparsity at: 0.5116013948497854
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9908e-07 - accuracy: 1.0000 - val_loss: 0.3085 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  1.0203651   0.9167627
  0.19019082]
Sparsity at: 0.5116013948497854
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7626e-07 - accuracy: 1.0000 - val_loss: 0.3099 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  1.0241971   0.92095083
  0.19137993]
Sparsity at: 0.5116013948497854
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5542e-07 - accuracy: 1.0000 - val_loss: 0.3112 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  1.02799     0.92511165
  0.1925311 ]
Sparsity at: 0.5116013948497854
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3658e-07 - accuracy: 1.0000 - val_loss: 0.3126 - val_accuracy: 0.9716
[ 0.08002246  0.         -0.07110295 ...  1.031684    0.9293551
  0.19356833]
Sparsity at: 0.5116013948497854
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1825e-07 - accuracy: 1.0000 - val_loss: 0.3139 - val_accuracy: 0.9717
[ 0.08002246  0.         -0.07110295 ...  1.0353853   0.9333732
  0.19463752]
Sparsity at: 0.5116013948497854
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0267e-07 - accuracy: 1.0000 - val_loss: 0.3150 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  1.0389435   0.9374391
  0.19565596]
Sparsity at: 0.5116013948497854
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8800e-07 - accuracy: 1.0000 - val_loss: 0.3163 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  1.0424333   0.9413828
  0.19680512]
Sparsity at: 0.5116013948497854
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7409e-07 - accuracy: 1.0000 - val_loss: 0.3176 - val_accuracy: 0.9715
[ 0.08002246  0.         -0.07110295 ...  1.0459361   0.945142
  0.19775382]
Sparsity at: 0.5116013948497854
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6225e-07 - accuracy: 1.0000 - val_loss: 0.3189 - val_accuracy: 0.9713
[ 0.08002246  0.         -0.07110295 ...  1.0492002   0.94897187
  0.1987091 ]
Sparsity at: 0.5116013948497854
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5074e-07 - accuracy: 1.0000 - val_loss: 0.3202 - val_accuracy: 0.9714
[ 0.08002246  0.         -0.07110295 ...  1.0526073   0.9526499
  0.19962764]
Sparsity at: 0.5116013948497854
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4107e-07 - accuracy: 1.0000 - val_loss: 0.3213 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.0558199   0.9561634
  0.20049673]
Sparsity at: 0.5116013948497854
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3153e-07 - accuracy: 1.0000 - val_loss: 0.3224 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.0589414   0.9597063
  0.20133002]
Sparsity at: 0.5116013948497854
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2293e-07 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.0620043   0.9630236
  0.2021836 ]
Sparsity at: 0.5116013948497854
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1511e-07 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.0648497   0.96642584
  0.20299056]
Sparsity at: 0.5116013948497854
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0807e-07 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.0675446   0.9695994
  0.20372798]
Sparsity at: 0.5116013948497854
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0171e-07 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.0703744   0.97263885
  0.20444918]
Sparsity at: 0.5116013948497854
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5594e-08 - accuracy: 1.0000 - val_loss: 0.3272 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.0730124   0.97574115
  0.20511667]
Sparsity at: 0.5116013948497854
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0005e-08 - accuracy: 1.0000 - val_loss: 0.3281 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.0756495   0.97860163
  0.20574787]
Sparsity at: 0.5116013948497854
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 8.5078e-08 - accuracy: 1.0000 - val_loss: 0.3291 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.078089    0.98135614
  0.2064157 ]
Sparsity at: 0.5116013948497854
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.344600686924764
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.5727802309721852
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 1. 0. ... 0. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 1.54758833033614
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 8.0526e-08 - accuracy: 1.0000 - val_loss: 0.3301 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.0805283   0.9840386
  0.20703177]
Sparsity at: 0.5116013948497854
Epoch 202/500
235/235 [==============================] - 2s 7ms/step - loss: 7.6006e-08 - accuracy: 1.0000 - val_loss: 0.3309 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.0828108   0.9865489
  0.20759048]
Sparsity at: 0.5116013948497854
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2247e-08 - accuracy: 1.0000 - val_loss: 0.3317 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.0850298   0.98894155
  0.20825376]
Sparsity at: 0.5116013948497854
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8913e-08 - accuracy: 1.0000 - val_loss: 0.3327 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.08705     0.9913236
  0.20887177]
Sparsity at: 0.5116013948497854
Epoch 205/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5237e-08 - accuracy: 1.0000 - val_loss: 0.3330 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.0891968   0.99357665
  0.20931752]
Sparsity at: 0.5116013948497854
Epoch 206/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2372e-08 - accuracy: 1.0000 - val_loss: 0.3335 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.0913095   0.9958616
  0.20984542]
Sparsity at: 0.5116013948497854
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9698e-08 - accuracy: 1.0000 - val_loss: 0.3344 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.0931442   0.9980131
  0.21035486]
Sparsity at: 0.5116013948497854
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7024e-08 - accuracy: 1.0000 - val_loss: 0.3351 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.0949134   1.0000789
  0.21077304]
Sparsity at: 0.5116013948497854
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 5.4586e-08 - accuracy: 1.0000 - val_loss: 0.3357 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.0967585   1.0020877
  0.21117713]
Sparsity at: 0.5116013948497854
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2241e-08 - accuracy: 1.0000 - val_loss: 0.3363 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.098568    1.0040693
  0.21163705]
Sparsity at: 0.5116013948497854
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0231e-08 - accuracy: 1.0000 - val_loss: 0.3369 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1002375   1.0059892
  0.21212219]
Sparsity at: 0.5116013948497854
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8280e-08 - accuracy: 1.0000 - val_loss: 0.3373 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1018791   1.007808
  0.21259817]
Sparsity at: 0.5116013948497854
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6511e-08 - accuracy: 1.0000 - val_loss: 0.3380 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1034169   1.0095576
  0.21301037]
Sparsity at: 0.5116013948497854
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4795e-08 - accuracy: 1.0000 - val_loss: 0.3387 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1048678   1.0112716
  0.21335705]
Sparsity at: 0.5116013948497854
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3227e-08 - accuracy: 1.0000 - val_loss: 0.3392 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1063703   1.0129441
  0.21372066]
Sparsity at: 0.5116013948497854
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1707e-08 - accuracy: 1.0000 - val_loss: 0.3397 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1078496   1.0145354
  0.21414009]
Sparsity at: 0.5116013948497854
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 4.0279e-08 - accuracy: 1.0000 - val_loss: 0.3401 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1092386   1.0160819
  0.21459222]
Sparsity at: 0.5116013948497854
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9128e-08 - accuracy: 1.0000 - val_loss: 0.3407 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1105025   1.017619
  0.21491283]
Sparsity at: 0.5116013948497854
Epoch 219/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7944e-08 - accuracy: 1.0000 - val_loss: 0.3411 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1118143   1.019124
  0.21521968]
Sparsity at: 0.5116013948497854
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6730e-08 - accuracy: 1.0000 - val_loss: 0.3415 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1130732   1.0205246
  0.21555029]
Sparsity at: 0.5116013948497854
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5640e-08 - accuracy: 1.0000 - val_loss: 0.3420 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1143157   1.0219153
  0.21584727]
Sparsity at: 0.5116013948497854
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4634e-08 - accuracy: 1.0000 - val_loss: 0.3423 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.11549     1.0233265
  0.2160833 ]
Sparsity at: 0.5116013948497854
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3673e-08 - accuracy: 1.0000 - val_loss: 0.3426 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1166044   1.0246931
  0.2164341 ]
Sparsity at: 0.5116013948497854
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2781e-08 - accuracy: 1.0000 - val_loss: 0.3431 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1176683   1.0259467
  0.21675147]
Sparsity at: 0.5116013948497854
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1938e-08 - accuracy: 1.0000 - val_loss: 0.3435 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1187712   1.0271915
  0.21707678]
Sparsity at: 0.5116013948497854
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1012e-08 - accuracy: 1.0000 - val_loss: 0.3439 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.119807    1.0284107
  0.21735293]
Sparsity at: 0.5116013948497854
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0265e-08 - accuracy: 1.0000 - val_loss: 0.3443 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1207848   1.0295526
  0.21773078]
Sparsity at: 0.5116013948497854
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9576e-08 - accuracy: 1.0000 - val_loss: 0.3448 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1217548   1.0306932
  0.21808322]
Sparsity at: 0.5116013948497854
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8843e-08 - accuracy: 1.0000 - val_loss: 0.3452 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.122732    1.0317628
  0.2184578 ]
Sparsity at: 0.5116013948497854
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8199e-08 - accuracy: 1.0000 - val_loss: 0.3456 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1236588   1.0328342
  0.21874437]
Sparsity at: 0.5116013948497854
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7643e-08 - accuracy: 1.0000 - val_loss: 0.3458 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1246171   1.0338436
  0.21902342]
Sparsity at: 0.5116013948497854
Epoch 232/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6900e-08 - accuracy: 1.0000 - val_loss: 0.3462 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1254479   1.0348175
  0.2193446 ]
Sparsity at: 0.5116013948497854
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6361e-08 - accuracy: 1.0000 - val_loss: 0.3467 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1262771   1.0358253
  0.21963872]
Sparsity at: 0.5116013948497854
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 2.5837e-08 - accuracy: 1.0000 - val_loss: 0.3471 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1271682   1.0367985
  0.2199002 ]
Sparsity at: 0.5116013948497854
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5384e-08 - accuracy: 1.0000 - val_loss: 0.3474 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1279825   1.037672
  0.2201489 ]
Sparsity at: 0.5116013948497854
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4867e-08 - accuracy: 1.0000 - val_loss: 0.3477 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1287953   1.0385947
  0.22049552]
Sparsity at: 0.5116013948497854
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4412e-08 - accuracy: 1.0000 - val_loss: 0.3480 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1296275   1.0394884
  0.22080153]
Sparsity at: 0.5116013948497854
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3899e-08 - accuracy: 1.0000 - val_loss: 0.3485 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1303983   1.0403681
  0.2210858 ]
Sparsity at: 0.5116013948497854
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3488e-08 - accuracy: 1.0000 - val_loss: 0.3487 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1311212   1.0412334
  0.22144732]
Sparsity at: 0.5116013948497854
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 2.3045e-08 - accuracy: 1.0000 - val_loss: 0.3490 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1319119   1.042071
  0.2217155 ]
Sparsity at: 0.5116013948497854
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2688e-08 - accuracy: 1.0000 - val_loss: 0.3492 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1326574   1.0429013
  0.22208643]
Sparsity at: 0.5116013948497854
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2153e-08 - accuracy: 1.0000 - val_loss: 0.3497 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1334141   1.043686
  0.22234629]
Sparsity at: 0.5116013948497854
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1793e-08 - accuracy: 1.0000 - val_loss: 0.3501 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1341418   1.0444715
  0.22267023]
Sparsity at: 0.5116013948497854
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1398e-08 - accuracy: 1.0000 - val_loss: 0.3504 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1348488   1.0452358
  0.2229607 ]
Sparsity at: 0.5116013948497854
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0999e-08 - accuracy: 1.0000 - val_loss: 0.3506 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1356156   1.0459969
  0.22321673]
Sparsity at: 0.5116013948497854
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0607e-08 - accuracy: 1.0000 - val_loss: 0.3509 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.136392    1.046743
  0.22343148]
Sparsity at: 0.5116013948497854
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0289e-08 - accuracy: 1.0000 - val_loss: 0.3511 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1371075   1.0474757
  0.22366564]
Sparsity at: 0.5116013948497854
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0023e-08 - accuracy: 1.0000 - val_loss: 0.3516 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.1378107   1.0482478
  0.22384568]
Sparsity at: 0.5116013948497854
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9618e-08 - accuracy: 1.0000 - val_loss: 0.3520 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.13852     1.0489675
  0.22405006]
Sparsity at: 0.5116013948497854
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9294e-08 - accuracy: 1.0000 - val_loss: 0.3520 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.1392056   1.0496923
  0.224286  ]
Sparsity at: 0.5116013948497854
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.46331068865162095
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.7079917295219857
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 1. 0. ... 0. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 1.8481063288886617
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 1.8974e-08 - accuracy: 1.0000 - val_loss: 0.3524 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.1398543   1.0504013
  0.2245038 ]
Sparsity at: 0.5116013948497854
Epoch 252/500
235/235 [==============================] - 2s 7ms/step - loss: 1.8587e-08 - accuracy: 1.0000 - val_loss: 0.3528 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.1405212   1.0511165
  0.2246761 ]
Sparsity at: 0.5116013948497854
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8295e-08 - accuracy: 1.0000 - val_loss: 0.3533 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.141181    1.0517938
  0.22482622]
Sparsity at: 0.5116013948497854
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7889e-08 - accuracy: 1.0000 - val_loss: 0.3534 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.1418693   1.0524273
  0.22497855]
Sparsity at: 0.5116013948497854
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7687e-08 - accuracy: 1.0000 - val_loss: 0.3539 - val_accuracy: 0.9712
[ 0.08002246  0.         -0.07110295 ...  1.1424868   1.0530823
  0.22512934]
Sparsity at: 0.5116013948497854
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7349e-08 - accuracy: 1.0000 - val_loss: 0.3541 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.1430708   1.0536879
  0.22526829]
Sparsity at: 0.5116013948497854
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7073e-08 - accuracy: 1.0000 - val_loss: 0.3546 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1436889   1.0543123
  0.22537068]
Sparsity at: 0.5116013948497854
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6755e-08 - accuracy: 1.0000 - val_loss: 0.3546 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1442436   1.0548899
  0.22551091]
Sparsity at: 0.5116013948497854
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6437e-08 - accuracy: 1.0000 - val_loss: 0.3551 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1448274   1.0555041
  0.22562212]
Sparsity at: 0.5116013948497854
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6250e-08 - accuracy: 1.0000 - val_loss: 0.3554 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1454196   1.056139
  0.22576065]
Sparsity at: 0.5116013948497854
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6057e-08 - accuracy: 1.0000 - val_loss: 0.3555 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1460092   1.056765
  0.22587511]
Sparsity at: 0.5116013948497854
Epoch 262/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5769e-08 - accuracy: 1.0000 - val_loss: 0.3559 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.1465688   1.0573351
  0.22598183]
Sparsity at: 0.5116013948497854
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5489e-08 - accuracy: 1.0000 - val_loss: 0.3559 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1471224   1.0579317
  0.22611773]
Sparsity at: 0.5116013948497854
Epoch 264/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5263e-08 - accuracy: 1.0000 - val_loss: 0.3562 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1476395   1.0584978
  0.22617161]
Sparsity at: 0.5116013948497854
Epoch 265/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5086e-08 - accuracy: 1.0000 - val_loss: 0.3565 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1481807   1.059052
  0.22631523]
Sparsity at: 0.5116013948497854
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4897e-08 - accuracy: 1.0000 - val_loss: 0.3567 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1487033   1.0595977
  0.22646254]
Sparsity at: 0.5116013948497854
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4629e-08 - accuracy: 1.0000 - val_loss: 0.3568 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1492277   1.0601337
  0.22659662]
Sparsity at: 0.5116013948497854
Epoch 268/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4377e-08 - accuracy: 1.0000 - val_loss: 0.3569 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1497445   1.0606531
  0.22675298]
Sparsity at: 0.5116013948497854
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4140e-08 - accuracy: 1.0000 - val_loss: 0.3571 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1502373   1.0611734
  0.22686619]
Sparsity at: 0.5116013948497854
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3985e-08 - accuracy: 1.0000 - val_loss: 0.3572 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1507502   1.0617042
  0.2270409 ]
Sparsity at: 0.5116013948497854
Epoch 271/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3814e-08 - accuracy: 1.0000 - val_loss: 0.3574 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1512592   1.0621785
  0.22721252]
Sparsity at: 0.5116013948497854
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3592e-08 - accuracy: 1.0000 - val_loss: 0.3573 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1517406   1.0626566
  0.22742122]
Sparsity at: 0.5116013948497854
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3361e-08 - accuracy: 1.0000 - val_loss: 0.3577 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1522075   1.0631145
  0.22755119]
Sparsity at: 0.5116013948497854
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3159e-08 - accuracy: 1.0000 - val_loss: 0.3577 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1526642   1.0635879
  0.22772576]
Sparsity at: 0.5116013948497854
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3008e-08 - accuracy: 1.0000 - val_loss: 0.3576 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1530999   1.0640544
  0.22791645]
Sparsity at: 0.5116013948497854
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2855e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1535734   1.0645125
  0.22805011]
Sparsity at: 0.5116013948497854
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2716e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1540412   1.064975
  0.2281984 ]
Sparsity at: 0.5116013948497854
Epoch 278/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2483e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1544703   1.065438
  0.22834817]
Sparsity at: 0.5116013948497854
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2300e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1549065   1.065873
  0.22848694]
Sparsity at: 0.5116013948497854
Epoch 280/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2137e-08 - accuracy: 1.0000 - val_loss: 0.3580 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1553357   1.0662625
  0.2285925 ]
Sparsity at: 0.5116013948497854
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2026e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1557691   1.0666908
  0.22869377]
Sparsity at: 0.5116013948497854
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1869e-08 - accuracy: 1.0000 - val_loss: 0.3581 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1561841   1.0671219
  0.22881536]
Sparsity at: 0.5116013948497854
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1667e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1565789   1.0675455
  0.22889107]
Sparsity at: 0.5116013948497854
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1535e-08 - accuracy: 1.0000 - val_loss: 0.3579 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1569626   1.0679674
  0.22898288]
Sparsity at: 0.5116013948497854
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1377e-08 - accuracy: 1.0000 - val_loss: 0.3579 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1573323   1.0683653
  0.22908865]
Sparsity at: 0.5116013948497854
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1192e-08 - accuracy: 1.0000 - val_loss: 0.3581 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1577159   1.0687848
  0.22916555]
Sparsity at: 0.5116013948497854
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1112e-08 - accuracy: 1.0000 - val_loss: 0.3581 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1581126   1.0691932
  0.22928175]
Sparsity at: 0.5116013948497854
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0943e-08 - accuracy: 1.0000 - val_loss: 0.3582 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1585153   1.0696371
  0.22935446]
Sparsity at: 0.5116013948497854
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0798e-08 - accuracy: 1.0000 - val_loss: 0.3581 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1589208   1.07005
  0.22944085]
Sparsity at: 0.5116013948497854
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0647e-08 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1592777   1.0704864
  0.22951971]
Sparsity at: 0.5116013948497854
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0540e-08 - accuracy: 1.0000 - val_loss: 0.3582 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1596526   1.0708917
  0.22960599]
Sparsity at: 0.5116013948497854
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0399e-08 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1600301   1.0712835
  0.22967878]
Sparsity at: 0.5116013948497854
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0286e-08 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1604029   1.0717083
  0.22971834]
Sparsity at: 0.5116013948497854
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0125e-08 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1607882   1.0720965
  0.2297882 ]
Sparsity at: 0.5116013948497854
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0010e-08 - accuracy: 1.0000 - val_loss: 0.3582 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1611373   1.0724416
  0.22984193]
Sparsity at: 0.5116013948497854
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 9.8864e-09 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1614971   1.0728202
  0.22986944]
Sparsity at: 0.5116013948497854
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7255e-09 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1618346   1.0731399
  0.22988464]
Sparsity at: 0.5116013948497854
Epoch 298/500
235/235 [==============================] - 2s 8ms/step - loss: 9.6639e-09 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1621814   1.0734906
  0.22993895]
Sparsity at: 0.5116013948497854
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5089e-09 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1625262   1.0738602
  0.22993574]
Sparsity at: 0.5116013948497854
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 9.4354e-09 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.162815    1.0741893
  0.22995822]
Sparsity at: 0.5116013948497854
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.5862329343481534
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.8287602642377081
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 1. 0. ... 0. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 2.051610510989633
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 42s 7ms/step - loss: 9.3341e-09 - accuracy: 1.0000 - val_loss: 0.3588 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1631196   1.0745474
  0.22998029]
Sparsity at: 0.5116013948497854
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 9.2645e-09 - accuracy: 1.0000 - val_loss: 0.3588 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1634215   1.0748631
  0.2300257 ]
Sparsity at: 0.5116013948497854
Epoch 303/500
235/235 [==============================] - 2s 9ms/step - loss: 9.1811e-09 - accuracy: 1.0000 - val_loss: 0.3588 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1637248   1.0751971
  0.23003697]
Sparsity at: 0.5116013948497854
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0659e-09 - accuracy: 1.0000 - val_loss: 0.3589 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.164049    1.0755434
  0.2300626 ]
Sparsity at: 0.5116013948497854
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0082e-09 - accuracy: 1.0000 - val_loss: 0.3590 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1643308   1.0759168
  0.23008688]
Sparsity at: 0.5116013948497854
Epoch 306/500
235/235 [==============================] - 2s 9ms/step - loss: 8.8771e-09 - accuracy: 1.0000 - val_loss: 0.3590 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1646442   1.0762503
  0.2300713 ]
Sparsity at: 0.5116013948497854
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 8.7877e-09 - accuracy: 1.0000 - val_loss: 0.3590 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1649318   1.0765939
  0.23008683]
Sparsity at: 0.5116013948497854
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 8.7380e-09 - accuracy: 1.0000 - val_loss: 0.3592 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1651964   1.0769114
  0.23008506]
Sparsity at: 0.5116013948497854
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 8.6248e-09 - accuracy: 1.0000 - val_loss: 0.3592 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1654812   1.0772346
  0.23009798]
Sparsity at: 0.5116013948497854
Epoch 310/500
235/235 [==============================] - 2s 9ms/step - loss: 8.5950e-09 - accuracy: 1.0000 - val_loss: 0.3594 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.165768    1.0775476
  0.23010552]
Sparsity at: 0.5116013948497854
Epoch 311/500
235/235 [==============================] - 2s 9ms/step - loss: 8.5135e-09 - accuracy: 1.0000 - val_loss: 0.3593 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.16606     1.0778714
  0.23012757]
Sparsity at: 0.5116013948497854
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 8.4519e-09 - accuracy: 1.0000 - val_loss: 0.3594 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1663413   1.0781776
  0.23012309]
Sparsity at: 0.5116013948497854
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3427e-09 - accuracy: 1.0000 - val_loss: 0.3594 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1666454   1.0784657
  0.2301197 ]
Sparsity at: 0.5116013948497854
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2652e-09 - accuracy: 1.0000 - val_loss: 0.3596 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1669067   1.0787494
  0.23011936]
Sparsity at: 0.5116013948497854
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 8.1937e-09 - accuracy: 1.0000 - val_loss: 0.3594 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.167183    1.0790354
  0.23013493]
Sparsity at: 0.5116013948497854
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 8.1460e-09 - accuracy: 1.0000 - val_loss: 0.3596 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1674542   1.0793331
  0.23012003]
Sparsity at: 0.5116013948497854
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 8.0665e-09 - accuracy: 1.0000 - val_loss: 0.3597 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1677039   1.0795984
  0.23011766]
Sparsity at: 0.5116013948497854
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 8.0287e-09 - accuracy: 1.0000 - val_loss: 0.3597 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1679814   1.0798817
  0.23008497]
Sparsity at: 0.5116013948497854
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9453e-09 - accuracy: 1.0000 - val_loss: 0.3597 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.1682395   1.0801301
  0.23010512]
Sparsity at: 0.5116013948497854
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8777e-09 - accuracy: 1.0000 - val_loss: 0.3598 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1685014   1.0804079
  0.23010372]
Sparsity at: 0.5116013948497854
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8241e-09 - accuracy: 1.0000 - val_loss: 0.3599 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.168765    1.0806571
  0.23010702]
Sparsity at: 0.5116013948497854
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7287e-09 - accuracy: 1.0000 - val_loss: 0.3599 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.169017    1.0808926
  0.23012121]
Sparsity at: 0.5116013948497854
Epoch 323/500
235/235 [==============================] - 2s 9ms/step - loss: 7.6890e-09 - accuracy: 1.0000 - val_loss: 0.3598 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1692684   1.0811584
  0.23013347]
Sparsity at: 0.5116013948497854
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 7.6214e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.169515    1.0814061
  0.23012125]
Sparsity at: 0.5116013948497854
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 7.5916e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1697769   1.0816578
  0.23014799]
Sparsity at: 0.5116013948497854
Epoch 326/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5301e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1700308   1.0818783
  0.23013666]
Sparsity at: 0.5116013948497854
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4844e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.1702859   1.0821365
  0.23009565]
Sparsity at: 0.5116013948497854
Epoch 328/500
235/235 [==============================] - 2s 10ms/step - loss: 7.4089e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1705389   1.0823873
  0.23011215]
Sparsity at: 0.5116013948497854
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 7.3393e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1707792   1.0826021
  0.2301243 ]
Sparsity at: 0.5116013948497854
Epoch 330/500
235/235 [==============================] - 2s 10ms/step - loss: 7.3234e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1710247   1.0828388
  0.23013248]
Sparsity at: 0.5116013948497854
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 7.2459e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1712635   1.0830818
  0.23015608]
Sparsity at: 0.5116013948497854
Epoch 332/500
235/235 [==============================] - 2s 9ms/step - loss: 7.1883e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1714829   1.083314
  0.23014392]
Sparsity at: 0.5116013948497854
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 7.1526e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1717483   1.0835366
  0.23013908]
Sparsity at: 0.5116013948497854
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0532e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1720021   1.083758
  0.23015961]
Sparsity at: 0.5116013948497854
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 6.9896e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1722404   1.0839764
  0.23014547]
Sparsity at: 0.5116013948497854
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0254e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1724933   1.0842116
  0.23015943]
Sparsity at: 0.5116013948497854
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 6.9638e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1727191   1.0844092
  0.23015836]
Sparsity at: 0.5116013948497854
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 6.8804e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1729624   1.0846213
  0.23015898]
Sparsity at: 0.5116013948497854
Epoch 339/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8426e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1731757   1.0848234
  0.23015562]
Sparsity at: 0.5116013948497854
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8128e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.173391    1.0850374
  0.23016551]
Sparsity at: 0.5116013948497854
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7234e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1735957   1.085208
  0.23016433]
Sparsity at: 0.5116013948497854
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6916e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1738055   1.0853877
  0.23018746]
Sparsity at: 0.5116013948497854
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6241e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1740375   1.0855465
  0.23017073]
Sparsity at: 0.5116013948497854
Epoch 344/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5843e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1742513   1.0857666
  0.23018725]
Sparsity at: 0.5116013948497854
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5049e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1744566   1.0859461
  0.23016134]
Sparsity at: 0.5116013948497854
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5108e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1746689   1.086118
  0.23016985]
Sparsity at: 0.5116013948497854
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 6.4214e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1748586   1.0862857
  0.23013805]
Sparsity at: 0.5116013948497854
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 6.4492e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1750917   1.0864719
  0.23011047]
Sparsity at: 0.5116013948497854
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 6.3837e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1752926   1.0866395
  0.23011269]
Sparsity at: 0.5116013948497854
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 6.3499e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1754985   1.086781
  0.23008804]
Sparsity at: 0.5116013948497854
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.6985140141575528
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.9118689464259191
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 1. 0. ... 0. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 2.2427521612542023
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 45s 7ms/step - loss: 6.3022e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9703
[ 0.08002246  0.         -0.07110295 ...  1.1756686   1.086932
  0.23009032]
Sparsity at: 0.5116013948497854
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 6.2625e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1758631   1.0870941
  0.23010021]
Sparsity at: 0.5116013948497854
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2188e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1760731   1.0872737
  0.23007955]
Sparsity at: 0.5116013948497854
Epoch 354/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1731e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1762825   1.0874262
  0.23004282]
Sparsity at: 0.5116013948497854
Epoch 355/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1552e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1764884   1.0876025
  0.23002936]
Sparsity at: 0.5116013948497854
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0916e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.176682    1.0877767
  0.22998   ]
Sparsity at: 0.5116013948497854
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0757e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1768727   1.0879391
  0.22997256]
Sparsity at: 0.5116013948497854
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0558e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1770712   1.088086
  0.22995155]
Sparsity at: 0.5116013948497854
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9843e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1772169   1.0882255
  0.22990614]
Sparsity at: 0.5116013948497854
Epoch 360/500
235/235 [==============================] - 2s 9ms/step - loss: 5.9625e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1774082   1.0883776
  0.22988926]
Sparsity at: 0.5116013948497854
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9326e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1775806   1.088529
  0.22986946]
Sparsity at: 0.5116013948497854
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9009e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.1777604   1.0886816
  0.22983423]
Sparsity at: 0.5116013948497854
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8532e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9704
[ 0.08002246  0.         -0.07110295 ...  1.177925    1.0888423
  0.2297981 ]
Sparsity at: 0.5116013948497854
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 5.8115e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1780928   1.0889885
  0.2297826 ]
Sparsity at: 0.5116013948497854
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7975e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.178279    1.0891893
  0.22976871]
Sparsity at: 0.5116013948497854
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7161e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1784364   1.089288
  0.22973473]
Sparsity at: 0.5116013948497854
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7121e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1786097   1.0894145
  0.22971025]
Sparsity at: 0.5116013948497854
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6962e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1787666   1.0895773
  0.22966279]
Sparsity at: 0.5116013948497854
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6664e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1789384   1.0897207
  0.22962297]
Sparsity at: 0.5116013948497854
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5949e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1790723   1.0898528
  0.22957551]
Sparsity at: 0.5116013948497854
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 5.5730e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1792345   1.0900047
  0.22954822]
Sparsity at: 0.5116013948497854
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5631e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1793876   1.0901343
  0.22952662]
Sparsity at: 0.5116013948497854
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5373e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1795535   1.0902618
  0.22950345]
Sparsity at: 0.5116013948497854
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5273e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1797327   1.0903895
  0.2294904 ]
Sparsity at: 0.5116013948497854
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5015e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1798908   1.090522
  0.22947064]
Sparsity at: 0.5116013948497854
Epoch 376/500
235/235 [==============================] - 2s 9ms/step - loss: 5.3883e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1800247   1.0906402
  0.2294463 ]
Sparsity at: 0.5116013948497854
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3883e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1801739   1.0907829
  0.22942409]
Sparsity at: 0.5116013948497854
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3763e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1803253   1.090896
  0.22938801]
Sparsity at: 0.5116013948497854
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3922e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9705
[ 0.08002246  0.         -0.07110295 ...  1.1804901   1.0910399
  0.2293874 ]
Sparsity at: 0.5116013948497854
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3028e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1806077   1.0911883
  0.2293565 ]
Sparsity at: 0.5116013948497854
Epoch 381/500
235/235 [==============================] - 2s 9ms/step - loss: 5.2651e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1807554   1.0913001
  0.22934125]
Sparsity at: 0.5116013948497854
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2253e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1808928   1.0914189
  0.2293167 ]
Sparsity at: 0.5116013948497854
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2333e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1810277   1.091544
  0.22930881]
Sparsity at: 0.5116013948497854
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 5.1876e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1811461   1.0916592
  0.22930853]
Sparsity at: 0.5116013948497854
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1637e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1812891   1.0917836
  0.22929932]
Sparsity at: 0.5116013948497854
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 5.1379e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1814337   1.091906
  0.22928692]
Sparsity at: 0.5116013948497854
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1379e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1815466   1.0920157
  0.22926933]
Sparsity at: 0.5116013948497854
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0962e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1816723   1.0921186
  0.2292446 ]
Sparsity at: 0.5116013948497854
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0803e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1818118   1.0922443
  0.22923039]
Sparsity at: 0.5116013948497854
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0684e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1819423   1.0923587
  0.22922897]
Sparsity at: 0.5116013948497854
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9909e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1820474   1.0924473
  0.22919482]
Sparsity at: 0.5116013948497854
Epoch 392/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0068e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.182177    1.0925411
  0.22916345]
Sparsity at: 0.5116013948497854
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 4.9770e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.182284    1.0926626
  0.22914757]
Sparsity at: 0.5116013948497854
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 4.9253e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1824272   1.092766
  0.22911125]
Sparsity at: 0.5116013948497854
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9531e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1825545   1.092882
  0.22909163]
Sparsity at: 0.5116013948497854
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8916e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1826692   1.0930057
  0.22906953]
Sparsity at: 0.5116013948497854
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8856e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.182783    1.0931149
  0.22906645]
Sparsity at: 0.5116013948497854
Epoch 398/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8498e-09 - accuracy: 1.0000 - val_loss: 0.3612 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1828873   1.0931897
  0.22905661]
Sparsity at: 0.5116013948497854
Epoch 399/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8161e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1829892   1.0932798
  0.2290029 ]
Sparsity at: 0.5116013948497854
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8081e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1831163   1.093383
  0.22900146]
Sparsity at: 0.5116013948497854
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.7700023260681235
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 0. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [0. 0. 1. ... 1. 1. 1.]
 [0. 0. 1. ... 0. 0. 0.]
 [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.9622702994533086
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 1. 0. 1.]
 [0. 1. 0. ... 0. 0. 0.]
 ...
 [0. 1. 0. ... 0. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 2.3538345517443133
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.10703125
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 45s 7ms/step - loss: 4.8021e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1832502   1.093481
  0.2289683 ]
Sparsity at: 0.5116013948497854
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 4.8280e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1833553   1.0935773
  0.2289609 ]
Sparsity at: 0.5116013948497854
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7723e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1834666   1.0936794
  0.22891815]
Sparsity at: 0.5116013948497854
Epoch 404/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6949e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1835827   1.093777
  0.22885376]
Sparsity at: 0.5116013948497854
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6889e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1836919   1.0938524
  0.22885163]
Sparsity at: 0.5116013948497854
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6293e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1837903   1.0939678
  0.22879697]
Sparsity at: 0.5116013948497854
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6810e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1838828   1.0940553
  0.22876023]
Sparsity at: 0.5116013948497854
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6035e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1839659   1.0941321
  0.22870722]
Sparsity at: 0.5116013948497854
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6293e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1840618   1.0942456
  0.2286833 ]
Sparsity at: 0.5116013948497854
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6035e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1841618   1.0943286
  0.2286565 ]
Sparsity at: 0.5116013948497854
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5776e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1842655   1.0944079
  0.22860484]
Sparsity at: 0.5116013948497854
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5061e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1843518   1.0944543
  0.22857326]
Sparsity at: 0.5116013948497854
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5955e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1844593   1.09454
  0.22852835]
Sparsity at: 0.5116013948497854
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4942e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1845502   1.0946108
  0.22848126]
Sparsity at: 0.5116013948497854
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 4.5220e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1846448   1.0946907
  0.22844957]
Sparsity at: 0.5116013948497854
Epoch 416/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4723e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1847351   1.094755
  0.22841242]
Sparsity at: 0.5116013948497854
Epoch 417/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4684e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1848236   1.0948414
  0.22838114]
Sparsity at: 0.5116013948497854
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4346e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1849036   1.0949341
  0.22833762]
Sparsity at: 0.5116013948497854
Epoch 419/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3968e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1849825   1.0950074
  0.22828466]
Sparsity at: 0.5116013948497854
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4286e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1850585   1.0950658
  0.2282313 ]
Sparsity at: 0.5116013948497854
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3889e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1851456   1.0951359
  0.22818317]
Sparsity at: 0.5116013948497854
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3432e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1852196   1.095184
  0.22812746]
Sparsity at: 0.5116013948497854
Epoch 423/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3690e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1853039   1.0952542
  0.22809878]
Sparsity at: 0.5116013948497854
Epoch 424/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3710e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1854053   1.0953146
  0.22806346]
Sparsity at: 0.5116013948497854
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3015e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1854706   1.0954005
  0.2280058 ]
Sparsity at: 0.5116013948497854
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3273e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.185539    1.0954604
  0.22794688]
Sparsity at: 0.5116013948497854
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2637e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1856053   1.0955254
  0.22790879]
Sparsity at: 0.5116013948497854
Epoch 428/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2359e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1856817   1.0955987
  0.22786526]
Sparsity at: 0.5116013948497854
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2657e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.185754    1.0956591
  0.22780494]
Sparsity at: 0.5116013948497854
Epoch 430/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2299e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1858206   1.0957048
  0.22776026]
Sparsity at: 0.5116013948497854
Epoch 431/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2180e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1858879   1.0957677
  0.22771387]
Sparsity at: 0.5116013948497854
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2061e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.185943    1.0958374
  0.22766127]
Sparsity at: 0.5116013948497854
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2121e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1860142   1.0958829
  0.22758906]
Sparsity at: 0.5116013948497854
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1842e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1860912   1.0959262
  0.2275436 ]
Sparsity at: 0.5116013948497854
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1445e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1861256   1.0960032
  0.22748429]
Sparsity at: 0.5116013948497854
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1286e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1861954   1.0960568
  0.22743481]
Sparsity at: 0.5116013948497854
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1584e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1862712   1.096132
  0.22738832]
Sparsity at: 0.5116013948497854
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1544e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1863238   1.0961661
  0.2273484 ]
Sparsity at: 0.5116013948497854
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0670e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1863686   1.0962348
  0.2272747 ]
Sparsity at: 0.5116013948497854
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1167e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1864234   1.0962626
  0.22721869]
Sparsity at: 0.5116013948497854
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0531e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1864911   1.0963213
  0.22715443]
Sparsity at: 0.5116013948497854
Epoch 442/500
235/235 [==============================] - 2s 10ms/step - loss: 4.0571e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1865233   1.0963441
  0.22711495]
Sparsity at: 0.5116013948497854
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0213e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1865418   1.0963957
  0.22703804]
Sparsity at: 0.5116013948497854
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 4.0154e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1865777   1.0964448
  0.22699466]
Sparsity at: 0.5116013948497854
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 3.9776e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1866338   1.0965048
  0.22691755]
Sparsity at: 0.5116013948497854
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 3.9915e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1866858   1.0965532
  0.22686228]
Sparsity at: 0.5116013948497854
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9796e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1867331   1.0966113
  0.22678454]
Sparsity at: 0.5116013948497854
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9518e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1867758   1.0966474
  0.22671086]
Sparsity at: 0.5116013948497854
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9081e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1868267   1.096685
  0.22666039]
Sparsity at: 0.5116013948497854
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9359e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1868615   1.0967233
  0.22658953]
Sparsity at: 0.5116013948497854
Epoch 451/500
235/235 [==============================] - 2s 9ms/step - loss: 3.8942e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1869019   1.0967691
  0.22651814]
Sparsity at: 0.5116013948497854
Epoch 452/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9220e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1869365   1.0967869
  0.22645849]
Sparsity at: 0.5116013948497854
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8882e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1869774   1.0967975
  0.22639075]
Sparsity at: 0.5116013948497854
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8763e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1870223   1.0968598
  0.22631685]
Sparsity at: 0.5116013948497854
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8683e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.18705     1.0969089
  0.22625887]
Sparsity at: 0.5116013948497854
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8683e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1870877   1.0969284
  0.22620864]
Sparsity at: 0.5116013948497854
Epoch 457/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7968e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.187123    1.0969594
  0.22611208]
Sparsity at: 0.5116013948497854
Epoch 458/500
235/235 [==============================] - 2s 9ms/step - loss: 3.8306e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1871573   1.0970007
  0.22604588]
Sparsity at: 0.5116013948497854
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7988e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1871676   1.0970674
  0.22596222]
Sparsity at: 0.5116013948497854
Epoch 460/500
235/235 [==============================] - 2s 9ms/step - loss: 3.8147e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1872003   1.0970823
  0.225897  ]
Sparsity at: 0.5116013948497854
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7928e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1872361   1.0971226
  0.22583555]
Sparsity at: 0.5116013948497854
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7909e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1872604   1.0971763
  0.22576582]
Sparsity at: 0.5116013948497854
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7670e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.187282    1.0972104
  0.22567806]
Sparsity at: 0.5116013948497854
Epoch 464/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7352e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1873018   1.0972477
  0.22561674]
Sparsity at: 0.5116013948497854
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7690e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1873215   1.0972822
  0.22554098]
Sparsity at: 0.5116013948497854
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7352e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.187341    1.0973063
  0.2254758 ]
Sparsity at: 0.5116013948497854
Epoch 467/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7352e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1873634   1.0973277
  0.2254055 ]
Sparsity at: 0.5116013948497854
Epoch 468/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6875e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1873848   1.0973638
  0.22533146]
Sparsity at: 0.5116013948497854
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6915e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1874093   1.0973834
  0.22526506]
Sparsity at: 0.5116013948497854
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6935e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1874173   1.0974022
  0.2251915 ]
Sparsity at: 0.5116013948497854
Epoch 471/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6975e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1874381   1.0974154
  0.22511114]
Sparsity at: 0.5116013948497854
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6875e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1874311   1.0974928
  0.2250421 ]
Sparsity at: 0.5116013948497854
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5961e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1874449   1.097501
  0.2249779 ]
Sparsity at: 0.5116013948497854
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6458e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9711
[ 0.08002246  0.         -0.07110295 ...  1.1874728   1.0975285
  0.22490689]
Sparsity at: 0.5116013948497854
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6279e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1874907   1.0975664
  0.22485054]
Sparsity at: 0.5116013948497854
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6577e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1875062   1.0975842
  0.22477673]
Sparsity at: 0.5116013948497854
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5961e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9710
[ 0.08002246  0.         -0.07110295 ...  1.1875216   1.0976083
  0.22471678]
Sparsity at: 0.5116013948497854
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6041e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1875678   1.097632
  0.22464328]
Sparsity at: 0.5116013948497854
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5822e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1875643   1.0976611
  0.22458616]
Sparsity at: 0.5116013948497854
Epoch 480/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5644e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1875792   1.0977179
  0.22451063]
Sparsity at: 0.5116013948497854
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5445e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1875803   1.0977219
  0.22443463]
Sparsity at: 0.5116013948497854
Epoch 482/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5842e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1875671   1.0977522
  0.22435221]
Sparsity at: 0.5116013948497854
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5842e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1875701   1.0977567
  0.22427812]
Sparsity at: 0.5116013948497854
Epoch 484/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5226e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1875992   1.0977837
  0.22418645]
Sparsity at: 0.5116013948497854
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5604e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1876097   1.0978056
  0.22412522]
Sparsity at: 0.5116013948497854
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5306e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1876284   1.097843
  0.22404796]
Sparsity at: 0.5116013948497854
Epoch 487/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5087e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9709
[ 0.08002246  0.         -0.07110295 ...  1.1876315   1.0978713
  0.2239542 ]
Sparsity at: 0.5116013948497854
Epoch 488/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4630e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1876422   1.0978981
  0.22387853]
Sparsity at: 0.5116013948497854
Epoch 489/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5246e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9706
[ 0.08002246  0.         -0.07110295 ...  1.1876338   1.0979394
  0.2238097 ]
Sparsity at: 0.5116013948497854
Epoch 490/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5048e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1876572   1.0979918
  0.22371836]
Sparsity at: 0.5116013948497854
Epoch 491/500
235/235 [==============================] - 2s 9ms/step - loss: 3.5187e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1876634   1.0979987
  0.22366638]
Sparsity at: 0.5116013948497854
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4432e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1876688   1.0980256
  0.22361137]
Sparsity at: 0.5116013948497854
Epoch 493/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4392e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1876969   1.0980332
  0.22353478]
Sparsity at: 0.5116013948497854
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4432e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708
[ 0.08002246  0.         -0.07110295 ...  1.1876934   1.0980425
  0.22345057]
Sparsity at: 0.5116013948497854
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4412e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.187698    1.0980881
  0.22334658]
Sparsity at: 0.5116013948497854
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4829e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.187698    1.0981208
  0.223247  ]
Sparsity at: 0.5116013948497854
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4332e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1876937   1.098148
  0.2231615 ]
Sparsity at: 0.5116013948497854
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4372e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1876825   1.098143
  0.22311288]
Sparsity at: 0.5116013948497854
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4153e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1877174   1.0981876
  0.22300586]
Sparsity at: 0.5116013948497854
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4153e-09 - accuracy: 1.0000 - val_loss: 0.3599 - val_accuracy: 0.9707
[ 0.08002246  0.         -0.07110295 ...  1.1877176   1.098194
  0.22296345]
Sparsity at: 0.5116013948497854
Epoch 1/500
235/235 [==============================] - 5s 15ms/step - loss: 0.1405 - accuracy: 0.9774 - val_loss: 0.1938 - val_accuracy: 0.9614
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.5819893e-02  2.5809383e-02]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9781 - val_loss: 0.1865 - val_accuracy: 0.9656
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.7460165e-02  2.6834970e-02]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9793 - val_loss: 0.1857 - val_accuracy: 0.9668
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.4778254e-02  2.9583558e-02]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9788 - val_loss: 0.1815 - val_accuracy: 0.9680
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.3779586e-02  1.9106163e-02]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9793 - val_loss: 0.1864 - val_accuracy: 0.9642
[ 0.000000e+00 -3.575435e-34  0.000000e+00 ...  0.000000e+00 -4.446961e-02
  2.707614e-02]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9791 - val_loss: 0.1901 - val_accuracy: 0.9681
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.9915849e-02  3.1123517e-02]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9791 - val_loss: 0.1888 - val_accuracy: 0.9642
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.9868724e-02  2.9073132e-02]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9793 - val_loss: 0.1920 - val_accuracy: 0.9657
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.7668787e-02  2.6284816e-02]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9794 - val_loss: 0.1946 - val_accuracy: 0.9637
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.4260429e-02  2.8926058e-02]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9783 - val_loss: 0.2052 - val_accuracy: 0.9601
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.6977250e-02  2.2327606e-02]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1392 - accuracy: 0.9788 - val_loss: 0.1967 - val_accuracy: 0.9619
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.4309519e-02  2.2324832e-02]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1386 - accuracy: 0.9792 - val_loss: 0.2114 - val_accuracy: 0.9578
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -5.8307283e-02  1.1967044e-02]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9800 - val_loss: 0.2316 - val_accuracy: 0.9536
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -5.2278005e-02  1.6850524e-02]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9799 - val_loss: 0.2132 - val_accuracy: 0.9584
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ... -0.0000000e+00
 -4.9926020e-02  1.4892332e-02]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9787 - val_loss: 0.1958 - val_accuracy: 0.9618
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.3949567e-02  1.6402822e-02]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9798 - val_loss: 0.1872 - val_accuracy: 0.9629
[ 0.000000e+00 -3.575435e-34  0.000000e+00 ...  0.000000e+00 -5.902245e-02
  9.148761e-03]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9785 - val_loss: 0.1979 - val_accuracy: 0.9636
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.8546914e-02  7.1059274e-03]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9791 - val_loss: 0.2188 - val_accuracy: 0.9561
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -5.2030463e-02  1.6246798e-02]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9796 - val_loss: 0.1875 - val_accuracy: 0.9659
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.8732214e-02  1.3158396e-02]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9791 - val_loss: 0.1929 - val_accuracy: 0.9647
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.3758623e-02  1.7782262e-02]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9796 - val_loss: 0.1810 - val_accuracy: 0.9660
[ 0.000000e+00 -3.575435e-34  0.000000e+00 ...  0.000000e+00 -4.331368e-02
  1.785288e-02]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9801 - val_loss: 0.1847 - val_accuracy: 0.9669
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -5.3441677e-02  1.7161423e-02]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1420 - accuracy: 0.9781 - val_loss: 0.1999 - val_accuracy: 0.9634
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.9332183e-02  2.0365538e-02]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9797 - val_loss: 0.2042 - val_accuracy: 0.9610
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -5.3669270e-02  1.9014565e-02]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1407 - accuracy: 0.9785 - val_loss: 0.1851 - val_accuracy: 0.9646
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -5.7365466e-02  2.2156268e-02]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9797 - val_loss: 0.2105 - val_accuracy: 0.9597
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.5485448e-02  2.2151986e-02]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9781 - val_loss: 0.2149 - val_accuracy: 0.9583
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.8103373e-02  2.5436182e-02]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9784 - val_loss: 0.2059 - val_accuracy: 0.9623
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -5.1290769e-02  2.4999343e-02]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9795 - val_loss: 0.2014 - val_accuracy: 0.9587
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.5503071e-02  1.6207779e-02]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9796 - val_loss: 0.1819 - val_accuracy: 0.9647
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.0592499e-02  1.9432642e-02]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9784 - val_loss: 0.1789 - val_accuracy: 0.9671
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.8934489e-02  2.2640448e-02]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9791 - val_loss: 0.1934 - val_accuracy: 0.9630
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -5.0750870e-02  2.5195653e-02]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.2209 - val_accuracy: 0.9562
[ 0.000000e+00 -3.575435e-34  0.000000e+00 ...  0.000000e+00 -3.676104e-02
  1.438975e-02]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1390 - accuracy: 0.9781 - val_loss: 0.2010 - val_accuracy: 0.9615
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.0574357e-02  2.9619228e-02]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9790 - val_loss: 0.2156 - val_accuracy: 0.9590
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.5800462e-02  2.0529052e-02]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9800 - val_loss: 0.2169 - val_accuracy: 0.9560
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.3138053e-02  1.8827004e-02]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9790 - val_loss: 0.2006 - val_accuracy: 0.9603
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.4723384e-02  1.7102208e-02]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9792 - val_loss: 0.1888 - val_accuracy: 0.9635
[ 0.000000e+00 -3.575435e-34  0.000000e+00 ...  0.000000e+00 -5.657761e-02
  1.612301e-02]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9789 - val_loss: 0.2193 - val_accuracy: 0.9607
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ... -0.0000000e+00
 -7.1078889e-02  1.5133626e-02]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9791 - val_loss: 0.2171 - val_accuracy: 0.9583
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.3893430e-02  2.6169395e-02]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.1999 - val_accuracy: 0.9623
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.1034408e-02  3.1785730e-02]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9793 - val_loss: 0.2269 - val_accuracy: 0.9573
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.2622484e-02  2.5180925e-02]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9785 - val_loss: 0.2040 - val_accuracy: 0.9605
[ 0.000000e+00 -3.575435e-34  0.000000e+00 ...  0.000000e+00 -3.352122e-02
  2.172237e-02]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9790 - val_loss: 0.2299 - val_accuracy: 0.9552
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.7076078e-02  1.9145414e-02]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9793 - val_loss: 0.2083 - val_accuracy: 0.9601
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.0090825e-02  1.7505517e-02]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9800 - val_loss: 0.2244 - val_accuracy: 0.9572
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -4.1549686e-02  1.7978881e-02]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9787 - val_loss: 0.2073 - val_accuracy: 0.9623
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.6542475e-02  2.3841660e-02]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9793 - val_loss: 0.2406 - val_accuracy: 0.9491
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ... -0.0000000e+00
 -4.5478377e-02  1.3162736e-02]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9796 - val_loss: 0.2118 - val_accuracy: 0.9566
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -3.6596384e-02  2.0668095e-02]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9786 - val_loss: 0.1957 - val_accuracy: 0.9619
[ 0.0000000e+00 -3.5754350e-34  0.0000000e+00 ...  0.0000000e+00
 -2.6196167e-02  2.0447779e-02]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9787 - val_loss: 0.1797 - val_accuracy: 0.9661
[ 0.          0.          0.         ...  0.         -0.03608026
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9795 - val_loss: 0.2000 - val_accuracy: 0.9604
[ 0.          0.          0.         ... -0.         -0.02991254
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9789 - val_loss: 0.1848 - val_accuracy: 0.9659
[ 0.          0.          0.         ...  0.         -0.03709638
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1336 - accuracy: 0.9801 - val_loss: 0.2359 - val_accuracy: 0.9513
[ 0.          0.          0.         ...  0.         -0.04022135
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1372 - accuracy: 0.9786 - val_loss: 0.1925 - val_accuracy: 0.9642
[ 0.          0.          0.         ...  0.         -0.04887099
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.2083 - val_accuracy: 0.9584
[ 0.          0.          0.         ...  0.         -0.04775592
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9797 - val_loss: 0.2096 - val_accuracy: 0.9582
[ 0.          0.          0.         ...  0.         -0.03270503
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9797 - val_loss: 0.1884 - val_accuracy: 0.9633
[ 0.          0.          0.         ...  0.         -0.04266148
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9787 - val_loss: 0.1872 - val_accuracy: 0.9649
[ 0.          0.          0.         ...  0.         -0.03778715
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9802 - val_loss: 0.1939 - val_accuracy: 0.9628
[ 0.          0.          0.         ...  0.         -0.04031143
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.1835 - val_accuracy: 0.9675
[ 0.          0.          0.         ...  0.         -0.03868223
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9790 - val_loss: 0.1856 - val_accuracy: 0.9668
[ 0.          0.          0.         ...  0.         -0.05343734
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9798 - val_loss: 0.2142 - val_accuracy: 0.9558
[ 0.          0.          0.         ...  0.         -0.03885864
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9791 - val_loss: 0.1838 - val_accuracy: 0.9679
[ 0.          0.          0.         ...  0.         -0.04641924
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.2095 - val_accuracy: 0.9574
[ 0.          0.          0.         ... -0.         -0.04290385
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9793 - val_loss: 0.1787 - val_accuracy: 0.9683
[ 0.          0.          0.         ...  0.         -0.04435262
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9798 - val_loss: 0.2015 - val_accuracy: 0.9607
[ 0.          0.          0.         ...  0.         -0.04105549
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9790 - val_loss: 0.1848 - val_accuracy: 0.9652
[ 0.          0.          0.         ...  0.         -0.05309426
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9787 - val_loss: 0.1961 - val_accuracy: 0.9623
[ 0.          0.          0.         ...  0.         -0.04534542
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9801 - val_loss: 0.1926 - val_accuracy: 0.9626
[ 0.          0.          0.         ...  0.         -0.04777248
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9794 - val_loss: 0.1952 - val_accuracy: 0.9630
[ 0.          0.          0.         ...  0.         -0.05099372
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9781 - val_loss: 0.2035 - val_accuracy: 0.9616
[ 0.          0.          0.         ...  0.         -0.05138997
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9793 - val_loss: 0.1910 - val_accuracy: 0.9633
[ 0.          0.          0.         ... -0.         -0.04545542
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9793 - val_loss: 0.1896 - val_accuracy: 0.9652
[ 0.          0.          0.         ...  0.         -0.03235805
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9790 - val_loss: 0.1780 - val_accuracy: 0.9657
[ 0.         0.         0.        ...  0.        -0.0392405 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9785 - val_loss: 0.1835 - val_accuracy: 0.9677
[ 0.         0.         0.        ...  0.        -0.0377299 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9796 - val_loss: 0.2122 - val_accuracy: 0.9574
[ 0.          0.          0.         ... -0.         -0.05849489
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9797 - val_loss: 0.2065 - val_accuracy: 0.9612
[ 0.          0.          0.         ...  0.         -0.04537514
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9792 - val_loss: 0.1861 - val_accuracy: 0.9661
[ 0.         0.         0.        ...  0.        -0.0388717 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.1965 - val_accuracy: 0.9615
[ 0.          0.          0.         ...  0.         -0.04702219
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9792 - val_loss: 0.2077 - val_accuracy: 0.9578
[ 0.          0.          0.         ...  0.         -0.06113814
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9783 - val_loss: 0.1932 - val_accuracy: 0.9652
[ 0.          0.          0.         ...  0.         -0.05584861
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9791 - val_loss: 0.2110 - val_accuracy: 0.9587
[ 0.          0.          0.         ...  0.         -0.04865564
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9789 - val_loss: 0.1959 - val_accuracy: 0.9628
[ 0.          0.          0.         ...  0.         -0.03944255
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9793 - val_loss: 0.1935 - val_accuracy: 0.9619
[ 0.          0.          0.         ...  0.         -0.04088381
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9785 - val_loss: 0.2057 - val_accuracy: 0.9583
[ 0.         0.         0.        ...  0.        -0.0348785 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9796 - val_loss: 0.1990 - val_accuracy: 0.9601
[ 0.         0.         0.        ...  0.        -0.0341835 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9805 - val_loss: 0.1976 - val_accuracy: 0.9607
[ 0.          0.          0.         ...  0.         -0.02939931
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9780 - val_loss: 0.2028 - val_accuracy: 0.9613
[ 0.          0.          0.         ...  0.         -0.03214215
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9791 - val_loss: 0.1754 - val_accuracy: 0.9688
[ 0.          0.          0.         ...  0.         -0.03378633
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9786 - val_loss: 0.1905 - val_accuracy: 0.9640
[ 0.          0.          0.         ...  0.         -0.03156013
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.1734 - val_accuracy: 0.9687
[ 0.          0.          0.         ...  0.         -0.03930213
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9783 - val_loss: 0.2202 - val_accuracy: 0.9567
[ 0.          0.          0.         ...  0.         -0.03493787
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9793 - val_loss: 0.1716 - val_accuracy: 0.9683
[ 0.          0.          0.         ...  0.         -0.03642095
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9794 - val_loss: 0.1912 - val_accuracy: 0.9628
[ 0.          0.          0.         ...  0.         -0.04021035
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.1959 - val_accuracy: 0.9632
[ 0.          0.          0.         ... -0.         -0.03334786
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9789 - val_loss: 0.1876 - val_accuracy: 0.9642
[ 0.          0.          0.         ...  0.         -0.03569957
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9797 - val_loss: 0.1848 - val_accuracy: 0.9661
[ 0.          0.          0.         ... -0.         -0.03729901
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9798 - val_loss: 0.2041 - val_accuracy: 0.9608
[ 0.          0.          0.         ...  0.         -0.03978111
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9788 - val_loss: 0.1757 - val_accuracy: 0.9668
[ 0.          0.          0.         ...  0.         -0.04269115
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9789 - val_loss: 0.2182 - val_accuracy: 0.9569
[ 0.          0.          0.         ...  0.         -0.03211332
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9793 - val_loss: 0.1917 - val_accuracy: 0.9627
[ 0.          0.          0.         ...  0.         -0.03584477
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9779 - val_loss: 0.2213 - val_accuracy: 0.9544
[ 0.          0.          0.         ... -0.         -0.02591021
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9796 - val_loss: 0.2415 - val_accuracy: 0.9498
[ 0.          0.          0.         ...  0.         -0.03363626
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9794 - val_loss: 0.1966 - val_accuracy: 0.9627
[ 0.         0.         0.        ...  0.        -0.0387113 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9804 - val_loss: 0.2501 - val_accuracy: 0.9478
[ 0.          0.          0.         ... -0.         -0.03638672
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9792 - val_loss: 0.1966 - val_accuracy: 0.9614
[ 0.          0.          0.         ...  0.         -0.04177481
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9795 - val_loss: 0.2203 - val_accuracy: 0.9543
[ 0.          0.          0.         ...  0.         -0.04567624
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9798 - val_loss: 0.1739 - val_accuracy: 0.9687
[ 0.          0.          0.         ...  0.         -0.04710067
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9778 - val_loss: 0.2175 - val_accuracy: 0.9576
[ 0.          0.          0.         ... -0.         -0.04480966
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9794 - val_loss: 0.2074 - val_accuracy: 0.9607
[ 0.          0.          0.         ... -0.         -0.03574139
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9785 - val_loss: 0.1848 - val_accuracy: 0.9657
[ 0.          0.          0.         ...  0.         -0.03368543
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9794 - val_loss: 0.2126 - val_accuracy: 0.9561
[ 0.          0.          0.         ...  0.         -0.03206794
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9790 - val_loss: 0.2100 - val_accuracy: 0.9585
[ 0.          0.          0.         ...  0.         -0.02995818
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9797 - val_loss: 0.2009 - val_accuracy: 0.9599
[ 0.          0.          0.         ... -0.         -0.03515729
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9792 - val_loss: 0.2321 - val_accuracy: 0.9486
[ 0.          0.          0.         ...  0.         -0.03244941
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9783 - val_loss: 0.1979 - val_accuracy: 0.9612
[ 0.          0.          0.         ...  0.         -0.03633303
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9789 - val_loss: 0.2059 - val_accuracy: 0.9591
[ 0.          0.          0.         ...  0.         -0.02373817
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9794 - val_loss: 0.1959 - val_accuracy: 0.9618
[ 0.          0.          0.         ...  0.         -0.03560742
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9785 - val_loss: 0.1910 - val_accuracy: 0.9636
[ 0.          0.          0.         ...  0.         -0.02764485
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9786 - val_loss: 0.2353 - val_accuracy: 0.9512
[ 0.         0.         0.        ...  0.        -0.0250035 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9783 - val_loss: 0.2209 - val_accuracy: 0.9521
[ 0.          0.          0.         ...  0.         -0.02616629
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9789 - val_loss: 0.2300 - val_accuracy: 0.9536
[ 0.         0.         0.        ...  0.        -0.0341068 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9777 - val_loss: 0.1774 - val_accuracy: 0.9673
[ 0.          0.          0.         ...  0.         -0.04064483
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9805 - val_loss: 0.1971 - val_accuracy: 0.9617
[ 0.          0.          0.         ...  0.         -0.03400218
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9789 - val_loss: 0.2211 - val_accuracy: 0.9533
[ 0.          0.          0.         ...  0.         -0.03219787
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9798 - val_loss: 0.2371 - val_accuracy: 0.9542
[ 0.          0.          0.         ... -0.         -0.02586298
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9795 - val_loss: 0.2240 - val_accuracy: 0.9563
[ 0.          0.          0.         ...  0.         -0.02731429
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9796 - val_loss: 0.2160 - val_accuracy: 0.9579
[ 0.          0.          0.         ... -0.         -0.03156611
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9783 - val_loss: 0.2460 - val_accuracy: 0.9499
[ 0.          0.          0.         ... -0.         -0.03899993
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9803 - val_loss: 0.1911 - val_accuracy: 0.9615
[ 0.          0.          0.         ...  0.         -0.03075077
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9793 - val_loss: 0.2021 - val_accuracy: 0.9628
[ 0.         0.         0.        ...  0.        -0.0322665 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9800 - val_loss: 0.2052 - val_accuracy: 0.9574
[ 0.          0.          0.         ...  0.         -0.04178954
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9787 - val_loss: 0.2113 - val_accuracy: 0.9585
[ 0.          0.          0.         ...  0.         -0.03717574
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9793 - val_loss: 0.1982 - val_accuracy: 0.9617
[ 0.          0.          0.         ...  0.         -0.03805895
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9790 - val_loss: 0.2162 - val_accuracy: 0.9583
[ 0.         0.         0.        ... -0.        -0.0292711 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9798 - val_loss: 0.2060 - val_accuracy: 0.9614
[ 0.          0.          0.         ...  0.         -0.03683012
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9793 - val_loss: 0.1868 - val_accuracy: 0.9642
[ 0.          0.          0.         ... -0.         -0.03066598
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9788 - val_loss: 0.1814 - val_accuracy: 0.9651
[ 0.          0.          0.         ...  0.         -0.03452725
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9791 - val_loss: 0.2084 - val_accuracy: 0.9584
[ 0.          0.          0.         ...  0.         -0.03986276
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9792 - val_loss: 0.1961 - val_accuracy: 0.9619
[ 0.          0.          0.         ...  0.         -0.03473964
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9798 - val_loss: 0.2055 - val_accuracy: 0.9593
[ 0.          0.          0.         ...  0.         -0.03745791
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9790 - val_loss: 0.2808 - val_accuracy: 0.9366
[ 0.          0.          0.         ...  0.         -0.03350218
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9786 - val_loss: 0.1952 - val_accuracy: 0.9636
[ 0.          0.          0.         ...  0.         -0.03314424
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1310 - accuracy: 0.9802 - val_loss: 0.2308 - val_accuracy: 0.9516
[ 0.          0.          0.         ...  0.         -0.03100204
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9778 - val_loss: 0.1926 - val_accuracy: 0.9624
[ 0.          0.          0.         ...  0.         -0.03731347
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9797 - val_loss: 0.2133 - val_accuracy: 0.9567
[ 0.          0.          0.         ...  0.         -0.03904126
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9788 - val_loss: 0.1906 - val_accuracy: 0.9632
[ 0.          0.          0.         ... -0.         -0.02536202
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9791 - val_loss: 0.2044 - val_accuracy: 0.9596
[ 0.          0.          0.         ...  0.         -0.02155666
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1371 - accuracy: 0.9778 - val_loss: 0.1979 - val_accuracy: 0.9621
[ 0.          0.          0.         ...  0.         -0.03493988
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1363 - accuracy: 0.9783 - val_loss: 0.1871 - val_accuracy: 0.9650
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9774 - val_loss: 0.2038 - val_accuracy: 0.9574
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1397 - accuracy: 0.9773 - val_loss: 0.1965 - val_accuracy: 0.9627
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9785 - val_loss: 0.2090 - val_accuracy: 0.9583
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1342 - accuracy: 0.9785 - val_loss: 0.2674 - val_accuracy: 0.9458
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1367 - accuracy: 0.9781 - val_loss: 0.1965 - val_accuracy: 0.9622
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1338 - accuracy: 0.9794 - val_loss: 0.2147 - val_accuracy: 0.9575
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9790 - val_loss: 0.1858 - val_accuracy: 0.9643
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9786 - val_loss: 0.2037 - val_accuracy: 0.9587
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9786 - val_loss: 0.1873 - val_accuracy: 0.9671
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9793 - val_loss: 0.2040 - val_accuracy: 0.9605
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9798 - val_loss: 0.2254 - val_accuracy: 0.9542
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9774 - val_loss: 0.2194 - val_accuracy: 0.9547
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9793 - val_loss: 0.2154 - val_accuracy: 0.9544
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9783 - val_loss: 0.1916 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9780 - val_loss: 0.2049 - val_accuracy: 0.9597
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9786 - val_loss: 0.1868 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9785 - val_loss: 0.2415 - val_accuracy: 0.9475
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9778 - val_loss: 0.1888 - val_accuracy: 0.9640
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9794 - val_loss: 0.1804 - val_accuracy: 0.9674
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9791 - val_loss: 0.2061 - val_accuracy: 0.9568
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9786 - val_loss: 0.1819 - val_accuracy: 0.9649
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9791 - val_loss: 0.1818 - val_accuracy: 0.9653
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9784 - val_loss: 0.1927 - val_accuracy: 0.9630
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9783 - val_loss: 0.2193 - val_accuracy: 0.9571
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9793 - val_loss: 0.1863 - val_accuracy: 0.9633
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9795 - val_loss: 0.1920 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9780 - val_loss: 0.2024 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9792 - val_loss: 0.2141 - val_accuracy: 0.9596
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9799 - val_loss: 0.2036 - val_accuracy: 0.9578
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9794 - val_loss: 0.2201 - val_accuracy: 0.9560
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9792 - val_loss: 0.2141 - val_accuracy: 0.9556
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9797 - val_loss: 0.1840 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1394 - accuracy: 0.9776 - val_loss: 0.1955 - val_accuracy: 0.9639
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9785 - val_loss: 0.2150 - val_accuracy: 0.9571
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9783 - val_loss: 0.2048 - val_accuracy: 0.9615
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9791 - val_loss: 0.1938 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9770 - val_loss: 0.1926 - val_accuracy: 0.9649
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9799 - val_loss: 0.2148 - val_accuracy: 0.9550
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9792 - val_loss: 0.2084 - val_accuracy: 0.9592
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9793 - val_loss: 0.1996 - val_accuracy: 0.9594
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9795 - val_loss: 0.1972 - val_accuracy: 0.9605
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9791 - val_loss: 0.1891 - val_accuracy: 0.9622
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.2094 - val_accuracy: 0.9616
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9793 - val_loss: 0.2118 - val_accuracy: 0.9584
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9783 - val_loss: 0.2006 - val_accuracy: 0.9602
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9779 - val_loss: 0.2149 - val_accuracy: 0.9591
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9789 - val_loss: 0.2114 - val_accuracy: 0.9582
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9790 - val_loss: 0.2149 - val_accuracy: 0.9564
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9780 - val_loss: 0.2099 - val_accuracy: 0.9563
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9783 - val_loss: 0.2080 - val_accuracy: 0.9577
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9773 - val_loss: 0.1891 - val_accuracy: 0.9653
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9771 - val_loss: 0.1957 - val_accuracy: 0.9618
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9778 - val_loss: 0.1961 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9778 - val_loss: 0.2207 - val_accuracy: 0.9558
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9787 - val_loss: 0.2044 - val_accuracy: 0.9605
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9780 - val_loss: 0.1942 - val_accuracy: 0.9621
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9789 - val_loss: 0.1985 - val_accuracy: 0.9637
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9783 - val_loss: 0.1946 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9780 - val_loss: 0.1821 - val_accuracy: 0.9654
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9785 - val_loss: 0.1865 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9786 - val_loss: 0.1856 - val_accuracy: 0.9663
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9789 - val_loss: 0.1978 - val_accuracy: 0.9619
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9789 - val_loss: 0.2084 - val_accuracy: 0.9567
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9782 - val_loss: 0.2067 - val_accuracy: 0.9608
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9786 - val_loss: 0.1974 - val_accuracy: 0.9588
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9785 - val_loss: 0.2064 - val_accuracy: 0.9606
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9786 - val_loss: 0.2592 - val_accuracy: 0.9454
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9795 - val_loss: 0.1821 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9779 - val_loss: 0.1882 - val_accuracy: 0.9624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9782 - val_loss: 0.2283 - val_accuracy: 0.9557
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9775 - val_loss: 0.1998 - val_accuracy: 0.9620
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9779 - val_loss: 0.2065 - val_accuracy: 0.9604
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9778 - val_loss: 0.1870 - val_accuracy: 0.9658
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9798 - val_loss: 0.1884 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9776 - val_loss: 0.1873 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9784 - val_loss: 0.2102 - val_accuracy: 0.9582
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9775 - val_loss: 0.1967 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9782 - val_loss: 0.1920 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9778 - val_loss: 0.1909 - val_accuracy: 0.9627
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9787 - val_loss: 0.1797 - val_accuracy: 0.9666
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1416 - accuracy: 0.9765 - val_loss: 0.1969 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9791 - val_loss: 0.1931 - val_accuracy: 0.9637
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9785 - val_loss: 0.1980 - val_accuracy: 0.9618
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9787 - val_loss: 0.2148 - val_accuracy: 0.9557
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9782 - val_loss: 0.1915 - val_accuracy: 0.9622
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9777 - val_loss: 0.2043 - val_accuracy: 0.9593
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9776 - val_loss: 0.1871 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9786 - val_loss: 0.1988 - val_accuracy: 0.9617
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9786 - val_loss: 0.1987 - val_accuracy: 0.9603
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9798 - val_loss: 0.1841 - val_accuracy: 0.9639
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9764 - val_loss: 0.1801 - val_accuracy: 0.9676
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9786 - val_loss: 0.2024 - val_accuracy: 0.9591
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9783 - val_loss: 0.1753 - val_accuracy: 0.9677
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9780 - val_loss: 0.2052 - val_accuracy: 0.9591
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9794 - val_loss: 0.2224 - val_accuracy: 0.9553
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9788 - val_loss: 0.1893 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9788 - val_loss: 0.2246 - val_accuracy: 0.9543
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9786 - val_loss: 0.2129 - val_accuracy: 0.9593
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9780 - val_loss: 0.1942 - val_accuracy: 0.9640
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9780 - val_loss: 0.1902 - val_accuracy: 0.9600
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1262 - accuracy: 0.9795 - val_loss: 0.1920 - val_accuracy: 0.9604
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1263 - accuracy: 0.9793 - val_loss: 0.1764 - val_accuracy: 0.9671
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9795 - val_loss: 0.1969 - val_accuracy: 0.9587
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9802 - val_loss: 0.1912 - val_accuracy: 0.9601
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9792 - val_loss: 0.1790 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9796 - val_loss: 0.1920 - val_accuracy: 0.9593
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9795 - val_loss: 0.2045 - val_accuracy: 0.9570
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9801 - val_loss: 0.1943 - val_accuracy: 0.9596
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9794 - val_loss: 0.1782 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9801 - val_loss: 0.2084 - val_accuracy: 0.9579
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9800 - val_loss: 0.2368 - val_accuracy: 0.9460
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9803 - val_loss: 0.1733 - val_accuracy: 0.9651
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9807 - val_loss: 0.2054 - val_accuracy: 0.9576
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9793 - val_loss: 0.2175 - val_accuracy: 0.9561
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9804 - val_loss: 0.1861 - val_accuracy: 0.9615
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1210 - accuracy: 0.9802 - val_loss: 0.1946 - val_accuracy: 0.9612
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9797 - val_loss: 0.1793 - val_accuracy: 0.9655
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1171 - accuracy: 0.9809 - val_loss: 0.1917 - val_accuracy: 0.9596
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9795 - val_loss: 0.1915 - val_accuracy: 0.9603
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9804 - val_loss: 0.2073 - val_accuracy: 0.9603
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1169 - accuracy: 0.9816 - val_loss: 0.2529 - val_accuracy: 0.9428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1197 - accuracy: 0.9807 - val_loss: 0.2158 - val_accuracy: 0.9547
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9805 - val_loss: 0.1940 - val_accuracy: 0.9583
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9810 - val_loss: 0.1911 - val_accuracy: 0.9591
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9792 - val_loss: 0.2041 - val_accuracy: 0.9588
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1189 - accuracy: 0.9806 - val_loss: 0.1746 - val_accuracy: 0.9680
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9802 - val_loss: 0.1841 - val_accuracy: 0.9616
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9801 - val_loss: 0.1844 - val_accuracy: 0.9623
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9805 - val_loss: 0.1836 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9797 - val_loss: 0.1930 - val_accuracy: 0.9616
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9817 - val_loss: 0.1745 - val_accuracy: 0.9652
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9801 - val_loss: 0.1795 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9807 - val_loss: 0.2256 - val_accuracy: 0.9507
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1159 - accuracy: 0.9811 - val_loss: 0.1929 - val_accuracy: 0.9619
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9797 - val_loss: 0.1795 - val_accuracy: 0.9642
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9799 - val_loss: 0.2031 - val_accuracy: 0.9616
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9797 - val_loss: 0.1770 - val_accuracy: 0.9665
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1173 - accuracy: 0.9809 - val_loss: 0.2066 - val_accuracy: 0.9562
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9803 - val_loss: 0.1821 - val_accuracy: 0.9625
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9804 - val_loss: 0.1829 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9805 - val_loss: 0.1684 - val_accuracy: 0.9684
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9815 - val_loss: 0.1753 - val_accuracy: 0.9655
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9803 - val_loss: 0.1803 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9810 - val_loss: 0.1952 - val_accuracy: 0.9587
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9801 - val_loss: 0.2023 - val_accuracy: 0.9551
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9803 - val_loss: 0.1832 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9814 - val_loss: 0.1921 - val_accuracy: 0.9641
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9801 - val_loss: 0.1888 - val_accuracy: 0.9616
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9797 - val_loss: 0.1832 - val_accuracy: 0.9624
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9767 - val_loss: 0.1649 - val_accuracy: 0.9662
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1175 - accuracy: 0.9787 - val_loss: 0.1694 - val_accuracy: 0.9643
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1141 - accuracy: 0.9795 - val_loss: 0.1862 - val_accuracy: 0.9606
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1120 - accuracy: 0.9801 - val_loss: 0.2026 - val_accuracy: 0.9553
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1105 - accuracy: 0.9808 - val_loss: 0.1745 - val_accuracy: 0.9622
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1105 - accuracy: 0.9798 - val_loss: 0.1898 - val_accuracy: 0.9599
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1068 - accuracy: 0.9812 - val_loss: 0.1956 - val_accuracy: 0.9562
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1076 - accuracy: 0.9805 - val_loss: 0.1894 - val_accuracy: 0.9574
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1062 - accuracy: 0.9813 - val_loss: 0.1926 - val_accuracy: 0.9591
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1083 - accuracy: 0.9806 - val_loss: 0.1790 - val_accuracy: 0.9613
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1074 - accuracy: 0.9808 - val_loss: 0.1857 - val_accuracy: 0.9608
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1068 - accuracy: 0.9805 - val_loss: 0.1712 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1055 - accuracy: 0.9811 - val_loss: 0.1772 - val_accuracy: 0.9601
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1041 - accuracy: 0.9815 - val_loss: 0.1831 - val_accuracy: 0.9578
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1057 - accuracy: 0.9814 - val_loss: 0.1695 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9811 - val_loss: 0.1794 - val_accuracy: 0.9615
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1034 - accuracy: 0.9821 - val_loss: 0.1680 - val_accuracy: 0.9653
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9812 - val_loss: 0.1622 - val_accuracy: 0.9664
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1040 - accuracy: 0.9813 - val_loss: 0.1810 - val_accuracy: 0.9597
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1037 - accuracy: 0.9816 - val_loss: 0.1793 - val_accuracy: 0.9611
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1051 - accuracy: 0.9812 - val_loss: 0.1654 - val_accuracy: 0.9671
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1027 - accuracy: 0.9817 - val_loss: 0.1631 - val_accuracy: 0.9657
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1077 - accuracy: 0.9804 - val_loss: 0.1672 - val_accuracy: 0.9665
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1035 - accuracy: 0.9816 - val_loss: 0.1668 - val_accuracy: 0.9645
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9819 - val_loss: 0.1834 - val_accuracy: 0.9598
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1037 - accuracy: 0.9816 - val_loss: 0.1826 - val_accuracy: 0.9626
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1044 - accuracy: 0.9813 - val_loss: 0.1718 - val_accuracy: 0.9627
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1018 - accuracy: 0.9821 - val_loss: 0.2174 - val_accuracy: 0.9485
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1046 - accuracy: 0.9812 - val_loss: 0.1887 - val_accuracy: 0.9607
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1036 - accuracy: 0.9815 - val_loss: 0.1935 - val_accuracy: 0.9564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1057 - accuracy: 0.9810 - val_loss: 0.1993 - val_accuracy: 0.9567
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1048 - accuracy: 0.9813 - val_loss: 0.1798 - val_accuracy: 0.9621
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9818 - val_loss: 0.1805 - val_accuracy: 0.9610
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1022 - accuracy: 0.9819 - val_loss: 0.1673 - val_accuracy: 0.9657
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9813 - val_loss: 0.1819 - val_accuracy: 0.9609
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9811 - val_loss: 0.1714 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1028 - accuracy: 0.9815 - val_loss: 0.1701 - val_accuracy: 0.9678
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1030 - accuracy: 0.9817 - val_loss: 0.1815 - val_accuracy: 0.9603
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1038 - accuracy: 0.9816 - val_loss: 0.1734 - val_accuracy: 0.9654
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1044 - accuracy: 0.9808 - val_loss: 0.1744 - val_accuracy: 0.9634
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1033 - accuracy: 0.9814 - val_loss: 0.1876 - val_accuracy: 0.9588
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1025 - accuracy: 0.9818 - val_loss: 0.1923 - val_accuracy: 0.9583
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1035 - accuracy: 0.9815 - val_loss: 0.1850 - val_accuracy: 0.9590
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1044 - accuracy: 0.9804 - val_loss: 0.1936 - val_accuracy: 0.9584
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1032 - accuracy: 0.9811 - val_loss: 0.1654 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1037 - accuracy: 0.9813 - val_loss: 0.1855 - val_accuracy: 0.9595
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9809 - val_loss: 0.1699 - val_accuracy: 0.9663
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1031 - accuracy: 0.9814 - val_loss: 0.1648 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1019 - accuracy: 0.9819 - val_loss: 0.2028 - val_accuracy: 0.9569
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1036 - accuracy: 0.9816 - val_loss: 0.1639 - val_accuracy: 0.9640
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9745 - val_loss: 0.1556 - val_accuracy: 0.9657
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1083 - accuracy: 0.9785 - val_loss: 0.1509 - val_accuracy: 0.9698
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1066 - accuracy: 0.9789 - val_loss: 0.1618 - val_accuracy: 0.9659
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1038 - accuracy: 0.9798 - val_loss: 0.1746 - val_accuracy: 0.9621
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1026 - accuracy: 0.9794 - val_loss: 0.1760 - val_accuracy: 0.9604
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1032 - accuracy: 0.9792 - val_loss: 0.1584 - val_accuracy: 0.9682
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1025 - accuracy: 0.9799 - val_loss: 0.1638 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1017 - accuracy: 0.9802 - val_loss: 0.1670 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1026 - accuracy: 0.9796 - val_loss: 0.1667 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9808 - val_loss: 0.1604 - val_accuracy: 0.9651
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0999 - accuracy: 0.9801 - val_loss: 0.1686 - val_accuracy: 0.9666
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1000 - accuracy: 0.9798 - val_loss: 0.1655 - val_accuracy: 0.9649
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0985 - accuracy: 0.9804 - val_loss: 0.1744 - val_accuracy: 0.9651
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1015 - accuracy: 0.9797 - val_loss: 0.1689 - val_accuracy: 0.9649
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1004 - accuracy: 0.9798 - val_loss: 0.1690 - val_accuracy: 0.9642
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0976 - accuracy: 0.9809 - val_loss: 0.1569 - val_accuracy: 0.9674
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9808 - val_loss: 0.1675 - val_accuracy: 0.9670
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0992 - accuracy: 0.9797 - val_loss: 0.1520 - val_accuracy: 0.9691
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0989 - accuracy: 0.9806 - val_loss: 0.1608 - val_accuracy: 0.9671
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9801 - val_loss: 0.1517 - val_accuracy: 0.9693
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0995 - accuracy: 0.9803 - val_loss: 0.1685 - val_accuracy: 0.9651
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9803 - val_loss: 0.1524 - val_accuracy: 0.9692
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0967 - accuracy: 0.9814 - val_loss: 0.1587 - val_accuracy: 0.9662
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0962 - accuracy: 0.9815 - val_loss: 0.1569 - val_accuracy: 0.9665
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0998 - accuracy: 0.9800 - val_loss: 0.1679 - val_accuracy: 0.9652
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0979 - accuracy: 0.9804 - val_loss: 0.1644 - val_accuracy: 0.9652
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9807 - val_loss: 0.1603 - val_accuracy: 0.9684
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9803 - val_loss: 0.1652 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9800 - val_loss: 0.1699 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0963 - accuracy: 0.9814 - val_loss: 0.1523 - val_accuracy: 0.9700
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9801 - val_loss: 0.1709 - val_accuracy: 0.9650
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0972 - accuracy: 0.9803 - val_loss: 0.1582 - val_accuracy: 0.9667
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0987 - accuracy: 0.9802 - val_loss: 0.1697 - val_accuracy: 0.9642
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9810 - val_loss: 0.1620 - val_accuracy: 0.9645
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0960 - accuracy: 0.9816 - val_loss: 0.1679 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0971 - accuracy: 0.9807 - val_loss: 0.1586 - val_accuracy: 0.9673
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0974 - accuracy: 0.9805 - val_loss: 0.1644 - val_accuracy: 0.9653
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0998 - accuracy: 0.9800 - val_loss: 0.1711 - val_accuracy: 0.9649
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9804 - val_loss: 0.1658 - val_accuracy: 0.9655
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0989 - accuracy: 0.9806 - val_loss: 0.1615 - val_accuracy: 0.9674
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0969 - accuracy: 0.9809 - val_loss: 0.1647 - val_accuracy: 0.9659
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0970 - accuracy: 0.9806 - val_loss: 0.1716 - val_accuracy: 0.9634
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0973 - accuracy: 0.9806 - val_loss: 0.1684 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9812 - val_loss: 0.1639 - val_accuracy: 0.9671
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9808 - val_loss: 0.1524 - val_accuracy: 0.9706
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9801 - val_loss: 0.1584 - val_accuracy: 0.9697
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0973 - accuracy: 0.9807 - val_loss: 0.1618 - val_accuracy: 0.9657
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0973 - accuracy: 0.9804 - val_loss: 0.1596 - val_accuracy: 0.9688
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0968 - accuracy: 0.9810 - val_loss: 0.1815 - val_accuracy: 0.9627
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9807 - val_loss: 0.1632 - val_accuracy: 0.9678
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9743 - val_loss: 0.1456 - val_accuracy: 0.9691
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1063 - accuracy: 0.9773 - val_loss: 0.1486 - val_accuracy: 0.9665
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1039 - accuracy: 0.9785 - val_loss: 0.1426 - val_accuracy: 0.9696
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1028 - accuracy: 0.9785 - val_loss: 0.1492 - val_accuracy: 0.9667
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1028 - accuracy: 0.9782 - val_loss: 0.1513 - val_accuracy: 0.9671
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1024 - accuracy: 0.9789 - val_loss: 0.1431 - val_accuracy: 0.9688
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1018 - accuracy: 0.9786 - val_loss: 0.1404 - val_accuracy: 0.9689
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1017 - accuracy: 0.9785 - val_loss: 0.1416 - val_accuracy: 0.9688
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1009 - accuracy: 0.9786 - val_loss: 0.1400 - val_accuracy: 0.9696
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1014 - accuracy: 0.9785 - val_loss: 0.1506 - val_accuracy: 0.9658
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1008 - accuracy: 0.9791 - val_loss: 0.1384 - val_accuracy: 0.9686
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0995 - accuracy: 0.9792 - val_loss: 0.1426 - val_accuracy: 0.9693
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0990 - accuracy: 0.9794 - val_loss: 0.1381 - val_accuracy: 0.9688
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0998 - accuracy: 0.9786 - val_loss: 0.1393 - val_accuracy: 0.9695
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1003 - accuracy: 0.9785 - val_loss: 0.1441 - val_accuracy: 0.9671
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1001 - accuracy: 0.9789 - val_loss: 0.1378 - val_accuracy: 0.9699
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1000 - accuracy: 0.9791 - val_loss: 0.1459 - val_accuracy: 0.9655
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9792 - val_loss: 0.1454 - val_accuracy: 0.9677
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9785 - val_loss: 0.1458 - val_accuracy: 0.9681
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1001 - accuracy: 0.9787 - val_loss: 0.1368 - val_accuracy: 0.9702
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9784 - val_loss: 0.1410 - val_accuracy: 0.9686
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9793 - val_loss: 0.1335 - val_accuracy: 0.9700
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9785 - val_loss: 0.1404 - val_accuracy: 0.9688
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0988 - accuracy: 0.9791 - val_loss: 0.1407 - val_accuracy: 0.9681
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0986 - accuracy: 0.9789 - val_loss: 0.1409 - val_accuracy: 0.9685
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9790 - val_loss: 0.1428 - val_accuracy: 0.9689
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0987 - accuracy: 0.9791 - val_loss: 0.1527 - val_accuracy: 0.9665
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0990 - accuracy: 0.9791 - val_loss: 0.1457 - val_accuracy: 0.9666
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9787 - val_loss: 0.1442 - val_accuracy: 0.9677
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9792 - val_loss: 0.1430 - val_accuracy: 0.9686
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0996 - accuracy: 0.9790 - val_loss: 0.1416 - val_accuracy: 0.9679
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9790 - val_loss: 0.1414 - val_accuracy: 0.9669
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9791 - val_loss: 0.1456 - val_accuracy: 0.9669
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9800 - val_loss: 0.1405 - val_accuracy: 0.9699
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0988 - accuracy: 0.9791 - val_loss: 0.1419 - val_accuracy: 0.9684
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9794 - val_loss: 0.1459 - val_accuracy: 0.9664
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0996 - accuracy: 0.9791 - val_loss: 0.1470 - val_accuracy: 0.9680
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9796 - val_loss: 0.1421 - val_accuracy: 0.9682
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9795 - val_loss: 0.1620 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9789 - val_loss: 0.1482 - val_accuracy: 0.9674
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9791 - val_loss: 0.1375 - val_accuracy: 0.9701
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9795 - val_loss: 0.1467 - val_accuracy: 0.9675
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9796 - val_loss: 0.1480 - val_accuracy: 0.9664
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0994 - accuracy: 0.9787 - val_loss: 0.1486 - val_accuracy: 0.9673
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9793 - val_loss: 0.1404 - val_accuracy: 0.9675
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0975 - accuracy: 0.9792 - val_loss: 0.1394 - val_accuracy: 0.9684
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0975 - accuracy: 0.9794 - val_loss: 0.1418 - val_accuracy: 0.9683
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9790 - val_loss: 0.1475 - val_accuracy: 0.9677
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0989 - accuracy: 0.9791 - val_loss: 0.1525 - val_accuracy: 0.9651
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0990 - accuracy: 0.9789 - val_loss: 0.1441 - val_accuracy: 0.9667
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0999 - accuracy: 0.9784 - val_loss: 0.1466 - val_accuracy: 0.9670
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0979 - accuracy: 0.9794 - val_loss: 0.1393 - val_accuracy: 0.9686
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0994 - accuracy: 0.9787 - val_loss: 0.1467 - val_accuracy: 0.9665
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0975 - accuracy: 0.9794 - val_loss: 0.1443 - val_accuracy: 0.9673
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1001 - accuracy: 0.9785 - val_loss: 0.1395 - val_accuracy: 0.9683
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0990 - accuracy: 0.9788 - val_loss: 0.1422 - val_accuracy: 0.9670
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9790 - val_loss: 0.1468 - val_accuracy: 0.9669
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0986 - accuracy: 0.9795 - val_loss: 0.1394 - val_accuracy: 0.9702
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0999 - accuracy: 0.9780 - val_loss: 0.1424 - val_accuracy: 0.9683
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9788 - val_loss: 0.1355 - val_accuracy: 0.9705
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0981 - accuracy: 0.9791 - val_loss: 0.1431 - val_accuracy: 0.9674
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0985 - accuracy: 0.9790 - val_loss: 0.1418 - val_accuracy: 0.9682
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0986 - accuracy: 0.9788 - val_loss: 0.1493 - val_accuracy: 0.9671
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9793 - val_loss: 0.1442 - val_accuracy: 0.9670
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0993 - accuracy: 0.9789 - val_loss: 0.1427 - val_accuracy: 0.9690
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0993 - accuracy: 0.9791 - val_loss: 0.1432 - val_accuracy: 0.9673
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0978 - accuracy: 0.9793 - val_loss: 0.1468 - val_accuracy: 0.9668
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9789 - val_loss: 0.1378 - val_accuracy: 0.9707
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9789 - val_loss: 0.1466 - val_accuracy: 0.9668
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0989 - accuracy: 0.9792 - val_loss: 0.1449 - val_accuracy: 0.9677
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0992 - accuracy: 0.9787 - val_loss: 0.1461 - val_accuracy: 0.9672
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0976 - accuracy: 0.9793 - val_loss: 0.1384 - val_accuracy: 0.9696
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0981 - accuracy: 0.9790 - val_loss: 0.1402 - val_accuracy: 0.9673
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0979 - accuracy: 0.9790 - val_loss: 0.1427 - val_accuracy: 0.9677
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0985 - accuracy: 0.9785 - val_loss: 0.1394 - val_accuracy: 0.9685
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0996 - accuracy: 0.9787 - val_loss: 0.1414 - val_accuracy: 0.9696
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9787 - val_loss: 0.1402 - val_accuracy: 0.9686
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9789 - val_loss: 0.1455 - val_accuracy: 0.9672
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9786 - val_loss: 0.1416 - val_accuracy: 0.9681
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9796 - val_loss: 0.1450 - val_accuracy: 0.9677
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9789 - val_loss: 0.1443 - val_accuracy: 0.9672
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9787 - val_loss: 0.1405 - val_accuracy: 0.9687
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0978 - accuracy: 0.9790 - val_loss: 0.1460 - val_accuracy: 0.9665
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0976 - accuracy: 0.9792 - val_loss: 0.1427 - val_accuracy: 0.9666
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0997 - accuracy: 0.9787 - val_loss: 0.1397 - val_accuracy: 0.9687
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0972 - accuracy: 0.9793 - val_loss: 0.1497 - val_accuracy: 0.9666
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1004 - accuracy: 0.9783 - val_loss: 0.1422 - val_accuracy: 0.9676
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9799 - val_loss: 0.1361 - val_accuracy: 0.9690
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9793 - val_loss: 0.1425 - val_accuracy: 0.9676
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0979 - accuracy: 0.9797 - val_loss: 0.1476 - val_accuracy: 0.9673
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0971 - accuracy: 0.9795 - val_loss: 0.1390 - val_accuracy: 0.9692
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9791 - val_loss: 0.1519 - val_accuracy: 0.9662
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0976 - accuracy: 0.9792 - val_loss: 0.1450 - val_accuracy: 0.9675
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0987 - accuracy: 0.9792 - val_loss: 0.1423 - val_accuracy: 0.9682
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9793 - val_loss: 0.1486 - val_accuracy: 0.9672
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0987 - accuracy: 0.9790 - val_loss: 0.1427 - val_accuracy: 0.9686
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9793 - val_loss: 0.1469 - val_accuracy: 0.9679
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0974 - accuracy: 0.9791 - val_loss: 0.1440 - val_accuracy: 0.9665
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9793 - val_loss: 0.1438 - val_accuracy: 0.9668
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0973 - accuracy: 0.9797 - val_loss: 0.1516 - val_accuracy: 0.9652
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 5s 15ms/step - loss: 8.1139e-04 - accuracy: 0.9998 - val_loss: 0.0883 - val_accuracy: 0.9826
[ 0.         0.        -0.        ... -0.        -0.        -0.4671491]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8021e-04 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9836
[ 0.          0.         -0.         ...  0.          0.
 -0.46529546]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1083e-04 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9825
[ 0.          0.         -0.         ...  0.          0.
 -0.46903822]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4239e-05 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9830
[ 0.         0.        -0.        ...  0.        -0.        -0.4711647]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8524e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9830
[ 0.         0.        -0.        ...  0.         0.        -0.4723889]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4405e-05 - accuracy: 1.0000 - val_loss: 0.0906 - val_accuracy: 0.9826
[ 0.          0.         -0.         ...  0.          0.
 -0.47327575]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 4s 15ms/step - loss: 1.2017e-04 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9827
[ 0.          0.         -0.         ...  0.          0.
 -0.47514236]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5489e-05 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 0.9822
[ 0.         0.        -0.        ...  0.         0.        -0.4760198]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 6.1156e-04 - accuracy: 0.9999 - val_loss: 0.1024 - val_accuracy: 0.9819
[ 0.          0.         -0.         ... -0.         -0.
 -0.50223625]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 3s 15ms/step - loss: 6.0307e-04 - accuracy: 0.9998 - val_loss: 0.1024 - val_accuracy: 0.9817
[ 0.          0.         -0.         ... -0.          0.
 -0.53111416]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9754e-04 - accuracy: 0.9998 - val_loss: 0.1012 - val_accuracy: 0.9824
[ 0.         0.        -0.        ... -0.         0.        -0.5248197]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5332e-04 - accuracy: 1.0000 - val_loss: 0.1027 - val_accuracy: 0.9824
[ 0.         0.        -0.        ... -0.         0.        -0.5346722]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6018e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9834
[ 0.        0.       -0.       ... -0.       -0.       -0.534304]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9690e-05 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9833
[ 0.          0.         -0.         ... -0.          0.
 -0.53748435]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6706e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9837
[ 0.          0.         -0.         ... -0.         -0.
 -0.53555095]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7840e-05 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9840
[ 0.        0.       -0.       ...  0.       -0.       -0.536586]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6978e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9837
[ 0.        0.       -0.       ...  0.        0.       -0.528645]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8348e-05 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9827
[ 0.         0.        -0.        ...  0.         0.        -0.5363368]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3327e-05 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9832
[ 0.          0.         -0.         ... -0.          0.
 -0.53580153]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9591e-05 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9830
[ 0.          0.         -0.         ... -0.          0.
 -0.53923535]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0101e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9829
[ 0.          0.         -0.         ... -0.          0.
 -0.53984076]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0219e-05 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9831
[ 0.        0.       -0.       ... -0.        0.       -0.540348]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 8.6337e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9830
[ 0.         0.        -0.        ...  0.         0.        -0.5417688]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 6.9942e-06 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9830
[ 0.         0.        -0.        ... -0.         0.        -0.5425944]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8343e-06 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9832
[ 0.         0.        -0.        ...  0.         0.        -0.5438243]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2881e-06 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9837
[ 0.          0.         -0.         ...  0.          0.
 -0.54764444]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1882e-06 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9835
[ 0.         0.        -0.        ... -0.         0.        -0.5485463]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1989e-06 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9832
[ 0.         0.        -0.        ...  0.         0.        -0.5491649]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2284e-06 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9833
[ 0.          0.         -0.         ...  0.          0.
 -0.55360985]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3660e-06 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9828
[ 0.         0.        -0.        ...  0.         0.        -0.5534608]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6567e-06 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9834
[ 0.         0.        -0.        ...  0.         0.        -0.5550426]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3635e-06 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9832
[ 0.          0.         -0.         ...  0.          0.
 -0.55540156]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8715e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9831
[ 0.          0.         -0.         ...  0.          0.
 -0.55539864]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9133e-06 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9834
[ 0.         0.        -0.        ... -0.         0.        -0.5557232]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2862e-06 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9832
[ 0.          0.         -0.         ...  0.          0.
 -0.55700326]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 15ms/step - loss: 2.4172e-06 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9832
[ 0.         0.        -0.        ... -0.         0.        -0.5576949]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2425e-04 - accuracy: 0.9999 - val_loss: 0.1549 - val_accuracy: 0.9767
[ 0.          0.         -0.         ...  0.          0.
 -0.53346455]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.1323 - val_accuracy: 0.9811
[ 0.        0.       -0.       ... -0.        0.       -0.562493]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2956e-04 - accuracy: 0.9998 - val_loss: 0.1259 - val_accuracy: 0.9829
[ 0.         0.        -0.        ... -0.         0.        -0.5601767]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3955e-04 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9825
[ 0.          0.         -0.         ...  0.          0.
 -0.56557643]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6135e-04 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9824
[ 0.         0.        -0.        ...  0.         0.        -0.5675069]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8136e-05 - accuracy: 1.0000 - val_loss: 0.1197 - val_accuracy: 0.9829
[ 0.         0.        -0.        ...  0.         0.        -0.5653893]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6441e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9829
[ 0.         0.        -0.        ...  0.         0.        -0.5657731]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3002e-05 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9826
[ 0.          0.         -0.         ... -0.          0.
 -0.56671613]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3028e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9832
[ 0.         0.        -0.        ... -0.         0.        -0.5710753]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4862e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9830
[ 0.          0.         -0.         ...  0.          0.
 -0.57677317]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3468e-06 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9831
[ 0.         0.        -0.        ...  0.         0.        -0.5773296]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1781e-05 - accuracy: 1.0000 - val_loss: 0.1202 - val_accuracy: 0.9836
[ 0.        0.       -0.       ...  0.       -0.       -0.576891]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 8.3234e-06 - accuracy: 1.0000 - val_loss: 0.1196 - val_accuracy: 0.9835
[ 0.         0.        -0.        ...  0.         0.        -0.5779928]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 15ms/step - loss: 9.8848e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9830
[ 0.         0.        -0.        ... -0.        -0.        -0.5803385]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0038 - accuracy: 0.9987 - val_loss: 0.1104 - val_accuracy: 0.9825
[ 0.          0.         -0.         ...  0.         -0.
 -0.61099213]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 6.9359e-04 - accuracy: 0.9998 - val_loss: 0.1066 - val_accuracy: 0.9836
[ 0.         0.        -0.        ... -0.         0.        -0.5973706]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5210e-04 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9835
[ 0.          0.         -0.         ... -0.          0.
 -0.59842896]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 8.8846e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9829
[ 0.        0.       -0.       ... -0.        0.       -0.600792]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1912e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9829
[ 0.          0.         -0.         ...  0.          0.
 -0.59894073]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1351e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9828
[ 0.         0.        -0.        ...  0.         0.        -0.6014865]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2841e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9825
[ 0.         0.        -0.        ...  0.         0.        -0.6021348]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7245e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9829
[ 0.          0.         -0.         ... -0.          0.
 -0.60381955]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6288e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9826
[ 0.         0.        -0.        ... -0.        -0.        -0.6035501]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2969e-05 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9826
[ 0.         0.        -0.        ...  0.         0.        -0.6065442]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2369e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9826
[ 0.          0.         -0.         ...  0.          0.
 -0.60562664]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7321e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9833
[ 0.         0.        -0.        ... -0.         0.        -0.6088135]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8203e-04 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9822
[ 0.        0.       -0.       ...  0.        0.       -0.607443]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7933e-05 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9835
[ 0.          0.         -0.         ...  0.          0.
 -0.61090803]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4568e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9831
[ 0.          0.         -0.         ...  0.          0.
 -0.61216223]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1277e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9831
[ 0.         0.        -0.        ...  0.        -0.        -0.6139568]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7235e-05 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9834
[ 0.          0.         -0.         ... -0.         -0.
 -0.61513203]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5118e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9834
[ 0.          0.         -0.         ... -0.         -0.
 -0.61469525]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2736e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9834
[ 0.         0.        -0.        ... -0.        -0.        -0.6140655]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2254e-05 - accuracy: 1.0000 - val_loss: 0.1095 - val_accuracy: 0.9829
[ 0.         0.        -0.        ...  0.         0.        -0.6163622]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1742e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9830
[ 0.         0.        -0.        ...  0.         0.        -0.6176249]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0528e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9835
[ 0.          0.         -0.         ...  0.         -0.
 -0.61973166]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5280e-06 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9834
[ 0.          0.         -0.         ... -0.          0.
 -0.61982465]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0174e-04 - accuracy: 0.9999 - val_loss: 0.1111 - val_accuracy: 0.9826
[ 0.          0.         -0.         ...  0.          0.
 -0.63283396]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6202e-05 - accuracy: 1.0000 - val_loss: 0.1118 - val_accuracy: 0.9828
[ 0.          0.         -0.         ...  0.         -0.
 -0.63129914]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4495e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9836
[ 0.          0.         -0.         ... -0.         -0.
 -0.63378036]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0891e-05 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9830
[ 0.          0.         -0.         ... -0.          0.
 -0.63251144]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1781e-04 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9831
[ 0.         0.        -0.        ... -0.         0.        -0.6369516]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1018e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9827
[ 0.         0.        -0.        ...  0.         0.        -0.6396962]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1593e-05 - accuracy: 1.0000 - val_loss: 0.1155 - val_accuracy: 0.9826
[ 0.         0.        -0.        ... -0.         0.        -0.6388876]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0472e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9826
[ 0.         0.        -0.        ...  0.         0.        -0.6370882]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 5.7347e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9828
[ 0.         0.        -0.        ... -0.         0.        -0.6405147]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6881e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9826
[ 0.          0.         -0.         ... -0.          0.
 -0.64197314]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3105e-06 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9827
[ 0.         0.        -0.        ...  0.        -0.        -0.6447998]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2670e-06 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9829
[ 0.         0.        -0.        ... -0.         0.        -0.6506941]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8014e-04 - accuracy: 0.9999 - val_loss: 0.1231 - val_accuracy: 0.9821
[ 0.          0.         -0.         ...  0.          0.
 -0.65720934]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2471e-04 - accuracy: 0.9999 - val_loss: 0.1281 - val_accuracy: 0.9822
[ 0.          0.         -0.         ... -0.         -0.
 -0.65045637]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5254e-04 - accuracy: 0.9999 - val_loss: 0.1236 - val_accuracy: 0.9825
[ 0.          0.         -0.         ...  0.          0.
 -0.64981794]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1732e-04 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9824
[ 0.          0.         -0.         ... -0.         -0.
 -0.65554583]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0435e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9824
[ 0.          0.         -0.         ... -0.          0.
 -0.64563733]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1209e-05 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9827
[ 0.          0.         -0.         ... -0.         -0.
 -0.64557904]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9674e-05 - accuracy: 1.0000 - val_loss: 0.1244 - val_accuracy: 0.9829
[ 0.         0.        -0.        ... -0.         0.        -0.6469672]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7864e-06 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9830
[ 0.         0.        -0.        ... -0.        -0.        -0.6440701]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2459e-05 - accuracy: 1.0000 - val_loss: 0.1231 - val_accuracy: 0.9829
[ 0.         0.        -0.        ...  0.         0.        -0.6452636]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4388e-06 - accuracy: 1.0000 - val_loss: 0.1224 - val_accuracy: 0.9830
[ 0.         0.        -0.        ... -0.         0.        -0.6460221]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1928e-06 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9831
[ 0.         0.        -0.        ... -0.         0.        -0.6454796]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 4s 15ms/step - loss: 5.3541e-06 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9832
[ 0.          0.         -0.         ... -0.          0.
 -0.64631057]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5180e-06 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9830
[ 0.         0.        -0.        ...  0.         0.        -0.6477382]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8481e-06 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9831
[ 0.          0.         -0.         ...  0.          0.
 -0.64765185]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2249e-06 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9831
[ 0.         0.        -0.        ... -0.         0.        -0.6476807]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0078 - accuracy: 0.9974 - val_loss: 0.1163 - val_accuracy: 0.9799
[ 0.         0.        -0.        ...  0.         0.        -0.5842136]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 8.8807e-04 - accuracy: 0.9998 - val_loss: 0.1092 - val_accuracy: 0.9813
[ 0.         0.        -0.        ...  0.         0.        -0.5974014]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1643e-04 - accuracy: 0.9999 - val_loss: 0.1085 - val_accuracy: 0.9817
[ 0.         0.        -0.        ...  0.         0.        -0.5953442]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5785e-04 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9818
[ 0.         0.        -0.        ... -0.         0.        -0.5923075]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6611e-04 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9815
[ 0.         0.        -0.        ... -0.        -0.        -0.5914813]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3024e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9820
[ 0.          0.         -0.         ... -0.          0.
 -0.59226054]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4367e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9814
[ 0.         0.        -0.        ... -0.         0.        -0.5966814]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1934e-04 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9820
[ 0.         0.        -0.        ... -0.         0.        -0.5975729]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1665e-04 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9817
[ 0.          0.         -0.         ...  0.         -0.
 -0.59761626]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2262e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9819
[ 0.        0.       -0.       ... -0.       -0.       -0.597967]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 6.9770e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9821
[ 0.         0.        -0.        ...  0.         0.        -0.5988403]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 5.9540e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9818
[ 0.          0.         -0.         ...  0.          0.
 -0.59926516]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0765e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9825
[ 0.         0.        -0.        ... -0.         0.        -0.5995413]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2578e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9828
[ 0.          0.         -0.         ...  0.          0.
 -0.60125154]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1549e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9828
[ 0.          0.         -0.         ...  0.         -0.
 -0.60275507]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4555e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9830
[ 0.         0.        -0.        ... -0.         0.        -0.6044884]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2628e-05 - accuracy: 1.0000 - val_loss: 0.1094 - val_accuracy: 0.9825
[ 0.         0.        -0.        ...  0.         0.        -0.6038372]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2986e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9828
[ 0.         0.        -0.        ... -0.        -0.        -0.6053426]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4561e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9826
[ 0.         0.        -0.        ...  0.         0.        -0.6063129]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2560e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9831
[ 0.        0.       -0.       ...  0.       -0.       -0.607389]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0694e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9832
[ 0.        0.       -0.       ... -0.        0.       -0.608513]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9994e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9829
[ 0.          0.         -0.         ... -0.         -0.
 -0.61073303]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8792e-05 - accuracy: 1.0000 - val_loss: 0.1121 - val_accuracy: 0.9828
[ 0.          0.         -0.         ... -0.         -0.
 -0.61220765]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6124e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9829
[ 0.         0.        -0.        ... -0.         0.        -0.6150207]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4744e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9829
[ 0.         0.        -0.        ...  0.        -0.        -0.6076736]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5048e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9829
[ 0.         0.        -0.        ...  0.         0.        -0.6079288]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3930e-05 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9826
[ 0.         0.        -0.        ... -0.        -0.        -0.6082331]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1395e-05 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9827
[ 0.          0.         -0.         ... -0.          0.
 -0.61064637]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 8.6879e-06 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9830
[ 0.          0.         -0.         ... -0.         -0.
 -0.61191964]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7471e-06 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9829
[ 0.         0.        -0.        ... -0.        -0.        -0.6168884]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4289e-06 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9831
[ 0.          0.         -0.         ...  0.         -0.
 -0.61866254]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8104e-06 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9831
[ 0.         0.        -0.        ... -0.         0.        -0.6144433]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 6.8746e-06 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9831
[ 0.         0.        -0.        ... -0.        -0.        -0.6183386]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6879e-06 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9833
[ 0.         0.        -0.        ... -0.        -0.        -0.6206976]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9201e-06 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9830
[ 0.         0.        -0.        ...  0.         0.        -0.6204374]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2600e-06 - accuracy: 1.0000 - val_loss: 0.1198 - val_accuracy: 0.9828
[ 0.         0.        -0.        ...  0.         0.        -0.6214009]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1230e-06 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9828
[ 0.         0.        -0.        ...  0.        -0.        -0.6234882]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3326e-06 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9827
[ 0.          0.         -0.         ... -0.          0.
 -0.62424976]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5562e-06 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9826
[ 0.          0.         -0.         ... -0.          0.
 -0.62826645]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3924e-06 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9826
[ 0.         0.        -0.        ... -0.         0.        -0.6309619]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5809e-06 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9828
[ 0.          0.         -0.         ... -0.          0.
 -0.63668805]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2865e-06 - accuracy: 1.0000 - val_loss: 0.1244 - val_accuracy: 0.9831
[ 0.          0.         -0.         ...  0.          0.
 -0.64158785]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0963e-06 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9831
[ 0.         0.        -0.        ...  0.        -0.        -0.6464676]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3335e-04 - accuracy: 0.9999 - val_loss: 0.1356 - val_accuracy: 0.9817
[ 0.         0.        -0.        ... -0.         0.        -0.6423403]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2827e-04 - accuracy: 0.9999 - val_loss: 0.1322 - val_accuracy: 0.9823
[ 0.         0.        -0.        ... -0.         0.        -0.6174077]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 3s 15ms/step - loss: 3.1257e-05 - accuracy: 1.0000 - val_loss: 0.1320 - val_accuracy: 0.9825
[ 0.          0.         -0.         ... -0.          0.
 -0.62500393]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0572e-05 - accuracy: 1.0000 - val_loss: 0.1319 - val_accuracy: 0.9824
[ 0.          0.         -0.         ... -0.          0.
 -0.62708527]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5441e-05 - accuracy: 1.0000 - val_loss: 0.1308 - val_accuracy: 0.9828
[ 0.          0.         -0.         ... -0.         -0.
 -0.61869895]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0918e-05 - accuracy: 1.0000 - val_loss: 0.1296 - val_accuracy: 0.9824
[ 0.          0.         -0.         ... -0.          0.
 -0.62153697]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6082e-05 - accuracy: 1.0000 - val_loss: 0.1328 - val_accuracy: 0.9825
[ 0.         0.        -0.        ... -0.         0.        -0.6228914]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0301 - accuracy: 0.9914 - val_loss: 0.1117 - val_accuracy: 0.9793
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.1071 - val_accuracy: 0.9801
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.1065 - val_accuracy: 0.9809
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1063 - val_accuracy: 0.9806
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1053 - val_accuracy: 0.9804
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0075e-04 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9807
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7817e-04 - accuracy: 0.9999 - val_loss: 0.1051 - val_accuracy: 0.9811
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8842e-04 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9811
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1369e-04 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9812
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7678e-04 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9814
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1642e-04 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9815
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5146e-04 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9812
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9013e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9816
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5453e-04 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9813
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3158e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9813
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1217e-04 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9811
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8526e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9815
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8377e-04 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9820
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2598e-04 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9814
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2123e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9813
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0645e-04 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9818
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5415e-05 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9818
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 8.8355e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9819
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 8.7932e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9820
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 8.0768e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9815
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3563e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9815
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1337e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9812
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 7.9862e-05 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9819
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3391e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9823
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1900e-05 - accuracy: 1.0000 - val_loss: 0.1155 - val_accuracy: 0.9823
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7239e-05 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9825
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2089e-04 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9819
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3092e-05 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9820
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9689e-05 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9822
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7875e-05 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9819
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0202e-04 - accuracy: 1.0000 - val_loss: 0.1235 - val_accuracy: 0.9817
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7977e-05 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9824
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 4s 16ms/step - loss: 2.2410e-05 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9823
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 4s 16ms/step - loss: 2.6043e-05 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9825
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 4s 16ms/step - loss: 3.7757e-05 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9823
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9601e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9819
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 15ms/step - loss: 1.9345e-05 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9820
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5515e-05 - accuracy: 1.0000 - val_loss: 0.1252 - val_accuracy: 0.9823
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7347e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9821
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1876e-05 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9818
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8458e-05 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9816
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9270e-05 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9823
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4144e-04 - accuracy: 1.0000 - val_loss: 0.1278 - val_accuracy: 0.9822
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5209e-05 - accuracy: 1.0000 - val_loss: 0.1295 - val_accuracy: 0.9824
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0913e-05 - accuracy: 1.0000 - val_loss: 0.1291 - val_accuracy: 0.9823
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0641 - accuracy: 0.9828 - val_loss: 0.1471 - val_accuracy: 0.9754
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0217 - accuracy: 0.9933 - val_loss: 0.1356 - val_accuracy: 0.9764
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0130 - accuracy: 0.9959 - val_loss: 0.1303 - val_accuracy: 0.9772
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0093 - accuracy: 0.9973 - val_loss: 0.1287 - val_accuracy: 0.9773
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0066 - accuracy: 0.9982 - val_loss: 0.1278 - val_accuracy: 0.9780
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0053 - accuracy: 0.9989 - val_loss: 0.1269 - val_accuracy: 0.9785
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 0.1271 - val_accuracy: 0.9790
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.1264 - val_accuracy: 0.9789
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.1264 - val_accuracy: 0.9789
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0025 - accuracy: 0.9997 - val_loss: 0.1272 - val_accuracy: 0.9792
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9997 - val_loss: 0.1268 - val_accuracy: 0.9787
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 0.1291 - val_accuracy: 0.9792
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0020 - accuracy: 0.9998 - val_loss: 0.1289 - val_accuracy: 0.9791
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1290 - val_accuracy: 0.9793
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9998 - val_loss: 0.1294 - val_accuracy: 0.9797
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9799
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1319 - val_accuracy: 0.9797
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 9.6419e-04 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9791
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.1347 - val_accuracy: 0.9792
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6382e-04 - accuracy: 0.9999 - val_loss: 0.1347 - val_accuracy: 0.9791
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1523e-04 - accuracy: 1.0000 - val_loss: 0.1363 - val_accuracy: 0.9795
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7127e-04 - accuracy: 0.9999 - val_loss: 0.1382 - val_accuracy: 0.9792
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2885e-04 - accuracy: 0.9999 - val_loss: 0.1375 - val_accuracy: 0.9797
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5147e-04 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9793
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0875e-04 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9791
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3029e-04 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9790
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0350e-04 - accuracy: 1.0000 - val_loss: 0.1404 - val_accuracy: 0.9797
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2924e-04 - accuracy: 1.0000 - val_loss: 0.1403 - val_accuracy: 0.9795
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6186e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9797
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7471e-04 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9803
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9325e-04 - accuracy: 1.0000 - val_loss: 0.1455 - val_accuracy: 0.9802
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4099e-04 - accuracy: 1.0000 - val_loss: 0.1471 - val_accuracy: 0.9798
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6475e-04 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9800
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5566e-04 - accuracy: 1.0000 - val_loss: 0.1498 - val_accuracy: 0.9800
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5523e-04 - accuracy: 0.9999 - val_loss: 0.1505 - val_accuracy: 0.9800
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0042e-04 - accuracy: 1.0000 - val_loss: 0.1502 - val_accuracy: 0.9799
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7887e-04 - accuracy: 1.0000 - val_loss: 0.1511 - val_accuracy: 0.9804
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0170e-04 - accuracy: 1.0000 - val_loss: 0.1519 - val_accuracy: 0.9806
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0058e-04 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9799
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0833e-04 - accuracy: 0.9999 - val_loss: 0.1566 - val_accuracy: 0.9795
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2957e-04 - accuracy: 0.9999 - val_loss: 0.1562 - val_accuracy: 0.9795
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4819e-04 - accuracy: 1.0000 - val_loss: 0.1559 - val_accuracy: 0.9791
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5834e-04 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9792
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2612e-04 - accuracy: 1.0000 - val_loss: 0.1603 - val_accuracy: 0.9799
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7013e-04 - accuracy: 1.0000 - val_loss: 0.1618 - val_accuracy: 0.9797
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5782e-05 - accuracy: 1.0000 - val_loss: 0.1619 - val_accuracy: 0.9798
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1604e-04 - accuracy: 1.0000 - val_loss: 0.1624 - val_accuracy: 0.9800
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2603e-04 - accuracy: 1.0000 - val_loss: 0.1658 - val_accuracy: 0.9801
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0294e-04 - accuracy: 1.0000 - val_loss: 0.1671 - val_accuracy: 0.9801
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5512e-04 - accuracy: 1.0000 - val_loss: 0.1693 - val_accuracy: 0.9800
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2561 - accuracy: 0.9444 - val_loss: 0.2392 - val_accuracy: 0.9494
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1075 - accuracy: 0.9700 - val_loss: 0.2073 - val_accuracy: 0.9547
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0852 - accuracy: 0.9750 - val_loss: 0.1916 - val_accuracy: 0.9575
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0729 - accuracy: 0.9778 - val_loss: 0.1809 - val_accuracy: 0.9585
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0646 - accuracy: 0.9798 - val_loss: 0.1726 - val_accuracy: 0.9597
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0585 - accuracy: 0.9812 - val_loss: 0.1672 - val_accuracy: 0.9605
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0542 - accuracy: 0.9829 - val_loss: 0.1638 - val_accuracy: 0.9613
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0497 - accuracy: 0.9840 - val_loss: 0.1601 - val_accuracy: 0.9621
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0474 - accuracy: 0.9846 - val_loss: 0.1584 - val_accuracy: 0.9624
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0437 - accuracy: 0.9862 - val_loss: 0.1574 - val_accuracy: 0.9632
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0411 - accuracy: 0.9873 - val_loss: 0.1570 - val_accuracy: 0.9629
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0388 - accuracy: 0.9876 - val_loss: 0.1551 - val_accuracy: 0.9634
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0368 - accuracy: 0.9883 - val_loss: 0.1540 - val_accuracy: 0.9639
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0346 - accuracy: 0.9894 - val_loss: 0.1543 - val_accuracy: 0.9638
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0336 - accuracy: 0.9895 - val_loss: 0.1542 - val_accuracy: 0.9639
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0317 - accuracy: 0.9900 - val_loss: 0.1540 - val_accuracy: 0.9638
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0300 - accuracy: 0.9907 - val_loss: 0.1546 - val_accuracy: 0.9640
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0285 - accuracy: 0.9912 - val_loss: 0.1547 - val_accuracy: 0.9644
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0269 - accuracy: 0.9918 - val_loss: 0.1549 - val_accuracy: 0.9645
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0259 - accuracy: 0.9920 - val_loss: 0.1562 - val_accuracy: 0.9648
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0249 - accuracy: 0.9924 - val_loss: 0.1555 - val_accuracy: 0.9646
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0234 - accuracy: 0.9927 - val_loss: 0.1580 - val_accuracy: 0.9649
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0227 - accuracy: 0.9932 - val_loss: 0.1579 - val_accuracy: 0.9654
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 0.1588 - val_accuracy: 0.9648
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0211 - accuracy: 0.9939 - val_loss: 0.1602 - val_accuracy: 0.9649
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0200 - accuracy: 0.9940 - val_loss: 0.1609 - val_accuracy: 0.9655
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0191 - accuracy: 0.9946 - val_loss: 0.1612 - val_accuracy: 0.9660
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0175 - accuracy: 0.9952 - val_loss: 0.1623 - val_accuracy: 0.9657
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0175 - accuracy: 0.9951 - val_loss: 0.1641 - val_accuracy: 0.9660
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0170 - accuracy: 0.9950 - val_loss: 0.1644 - val_accuracy: 0.9670
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0156 - accuracy: 0.9960 - val_loss: 0.1654 - val_accuracy: 0.9670
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0149 - accuracy: 0.9961 - val_loss: 0.1670 - val_accuracy: 0.9666
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0142 - accuracy: 0.9966 - val_loss: 0.1693 - val_accuracy: 0.9669
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0136 - accuracy: 0.9965 - val_loss: 0.1714 - val_accuracy: 0.9668
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0134 - accuracy: 0.9966 - val_loss: 0.1723 - val_accuracy: 0.9666
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0129 - accuracy: 0.9966 - val_loss: 0.1727 - val_accuracy: 0.9669
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0120 - accuracy: 0.9969 - val_loss: 0.1761 - val_accuracy: 0.9672
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0114 - accuracy: 0.9975 - val_loss: 0.1764 - val_accuracy: 0.9664
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0113 - accuracy: 0.9973 - val_loss: 0.1792 - val_accuracy: 0.9668
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0104 - accuracy: 0.9973 - val_loss: 0.1812 - val_accuracy: 0.9670
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0105 - accuracy: 0.9976 - val_loss: 0.1810 - val_accuracy: 0.9673
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0099 - accuracy: 0.9977 - val_loss: 0.1832 - val_accuracy: 0.9675
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0093 - accuracy: 0.9980 - val_loss: 0.1871 - val_accuracy: 0.9673
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0092 - accuracy: 0.9980 - val_loss: 0.1871 - val_accuracy: 0.9676
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9983 - val_loss: 0.1895 - val_accuracy: 0.9674
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 0.1921 - val_accuracy: 0.9671
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9981 - val_loss: 0.1931 - val_accuracy: 0.9678
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0079 - accuracy: 0.9984 - val_loss: 0.1946 - val_accuracy: 0.9676
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0074 - accuracy: 0.9984 - val_loss: 0.1972 - val_accuracy: 0.9677
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0066 - accuracy: 0.9987 - val_loss: 0.2003 - val_accuracy: 0.9680
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4957 - accuracy: 0.8734 - val_loss: 0.3896 - val_accuracy: 0.9014
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2971 - accuracy: 0.9146 - val_loss: 0.3224 - val_accuracy: 0.9191
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2581 - accuracy: 0.9239 - val_loss: 0.2935 - val_accuracy: 0.9228
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2367 - accuracy: 0.9287 - val_loss: 0.2767 - val_accuracy: 0.9262
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2218 - accuracy: 0.9333 - val_loss: 0.2641 - val_accuracy: 0.9289
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2121 - accuracy: 0.9357 - val_loss: 0.2542 - val_accuracy: 0.9302
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2026 - accuracy: 0.9383 - val_loss: 0.2465 - val_accuracy: 0.9324
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1971 - accuracy: 0.9403 - val_loss: 0.2405 - val_accuracy: 0.9329
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1915 - accuracy: 0.9414 - val_loss: 0.2358 - val_accuracy: 0.9348
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1871 - accuracy: 0.9426 - val_loss: 0.2312 - val_accuracy: 0.9363
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1836 - accuracy: 0.9442 - val_loss: 0.2276 - val_accuracy: 0.9373
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1793 - accuracy: 0.9450 - val_loss: 0.2235 - val_accuracy: 0.9378
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1770 - accuracy: 0.9452 - val_loss: 0.2208 - val_accuracy: 0.9382
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1735 - accuracy: 0.9470 - val_loss: 0.2178 - val_accuracy: 0.9381
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1706 - accuracy: 0.9479 - val_loss: 0.2152 - val_accuracy: 0.9381
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1671 - accuracy: 0.9488 - val_loss: 0.2126 - val_accuracy: 0.9384
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1656 - accuracy: 0.9488 - val_loss: 0.2107 - val_accuracy: 0.9397
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1629 - accuracy: 0.9495 - val_loss: 0.2080 - val_accuracy: 0.9396
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1606 - accuracy: 0.9508 - val_loss: 0.2065 - val_accuracy: 0.9405
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1582 - accuracy: 0.9516 - val_loss: 0.2048 - val_accuracy: 0.9413
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1562 - accuracy: 0.9516 - val_loss: 0.2035 - val_accuracy: 0.9417
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1549 - accuracy: 0.9520 - val_loss: 0.2020 - val_accuracy: 0.9419
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1524 - accuracy: 0.9526 - val_loss: 0.2007 - val_accuracy: 0.9418
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1510 - accuracy: 0.9524 - val_loss: 0.1995 - val_accuracy: 0.9427
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1487 - accuracy: 0.9538 - val_loss: 0.1985 - val_accuracy: 0.9431
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1476 - accuracy: 0.9542 - val_loss: 0.1976 - val_accuracy: 0.9430
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1458 - accuracy: 0.9537 - val_loss: 0.1962 - val_accuracy: 0.9440
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9550 - val_loss: 0.1950 - val_accuracy: 0.9447
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9555 - val_loss: 0.1945 - val_accuracy: 0.9453
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1418 - accuracy: 0.9561 - val_loss: 0.1940 - val_accuracy: 0.9455
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9563 - val_loss: 0.1933 - val_accuracy: 0.9453
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9565 - val_loss: 0.1930 - val_accuracy: 0.9456
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9572 - val_loss: 0.1925 - val_accuracy: 0.9456
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9574 - val_loss: 0.1923 - val_accuracy: 0.9459
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9578 - val_loss: 0.1919 - val_accuracy: 0.9465
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9578 - val_loss: 0.1918 - val_accuracy: 0.9466
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9582 - val_loss: 0.1914 - val_accuracy: 0.9472
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9583 - val_loss: 0.1912 - val_accuracy: 0.9473
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9590 - val_loss: 0.1908 - val_accuracy: 0.9475
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9591 - val_loss: 0.1904 - val_accuracy: 0.9472
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9596 - val_loss: 0.1901 - val_accuracy: 0.9474
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9592 - val_loss: 0.1894 - val_accuracy: 0.9478
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9596 - val_loss: 0.1893 - val_accuracy: 0.9474
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9599 - val_loss: 0.1889 - val_accuracy: 0.9481
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9605 - val_loss: 0.1889 - val_accuracy: 0.9477
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9599 - val_loss: 0.1890 - val_accuracy: 0.9474
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9606 - val_loss: 0.1887 - val_accuracy: 0.9477
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9611 - val_loss: 0.1884 - val_accuracy: 0.9474
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9604 - val_loss: 0.1883 - val_accuracy: 0.9476
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9613 - val_loss: 0.1883 - val_accuracy: 0.9482
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 3s 14ms/step - loss: 0.6897 - accuracy: 0.7849 - val_loss: 0.6106 - val_accuracy: 0.8092
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5769 - accuracy: 0.8160 - val_loss: 0.5671 - val_accuracy: 0.8214
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5502 - accuracy: 0.8233 - val_loss: 0.5467 - val_accuracy: 0.8275
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5368 - accuracy: 0.8265 - val_loss: 0.5344 - val_accuracy: 0.8313
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5269 - accuracy: 0.8295 - val_loss: 0.5243 - val_accuracy: 0.8364
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5187 - accuracy: 0.8318 - val_loss: 0.5165 - val_accuracy: 0.8371
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5129 - accuracy: 0.8329 - val_loss: 0.5104 - val_accuracy: 0.8386
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5071 - accuracy: 0.8341 - val_loss: 0.5053 - val_accuracy: 0.8415
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5018 - accuracy: 0.8361 - val_loss: 0.5004 - val_accuracy: 0.8428
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4975 - accuracy: 0.8370 - val_loss: 0.4958 - val_accuracy: 0.8438
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4928 - accuracy: 0.8391 - val_loss: 0.4915 - val_accuracy: 0.8460
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4887 - accuracy: 0.8397 - val_loss: 0.4859 - val_accuracy: 0.8475
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4838 - accuracy: 0.8425 - val_loss: 0.4825 - val_accuracy: 0.8481
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4811 - accuracy: 0.8425 - val_loss: 0.4794 - val_accuracy: 0.8483
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4783 - accuracy: 0.8442 - val_loss: 0.4772 - val_accuracy: 0.8495
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4766 - accuracy: 0.8440 - val_loss: 0.4751 - val_accuracy: 0.8501
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4744 - accuracy: 0.8452 - val_loss: 0.4732 - val_accuracy: 0.8504
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4729 - accuracy: 0.8457 - val_loss: 0.4712 - val_accuracy: 0.8520
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4700 - accuracy: 0.8468 - val_loss: 0.4694 - val_accuracy: 0.8523
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4690 - accuracy: 0.8466 - val_loss: 0.4678 - val_accuracy: 0.8531
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4663 - accuracy: 0.8475 - val_loss: 0.4661 - val_accuracy: 0.8549
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4649 - accuracy: 0.8482 - val_loss: 0.4647 - val_accuracy: 0.8551
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4637 - accuracy: 0.8486 - val_loss: 0.4637 - val_accuracy: 0.8554
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4625 - accuracy: 0.8492 - val_loss: 0.4627 - val_accuracy: 0.8566
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4624 - accuracy: 0.8494 - val_loss: 0.4619 - val_accuracy: 0.8567
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4604 - accuracy: 0.8495 - val_loss: 0.4613 - val_accuracy: 0.8569
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4604 - accuracy: 0.8503 - val_loss: 0.4606 - val_accuracy: 0.8572
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4594 - accuracy: 0.8513 - val_loss: 0.4598 - val_accuracy: 0.8573
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4584 - accuracy: 0.8507 - val_loss: 0.4600 - val_accuracy: 0.8575
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4580 - accuracy: 0.8505 - val_loss: 0.4596 - val_accuracy: 0.8571
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4580 - accuracy: 0.8507 - val_loss: 0.4595 - val_accuracy: 0.8563
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4574 - accuracy: 0.8504 - val_loss: 0.4589 - val_accuracy: 0.8568
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4566 - accuracy: 0.8508 - val_loss: 0.4581 - val_accuracy: 0.8568
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4554 - accuracy: 0.8523 - val_loss: 0.4575 - val_accuracy: 0.8574
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4544 - accuracy: 0.8520 - val_loss: 0.4571 - val_accuracy: 0.8576
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4547 - accuracy: 0.8513 - val_loss: 0.4569 - val_accuracy: 0.8570
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4537 - accuracy: 0.8522 - val_loss: 0.4559 - val_accuracy: 0.8567
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4537 - accuracy: 0.8518 - val_loss: 0.4560 - val_accuracy: 0.8568
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4525 - accuracy: 0.8520 - val_loss: 0.4556 - val_accuracy: 0.8566
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4510 - accuracy: 0.8528 - val_loss: 0.4550 - val_accuracy: 0.8578
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4514 - accuracy: 0.8522 - val_loss: 0.4542 - val_accuracy: 0.8570
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4511 - accuracy: 0.8525 - val_loss: 0.4538 - val_accuracy: 0.8566
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4499 - accuracy: 0.8531 - val_loss: 0.4534 - val_accuracy: 0.8577
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4499 - accuracy: 0.8533 - val_loss: 0.4532 - val_accuracy: 0.8573
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4497 - accuracy: 0.8531 - val_loss: 0.4528 - val_accuracy: 0.8568
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4479 - accuracy: 0.8538 - val_loss: 0.4518 - val_accuracy: 0.8577
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4473 - accuracy: 0.8550 - val_loss: 0.4508 - val_accuracy: 0.8577
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4471 - accuracy: 0.8541 - val_loss: 0.4498 - val_accuracy: 0.8572
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4463 - accuracy: 0.8557 - val_loss: 0.4489 - val_accuracy: 0.8582
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 4s 16ms/step - loss: 0.4460 - accuracy: 0.8546 - val_loss: 0.4485 - val_accuracy: 0.8581
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 4s 16ms/step - loss: 1.0185 - accuracy: 0.6533 - val_loss: 0.9604 - val_accuracy: 0.6694
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9434 - accuracy: 0.6708 - val_loss: 0.9275 - val_accuracy: 0.6843
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 3s 15ms/step - loss: 0.9315 - accuracy: 0.6748 - val_loss: 0.9188 - val_accuracy: 0.6857
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9238 - accuracy: 0.6759 - val_loss: 0.9133 - val_accuracy: 0.6854
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9189 - accuracy: 0.6783 - val_loss: 0.9097 - val_accuracy: 0.6852
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9160 - accuracy: 0.6791 - val_loss: 0.9067 - val_accuracy: 0.6873
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9128 - accuracy: 0.6795 - val_loss: 0.9044 - val_accuracy: 0.6852
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9100 - accuracy: 0.6819 - val_loss: 0.9023 - val_accuracy: 0.6884
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9074 - accuracy: 0.6818 - val_loss: 0.8995 - val_accuracy: 0.6872
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9043 - accuracy: 0.6830 - val_loss: 0.8969 - val_accuracy: 0.6874
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9029 - accuracy: 0.6841 - val_loss: 0.8955 - val_accuracy: 0.6893
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9015 - accuracy: 0.6837 - val_loss: 0.8938 - val_accuracy: 0.6904
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8996 - accuracy: 0.6845 - val_loss: 0.8934 - val_accuracy: 0.6908
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8989 - accuracy: 0.6849 - val_loss: 0.8921 - val_accuracy: 0.6907
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8974 - accuracy: 0.6858 - val_loss: 0.8910 - val_accuracy: 0.6942
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8951 - accuracy: 0.6868 - val_loss: 0.8871 - val_accuracy: 0.6929
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8912 - accuracy: 0.6876 - val_loss: 0.8847 - val_accuracy: 0.6944
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8903 - accuracy: 0.6876 - val_loss: 0.8830 - val_accuracy: 0.6954
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8901 - accuracy: 0.6888 - val_loss: 0.8824 - val_accuracy: 0.6966
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8872 - accuracy: 0.6885 - val_loss: 0.8791 - val_accuracy: 0.6979
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8852 - accuracy: 0.6885 - val_loss: 0.8777 - val_accuracy: 0.6977
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8840 - accuracy: 0.6885 - val_loss: 0.8762 - val_accuracy: 0.6978
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8836 - accuracy: 0.6894 - val_loss: 0.8753 - val_accuracy: 0.6977
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8830 - accuracy: 0.6893 - val_loss: 0.8753 - val_accuracy: 0.6974
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8830 - accuracy: 0.6900 - val_loss: 0.8744 - val_accuracy: 0.6969
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8819 - accuracy: 0.6892 - val_loss: 0.8739 - val_accuracy: 0.6988
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8816 - accuracy: 0.6899 - val_loss: 0.8735 - val_accuracy: 0.6983
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8803 - accuracy: 0.6908 - val_loss: 0.8735 - val_accuracy: 0.6995
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 4s 15ms/step - loss: 0.8806 - accuracy: 0.6901 - val_loss: 0.8731 - val_accuracy: 0.7000
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8801 - accuracy: 0.6906 - val_loss: 0.8731 - val_accuracy: 0.6989
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8798 - accuracy: 0.6909 - val_loss: 0.8725 - val_accuracy: 0.7004
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8784 - accuracy: 0.6919 - val_loss: 0.8719 - val_accuracy: 0.6988
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8790 - accuracy: 0.6910 - val_loss: 0.8721 - val_accuracy: 0.6996
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8792 - accuracy: 0.6911 - val_loss: 0.8720 - val_accuracy: 0.6996
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8774 - accuracy: 0.6930 - val_loss: 0.8711 - val_accuracy: 0.6994
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8775 - accuracy: 0.6922 - val_loss: 0.8711 - val_accuracy: 0.7001
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8770 - accuracy: 0.6921 - val_loss: 0.8714 - val_accuracy: 0.7009
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8766 - accuracy: 0.6928 - val_loss: 0.8705 - val_accuracy: 0.7000
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8763 - accuracy: 0.6922 - val_loss: 0.8704 - val_accuracy: 0.7013
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8765 - accuracy: 0.6928 - val_loss: 0.8701 - val_accuracy: 0.7011
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8755 - accuracy: 0.6925 - val_loss: 0.8693 - val_accuracy: 0.7003
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8759 - accuracy: 0.6930 - val_loss: 0.8685 - val_accuracy: 0.7004
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8749 - accuracy: 0.6934 - val_loss: 0.8681 - val_accuracy: 0.7010
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8741 - accuracy: 0.6931 - val_loss: 0.8681 - val_accuracy: 0.7016
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8738 - accuracy: 0.6935 - val_loss: 0.8678 - val_accuracy: 0.7012
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8736 - accuracy: 0.6937 - val_loss: 0.8684 - val_accuracy: 0.7020
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8730 - accuracy: 0.6939 - val_loss: 0.8675 - val_accuracy: 0.7024
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8731 - accuracy: 0.6939 - val_loss: 0.8689 - val_accuracy: 0.7022
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8726 - accuracy: 0.6941 - val_loss: 0.8686 - val_accuracy: 0.7004
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8722 - accuracy: 0.6940 - val_loss: 0.8686 - val_accuracy: 0.7027
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8726 - accuracy: 0.6940 - val_loss: 0.8673 - val_accuracy: 0.7009
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8718 - accuracy: 0.6941 - val_loss: 0.8676 - val_accuracy: 0.7017
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8735 - accuracy: 0.6940 - val_loss: 0.8676 - val_accuracy: 0.7021
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8720 - accuracy: 0.6952 - val_loss: 0.8669 - val_accuracy: 0.7014
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8711 - accuracy: 0.6951 - val_loss: 0.8674 - val_accuracy: 0.7015
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8700 - accuracy: 0.6953 - val_loss: 0.8673 - val_accuracy: 0.7028
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8708 - accuracy: 0.6946 - val_loss: 0.8664 - val_accuracy: 0.7030
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 12ms/step - loss: 0.8707 - accuracy: 0.6949 - val_loss: 0.8667 - val_accuracy: 0.7034
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8706 - accuracy: 0.6950 - val_loss: 0.8660 - val_accuracy: 0.7023
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8708 - accuracy: 0.6949 - val_loss: 0.8660 - val_accuracy: 0.7019
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8716 - accuracy: 0.6959 - val_loss: 0.8656 - val_accuracy: 0.7035
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8707 - accuracy: 0.6949 - val_loss: 0.8657 - val_accuracy: 0.7020
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8704 - accuracy: 0.6955 - val_loss: 0.8655 - val_accuracy: 0.7024
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8704 - accuracy: 0.6952 - val_loss: 0.8648 - val_accuracy: 0.7018
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8701 - accuracy: 0.6947 - val_loss: 0.8653 - val_accuracy: 0.7040
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8697 - accuracy: 0.6959 - val_loss: 0.8646 - val_accuracy: 0.7024
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8697 - accuracy: 0.6949 - val_loss: 0.8641 - val_accuracy: 0.7031
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8696 - accuracy: 0.6951 - val_loss: 0.8645 - val_accuracy: 0.7036
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8696 - accuracy: 0.6956 - val_loss: 0.8645 - val_accuracy: 0.7046
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8695 - accuracy: 0.6968 - val_loss: 0.8636 - val_accuracy: 0.7028
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8692 - accuracy: 0.6946 - val_loss: 0.8642 - val_accuracy: 0.7029
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8693 - accuracy: 0.6961 - val_loss: 0.8636 - val_accuracy: 0.7024
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8687 - accuracy: 0.6962 - val_loss: 0.8634 - val_accuracy: 0.7024
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8695 - accuracy: 0.6959 - val_loss: 0.8630 - val_accuracy: 0.7035
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8688 - accuracy: 0.6957 - val_loss: 0.8623 - val_accuracy: 0.7041
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8686 - accuracy: 0.6959 - val_loss: 0.8619 - val_accuracy: 0.7027
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8674 - accuracy: 0.6963 - val_loss: 0.8621 - val_accuracy: 0.7036
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8681 - accuracy: 0.6960 - val_loss: 0.8626 - val_accuracy: 0.7027
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8681 - accuracy: 0.6966 - val_loss: 0.8619 - val_accuracy: 0.7039
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8686 - accuracy: 0.6963 - val_loss: 0.8611 - val_accuracy: 0.7028
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8682 - accuracy: 0.6960 - val_loss: 0.8617 - val_accuracy: 0.7038
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8686 - accuracy: 0.6953 - val_loss: 0.8611 - val_accuracy: 0.7036
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8678 - accuracy: 0.6958 - val_loss: 0.8613 - val_accuracy: 0.7039
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8674 - accuracy: 0.6970 - val_loss: 0.8615 - val_accuracy: 0.7045
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8686 - accuracy: 0.6962 - val_loss: 0.8616 - val_accuracy: 0.7042
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8678 - accuracy: 0.6970 - val_loss: 0.8616 - val_accuracy: 0.7034
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8680 - accuracy: 0.6964 - val_loss: 0.8613 - val_accuracy: 0.7040
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8676 - accuracy: 0.6968 - val_loss: 0.8615 - val_accuracy: 0.7048
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8688 - accuracy: 0.6967 - val_loss: 0.8611 - val_accuracy: 0.7039
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8678 - accuracy: 0.6961 - val_loss: 0.8614 - val_accuracy: 0.7032
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8676 - accuracy: 0.6973 - val_loss: 0.8614 - val_accuracy: 0.7044
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8672 - accuracy: 0.6971 - val_loss: 0.8606 - val_accuracy: 0.7044
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8673 - accuracy: 0.6966 - val_loss: 0.8611 - val_accuracy: 0.7040
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8660 - accuracy: 0.6969 - val_loss: 0.8603 - val_accuracy: 0.7040
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8665 - accuracy: 0.6965 - val_loss: 0.8605 - val_accuracy: 0.7035
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8674 - accuracy: 0.6971 - val_loss: 0.8604 - val_accuracy: 0.7032
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8668 - accuracy: 0.6978 - val_loss: 0.8605 - val_accuracy: 0.7039
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8667 - accuracy: 0.6968 - val_loss: 0.8597 - val_accuracy: 0.7038
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 15ms/step - loss: 0.8674 - accuracy: 0.6956 - val_loss: 0.8599 - val_accuracy: 0.7033
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.8673 - accuracy: 0.6964 - val_loss: 0.8601 - val_accuracy: 0.7048
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 3s 9ms/step - loss: 0.8526 - accuracy: 0.8982 - val_loss: 0.8265 - val_accuracy: 0.9035
[ 0.          0.          0.         ... -0.          0.13614766
  0.        ]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8437 - accuracy: 0.9003 - val_loss: 0.8249 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.          0.14399227
  0.        ]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8427 - accuracy: 0.9002 - val_loss: 0.8243 - val_accuracy: 0.9034
[ 0.          0.          0.         ... -0.          0.14975326
  0.        ]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8422 - accuracy: 0.8999 - val_loss: 0.8238 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.          0.15280853
  0.        ]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8418 - accuracy: 0.8999 - val_loss: 0.8236 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.          0.15454446
  0.        ]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8419 - accuracy: 0.8999 - val_loss: 0.8236 - val_accuracy: 0.9036
[ 0.         0.         0.        ... -0.         0.1554479  0.       ]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8417 - accuracy: 0.9000 - val_loss: 0.8235 - val_accuracy: 0.9038
[ 0.          0.          0.         ... -0.          0.15593015
  0.        ]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8416 - accuracy: 0.9000 - val_loss: 0.8234 - val_accuracy: 0.9034
[ 0.          0.          0.         ... -0.          0.15611143
  0.        ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8414 - accuracy: 0.9000 - val_loss: 0.8232 - val_accuracy: 0.9038
[ 0.         0.         0.        ... -0.         0.1561268  0.       ]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8413 - accuracy: 0.8999 - val_loss: 0.8231 - val_accuracy: 0.9034
[ 0.          0.          0.         ... -0.          0.15617429
  0.        ]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8413 - accuracy: 0.8997 - val_loss: 0.8229 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.          0.15605137
  0.        ]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8411 - accuracy: 0.9004 - val_loss: 0.8229 - val_accuracy: 0.9032
[ 0.          0.          0.         ... -0.          0.15591909
  0.        ]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8411 - accuracy: 0.9001 - val_loss: 0.8228 - val_accuracy: 0.9033
[ 0.          0.          0.         ... -0.          0.15589362
  0.        ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8410 - accuracy: 0.9002 - val_loss: 0.8231 - val_accuracy: 0.9032
[ 0.          0.          0.         ... -0.          0.15556595
  0.        ]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8409 - accuracy: 0.9003 - val_loss: 0.8226 - val_accuracy: 0.9034
[ 0.          0.          0.         ... -0.          0.15540807
  0.        ]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8410 - accuracy: 0.9000 - val_loss: 0.8230 - val_accuracy: 0.9037
[ 0.          0.          0.         ... -0.          0.15545778
  0.        ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8408 - accuracy: 0.9000 - val_loss: 0.8228 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.          0.15539718
  0.        ]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8410 - accuracy: 0.9003 - val_loss: 0.8227 - val_accuracy: 0.9036
[ 0.          0.          0.         ... -0.          0.15533721
  0.        ]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8410 - accuracy: 0.8999 - val_loss: 0.8226 - val_accuracy: 0.9032
[ 0.          0.          0.         ... -0.          0.15529539
  0.        ]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8407 - accuracy: 0.9002 - val_loss: 0.8227 - val_accuracy: 0.9033
[ 0.         0.         0.        ... -0.         0.1553931  0.       ]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9002 - val_loss: 0.8229 - val_accuracy: 0.9030
[ 0.          0.          0.         ... -0.          0.15537092
  0.        ]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8407 - accuracy: 0.9002 - val_loss: 0.8224 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.          0.15536803
  0.        ]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8407 - accuracy: 0.8999 - val_loss: 0.8226 - val_accuracy: 0.9035
[ 0.          0.          0.         ... -0.          0.15553221
  0.        ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8407 - accuracy: 0.9000 - val_loss: 0.8225 - val_accuracy: 0.9038
[ 0.        0.        0.       ... -0.        0.155561  0.      ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8408 - accuracy: 0.9001 - val_loss: 0.8229 - val_accuracy: 0.9029
[ 0.          0.          0.         ... -0.          0.15556252
  0.        ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8407 - accuracy: 0.9002 - val_loss: 0.8226 - val_accuracy: 0.9038
[ 0.          0.          0.         ... -0.          0.15558323
  0.        ]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.9002 - val_loss: 0.8222 - val_accuracy: 0.9035
[ 0.          0.          0.         ... -0.          0.15563019
  0.        ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9000 - val_loss: 0.8225 - val_accuracy: 0.9033
[ 0.          0.          0.         ... -0.          0.15549228
  0.        ]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9001 - val_loss: 0.8219 - val_accuracy: 0.9040
[ 0.          0.          0.         ... -0.          0.15556586
  0.        ]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9002 - val_loss: 0.8222 - val_accuracy: 0.9036
[ 0.          0.          0.         ... -0.          0.15543585
  0.        ]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9003 - val_loss: 0.8224 - val_accuracy: 0.9037
[ 0.          0.          0.         ... -0.          0.15558897
  0.        ]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9001 - val_loss: 0.8226 - val_accuracy: 0.9034
[ 0.          0.          0.         ... -0.          0.15551874
  0.        ]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9004 - val_loss: 0.8222 - val_accuracy: 0.9033
[ 0.        0.        0.       ... -0.        0.155691  0.      ]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8999 - val_loss: 0.8223 - val_accuracy: 0.9030
[ 0.          0.          0.         ... -0.          0.15572841
  0.        ]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9000 - val_loss: 0.8221 - val_accuracy: 0.9037
[ 0.          0.          0.         ... -0.          0.15581815
  0.        ]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9003 - val_loss: 0.8221 - val_accuracy: 0.9043
[ 0.         0.         0.        ... -0.         0.1560769  0.       ]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.8998 - val_loss: 0.8223 - val_accuracy: 0.9033
[ 0.         0.         0.        ... -0.         0.1557283  0.       ]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9000 - val_loss: 0.8225 - val_accuracy: 0.9033
[ 0.          0.          0.         ... -0.          0.15587306
  0.        ]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8408 - accuracy: 0.9000 - val_loss: 0.8223 - val_accuracy: 0.9038
[ 0.         0.         0.        ... -0.         0.1558708  0.       ]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9001 - val_loss: 0.8224 - val_accuracy: 0.9035
[ 0.          0.          0.         ... -0.          0.15607613
  0.        ]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8998 - val_loss: 0.8222 - val_accuracy: 0.9042
[ 0.          0.          0.         ... -0.          0.15618749
  0.        ]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8997 - val_loss: 0.8225 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.          0.15593015
  0.        ]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8999 - val_loss: 0.8221 - val_accuracy: 0.9036
[ 0.         0.         0.        ... -0.         0.1560732  0.       ]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.8999 - val_loss: 0.8219 - val_accuracy: 0.9032
[ 0.          0.          0.         ... -0.          0.15593822
  0.        ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9001 - val_loss: 0.8222 - val_accuracy: 0.9033
[ 0.          0.          0.         ... -0.          0.15585046
  0.        ]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8407 - accuracy: 0.8999 - val_loss: 0.8224 - val_accuracy: 0.9039
[ 0.          0.          0.         ... -0.          0.15607128
  0.        ]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.9000 - val_loss: 0.8225 - val_accuracy: 0.9034
[ 0.          0.          0.         ... -0.          0.15611024
  0.        ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9001 - val_loss: 0.8224 - val_accuracy: 0.9033
[ 0.          0.          0.         ... -0.          0.15624961
  0.        ]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8404 - accuracy: 0.8999 - val_loss: 0.8227 - val_accuracy: 0.9031
[ 0.          0.          0.         ... -0.          0.15619323
  0.        ]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8404 - accuracy: 0.9002 - val_loss: 0.8220 - val_accuracy: 0.9032
[ 0.         0.         0.        ... -0.         0.1561847  0.       ]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8644 - accuracy: 0.8993 - val_loss: 0.8411 - val_accuracy: 0.9050
[ 0.          0.          0.         ... -0.          0.16709255
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 52/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.9002 - val_loss: 0.8401 - val_accuracy: 0.9053
[ 0.         0.         0.        ... -0.         0.1681191  0.       ]
Sparsity at: 0.6457718615879828
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8588 - accuracy: 0.9001 - val_loss: 0.8398 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.          0.16831204
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 54/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8584 - accuracy: 0.9000 - val_loss: 0.8395 - val_accuracy: 0.9052
[ 0.          0.          0.         ... -0.          0.16851896
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8585 - accuracy: 0.9002 - val_loss: 0.8397 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.          0.16847257
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8582 - accuracy: 0.9003 - val_loss: 0.8393 - val_accuracy: 0.9050
[ 0.         0.         0.        ... -0.         0.1684288  0.       ]
Sparsity at: 0.6457718615879828
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8581 - accuracy: 0.9001 - val_loss: 0.8393 - val_accuracy: 0.9050
[ 0.          0.          0.         ... -0.          0.16856475
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8582 - accuracy: 0.9001 - val_loss: 0.8395 - val_accuracy: 0.9050
[ 0.         0.         0.        ... -0.         0.1685264  0.       ]
Sparsity at: 0.6457718615879828
Epoch 59/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8580 - accuracy: 0.9003 - val_loss: 0.8393 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.          0.16869482
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8580 - accuracy: 0.9003 - val_loss: 0.8390 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16872844
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8580 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.          0.16866003
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8580 - accuracy: 0.9003 - val_loss: 0.8388 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16862403
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9003 - val_loss: 0.8393 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16875425
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8579 - accuracy: 0.9000 - val_loss: 0.8389 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.          0.16872597
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9000 - val_loss: 0.8391 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.          0.16869523
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8579 - accuracy: 0.9002 - val_loss: 0.8392 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.          0.16852418
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 67/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8580 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.          0.16840167
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8579 - accuracy: 0.9002 - val_loss: 0.8388 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.          0.16841999
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9001 - val_loss: 0.8391 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.          0.16855407
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8580 - accuracy: 0.9002 - val_loss: 0.8393 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.          0.16848816
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9049
[ 0.         0.         0.        ... -0.         0.1685914  0.       ]
Sparsity at: 0.6457718615879828
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9004 - val_loss: 0.8389 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.          0.16871257
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 73/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9004 - val_loss: 0.8391 - val_accuracy: 0.9051
[ 0.          0.          0.         ... -0.          0.16862175
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8576 - accuracy: 0.9004 - val_loss: 0.8389 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.          0.16859733
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 75/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8389 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16842467
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 76/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9004 - val_loss: 0.8389 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.          0.16854127
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 77/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8578 - accuracy: 0.9005 - val_loss: 0.8390 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.          0.16846651
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 78/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8577 - accuracy: 0.9002 - val_loss: 0.8389 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16853034
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 79/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8579 - accuracy: 0.9000 - val_loss: 0.8389 - val_accuracy: 0.9049
[ 0.          0.          0.         ... -0.          0.16842672
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8577 - accuracy: 0.9002 - val_loss: 0.8387 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.          0.16840588
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8577 - accuracy: 0.9001 - val_loss: 0.8389 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16844028
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8579 - accuracy: 0.9000 - val_loss: 0.8390 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16845325
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8579 - accuracy: 0.9000 - val_loss: 0.8388 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16829588
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8576 - accuracy: 0.9003 - val_loss: 0.8390 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.          0.16849287
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9051
[ 0.          0.          0.         ... -0.          0.16851202
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 86/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9001 - val_loss: 0.8388 - val_accuracy: 0.9043
[ 0.          0.          0.         ... -0.          0.16839112
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9000 - val_loss: 0.8389 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.          0.16838484
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8577 - accuracy: 0.9000 - val_loss: 0.8386 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.          0.16855457
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16866429
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9000 - val_loss: 0.8388 - val_accuracy: 0.9045
[ 0.         0.         0.        ... -0.         0.1684724  0.       ]
Sparsity at: 0.6457718615879828
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8392 - val_accuracy: 0.9053
[ 0.         0.         0.        ... -0.         0.1685284  0.       ]
Sparsity at: 0.6457718615879828
Epoch 92/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9001 - val_loss: 0.8392 - val_accuracy: 0.9051
[ 0.          0.          0.         ... -0.          0.16854161
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8387 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.          0.16842692
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9000 - val_loss: 0.8390 - val_accuracy: 0.9048
[ 0.          0.          0.         ... -0.          0.16829602
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 95/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9001 - val_loss: 0.8387 - val_accuracy: 0.9043
[ 0.          0.          0.         ... -0.          0.16845188
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.          0.16847849
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8576 - accuracy: 0.9002 - val_loss: 0.8387 - val_accuracy: 0.9045
[ 0.          0.          0.         ... -0.          0.16848397
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8579 - accuracy: 0.9001 - val_loss: 0.8388 - val_accuracy: 0.9047
[ 0.          0.          0.         ... -0.          0.16843626
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8577 - accuracy: 0.8998 - val_loss: 0.8388 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16848972
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9001 - val_loss: 0.8387 - val_accuracy: 0.9046
[ 0.          0.          0.         ... -0.          0.16826412
  0.        ]
Sparsity at: 0.6457718615879828
Epoch 101/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9062 - accuracy: 0.8971 - val_loss: 0.8813 - val_accuracy: 0.9029
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 102/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8980 - accuracy: 0.8986 - val_loss: 0.8804 - val_accuracy: 0.9022
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8973 - accuracy: 0.8984 - val_loss: 0.8797 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8968 - accuracy: 0.8986 - val_loss: 0.8793 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8966 - accuracy: 0.8986 - val_loss: 0.8797 - val_accuracy: 0.9021
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8964 - accuracy: 0.8984 - val_loss: 0.8793 - val_accuracy: 0.9021
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8962 - accuracy: 0.8986 - val_loss: 0.8789 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8962 - accuracy: 0.8985 - val_loss: 0.8791 - val_accuracy: 0.9021
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8961 - accuracy: 0.8984 - val_loss: 0.8789 - val_accuracy: 0.9027
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8960 - accuracy: 0.8985 - val_loss: 0.8788 - val_accuracy: 0.9022
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8959 - accuracy: 0.8985 - val_loss: 0.8787 - val_accuracy: 0.9027
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8958 - accuracy: 0.8988 - val_loss: 0.8790 - val_accuracy: 0.9022
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8959 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9021
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8959 - accuracy: 0.8987 - val_loss: 0.8787 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 115/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8959 - accuracy: 0.8984 - val_loss: 0.8787 - val_accuracy: 0.9026
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8785 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9021
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8787 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8958 - accuracy: 0.8986 - val_loss: 0.8787 - val_accuracy: 0.9022
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8985 - val_loss: 0.8786 - val_accuracy: 0.9025
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8785 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8987 - val_loss: 0.8788 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8784 - val_accuracy: 0.9024
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9026
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 125/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8988 - val_loss: 0.8782 - val_accuracy: 0.9027
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8985 - val_loss: 0.8786 - val_accuracy: 0.9027
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 127/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8988 - val_loss: 0.8785 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8784 - val_accuracy: 0.9029
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8785 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8988 - val_loss: 0.8785 - val_accuracy: 0.9026
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.759438707081545
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8984 - val_loss: 0.8785 - val_accuracy: 0.9026
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8986 - val_loss: 0.8782 - val_accuracy: 0.9026
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8985 - val_loss: 0.8788 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8983 - val_loss: 0.8785 - val_accuracy: 0.9028
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 135/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8955 - accuracy: 0.8988 - val_loss: 0.8787 - val_accuracy: 0.9028
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.759438707081545
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8987 - val_loss: 0.8784 - val_accuracy: 0.9027
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8988 - val_loss: 0.8786 - val_accuracy: 0.9022
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8781 - val_accuracy: 0.9028
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8985 - val_loss: 0.8783 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8955 - accuracy: 0.8987 - val_loss: 0.8786 - val_accuracy: 0.9030
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8785 - val_accuracy: 0.9020
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8782 - val_accuracy: 0.9026
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8782 - val_accuracy: 0.9027
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 144/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8955 - accuracy: 0.8988 - val_loss: 0.8783 - val_accuracy: 0.9028
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8955 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9024
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 146/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8986 - val_loss: 0.8785 - val_accuracy: 0.9029
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9029
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8784 - val_accuracy: 0.9025
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.759438707081545
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9022
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 150/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8985 - val_loss: 0.8784 - val_accuracy: 0.9023
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.759438707081545
Epoch 151/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9513 - accuracy: 0.8920 - val_loss: 0.9243 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 152/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9418 - accuracy: 0.8941 - val_loss: 0.9232 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9410 - accuracy: 0.8944 - val_loss: 0.9228 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 154/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9406 - accuracy: 0.8946 - val_loss: 0.9226 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9405 - accuracy: 0.8946 - val_loss: 0.9225 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9403 - accuracy: 0.8944 - val_loss: 0.9224 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9402 - accuracy: 0.8946 - val_loss: 0.9222 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9403 - accuracy: 0.8943 - val_loss: 0.9222 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9402 - accuracy: 0.8945 - val_loss: 0.9223 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9401 - accuracy: 0.8946 - val_loss: 0.9222 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9400 - accuracy: 0.8944 - val_loss: 0.9221 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 162/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8945 - val_loss: 0.9220 - val_accuracy: 0.8982
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8945 - val_loss: 0.9220 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8944 - val_loss: 0.9219 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9400 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8944 - val_loss: 0.9220 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9219 - val_accuracy: 0.8981
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 168/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8946 - val_loss: 0.9221 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 169/500
235/235 [==============================] - 2s 10ms/step - loss: 0.9400 - accuracy: 0.8945 - val_loss: 0.9220 - val_accuracy: 0.8973
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 170/500
235/235 [==============================] - 2s 10ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9220 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8948 - val_loss: 0.9220 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9219 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9398 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9398 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8944 - val_loss: 0.9218 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8943 - val_loss: 0.9219 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 180/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 182/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9400 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8943 - val_loss: 0.9217 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8943 - val_loss: 0.9219 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8948 - val_loss: 0.9220 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 186/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8946 - val_loss: 0.9218 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9398 - accuracy: 0.8946 - val_loss: 0.9220 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9217 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8944 - val_loss: 0.9220 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9218 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8944 - val_loss: 0.9219 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 192/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8944 - val_loss: 0.9218 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9218 - val_accuracy: 0.8981
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8948 - val_loss: 0.9219 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8944 - val_loss: 0.9219 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9218 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9218 - val_accuracy: 0.8980
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8947 - val_loss: 0.9219 - val_accuracy: 0.8979
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8447223712446352
Epoch 201/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0900 - accuracy: 0.8714 - val_loss: 1.0471 - val_accuracy: 0.8846
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059482296137339
Epoch 202/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0653 - accuracy: 0.8819 - val_loss: 1.0437 - val_accuracy: 0.8858
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 203/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0631 - accuracy: 0.8829 - val_loss: 1.0421 - val_accuracy: 0.8868
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0620 - accuracy: 0.8830 - val_loss: 1.0414 - val_accuracy: 0.8874
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 205/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0614 - accuracy: 0.8832 - val_loss: 1.0410 - val_accuracy: 0.8875
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0609 - accuracy: 0.8832 - val_loss: 1.0404 - val_accuracy: 0.8878
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0606 - accuracy: 0.8835 - val_loss: 1.0400 - val_accuracy: 0.8876
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0603 - accuracy: 0.8836 - val_loss: 1.0397 - val_accuracy: 0.8876
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0601 - accuracy: 0.8833 - val_loss: 1.0393 - val_accuracy: 0.8881
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0598 - accuracy: 0.8833 - val_loss: 1.0391 - val_accuracy: 0.8885
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0597 - accuracy: 0.8834 - val_loss: 1.0386 - val_accuracy: 0.8884
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0595 - accuracy: 0.8835 - val_loss: 1.0385 - val_accuracy: 0.8887
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0594 - accuracy: 0.8834 - val_loss: 1.0385 - val_accuracy: 0.8888
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0594 - accuracy: 0.8835 - val_loss: 1.0383 - val_accuracy: 0.8890
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0592 - accuracy: 0.8835 - val_loss: 1.0383 - val_accuracy: 0.8889
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0592 - accuracy: 0.8834 - val_loss: 1.0382 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0591 - accuracy: 0.8837 - val_loss: 1.0382 - val_accuracy: 0.8890
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0591 - accuracy: 0.8837 - val_loss: 1.0382 - val_accuracy: 0.8890
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0591 - accuracy: 0.8837 - val_loss: 1.0381 - val_accuracy: 0.8890
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0590 - accuracy: 0.8836 - val_loss: 1.0382 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0590 - accuracy: 0.8834 - val_loss: 1.0381 - val_accuracy: 0.8888
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8837 - val_loss: 1.0380 - val_accuracy: 0.8889
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8835 - val_loss: 1.0381 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 225/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8892
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8834 - val_loss: 1.0380 - val_accuracy: 0.8892
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 227/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8835 - val_loss: 1.0379 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 228/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0378 - val_accuracy: 0.8896
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0590 - accuracy: 0.8836 - val_loss: 1.0381 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 230/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8834 - val_loss: 1.0380 - val_accuracy: 0.8892
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 231/500
235/235 [==============================] - 3s 11ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0379 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0590 - accuracy: 0.8838 - val_loss: 1.0378 - val_accuracy: 0.8890
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0378 - val_accuracy: 0.8895
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0378 - val_accuracy: 0.8892
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 237/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8888
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0588 - accuracy: 0.8837 - val_loss: 1.0378 - val_accuracy: 0.8893
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0588 - accuracy: 0.8836 - val_loss: 1.0378 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0379 - val_accuracy: 0.8892
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 241/500
235/235 [==============================] - 2s 10ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8895
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 242/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8894
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8891
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8837 - val_loss: 1.0379 - val_accuracy: 0.8892
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8837 - val_loss: 1.0380 - val_accuracy: 0.8893
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8893
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0588 - accuracy: 0.8835 - val_loss: 1.0380 - val_accuracy: 0.8894
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 248/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0377 - val_accuracy: 0.8893
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0379 - val_accuracy: 0.8894
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0588 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8889
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059482296137339
Epoch 251/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2557 - accuracy: 0.8450 - val_loss: 1.1934 - val_accuracy: 0.8707
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2080 - accuracy: 0.8680 - val_loss: 1.1836 - val_accuracy: 0.8750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2019 - accuracy: 0.8697 - val_loss: 1.1802 - val_accuracy: 0.8756
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1994 - accuracy: 0.8698 - val_loss: 1.1785 - val_accuracy: 0.8768
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1981 - accuracy: 0.8701 - val_loss: 1.1777 - val_accuracy: 0.8766
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1973 - accuracy: 0.8702 - val_loss: 1.1772 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1969 - accuracy: 0.8705 - val_loss: 1.1769 - val_accuracy: 0.8769
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1965 - accuracy: 0.8702 - val_loss: 1.1767 - val_accuracy: 0.8766
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1963 - accuracy: 0.8703 - val_loss: 1.1765 - val_accuracy: 0.8765
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1962 - accuracy: 0.8703 - val_loss: 1.1763 - val_accuracy: 0.8763
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 261/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1960 - accuracy: 0.8704 - val_loss: 1.1763 - val_accuracy: 0.8765
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1960 - accuracy: 0.8705 - val_loss: 1.1762 - val_accuracy: 0.8765
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1959 - accuracy: 0.8703 - val_loss: 1.1762 - val_accuracy: 0.8764
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1958 - accuracy: 0.8704 - val_loss: 1.1761 - val_accuracy: 0.8767
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1958 - accuracy: 0.8703 - val_loss: 1.1761 - val_accuracy: 0.8773
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1957 - accuracy: 0.8705 - val_loss: 1.1761 - val_accuracy: 0.8772
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 267/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1957 - accuracy: 0.8705 - val_loss: 1.1761 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1957 - accuracy: 0.8705 - val_loss: 1.1760 - val_accuracy: 0.8773
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 269/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8772
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1760 - val_accuracy: 0.8772
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 271/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1957 - accuracy: 0.8706 - val_loss: 1.1760 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1760 - val_accuracy: 0.8773
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8704 - val_loss: 1.1759 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1760 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 275/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1760 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1760 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8774
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1758 - val_accuracy: 0.8774
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 284/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1955 - accuracy: 0.8708 - val_loss: 1.1759 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1760 - val_accuracy: 0.8773
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1758 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 287/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8708 - val_loss: 1.1758 - val_accuracy: 0.8773
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1955 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8768
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1758 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 290/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1758 - val_accuracy: 0.8771
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 292/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1758 - val_accuracy: 0.8772
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1955 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8769
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1758 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1955 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8768
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1955 - accuracy: 0.8708 - val_loss: 1.1758 - val_accuracy: 0.8769
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 297/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1758 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 298/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1955 - accuracy: 0.8708 - val_loss: 1.1759 - val_accuracy: 0.8772
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 299/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1758 - val_accuracy: 0.8768
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 300/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1758 - val_accuracy: 0.8770
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.946938707081545
Epoch 301/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4323 - accuracy: 0.7466 - val_loss: 1.3752 - val_accuracy: 0.7682
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 302/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3829 - accuracy: 0.7654 - val_loss: 1.3633 - val_accuracy: 0.7734
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 303/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3769 - accuracy: 0.7677 - val_loss: 1.3599 - val_accuracy: 0.7735
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 304/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3751 - accuracy: 0.7688 - val_loss: 1.3585 - val_accuracy: 0.7738
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 305/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3742 - accuracy: 0.7693 - val_loss: 1.3578 - val_accuracy: 0.7744
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3738 - accuracy: 0.7694 - val_loss: 1.3573 - val_accuracy: 0.7747
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 307/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3735 - accuracy: 0.7697 - val_loss: 1.3570 - val_accuracy: 0.7748
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3733 - accuracy: 0.7699 - val_loss: 1.3568 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3731 - accuracy: 0.7698 - val_loss: 1.3566 - val_accuracy: 0.7752
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3730 - accuracy: 0.7699 - val_loss: 1.3564 - val_accuracy: 0.7748
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3730 - accuracy: 0.7698 - val_loss: 1.3564 - val_accuracy: 0.7748
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3729 - accuracy: 0.7699 - val_loss: 1.3563 - val_accuracy: 0.7748
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3728 - accuracy: 0.7699 - val_loss: 1.3563 - val_accuracy: 0.7748
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3728 - accuracy: 0.7699 - val_loss: 1.3563 - val_accuracy: 0.7747
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 315/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3728 - accuracy: 0.7699 - val_loss: 1.3563 - val_accuracy: 0.7748
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3562 - val_accuracy: 0.7749
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3562 - val_accuracy: 0.7747
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3562 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3562 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 320/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3562 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 321/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 323/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 324/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7749
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3561 - val_accuracy: 0.7752
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3561 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7752
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 331/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 332/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7752
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7752
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7703 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3726 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 339/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3560 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3560 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3560 - val_accuracy: 0.7752
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7749
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 344/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7748
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 345/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3726 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 347/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7750
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3726 - accuracy: 0.7701 - val_loss: 1.3560 - val_accuracy: 0.7751
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3560 - val_accuracy: 0.7752
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9717341738197425
Epoch 351/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7640 - accuracy: 0.5156 - val_loss: 1.7262 - val_accuracy: 0.5298
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 352/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7226 - accuracy: 0.5956 - val_loss: 1.7166 - val_accuracy: 0.6094
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7173 - accuracy: 0.6056 - val_loss: 1.7138 - val_accuracy: 0.6100
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7153 - accuracy: 0.6063 - val_loss: 1.7124 - val_accuracy: 0.6102
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7142 - accuracy: 0.6067 - val_loss: 1.7115 - val_accuracy: 0.6102
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7135 - accuracy: 0.6067 - val_loss: 1.7110 - val_accuracy: 0.6106
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.6066 - val_loss: 1.7106 - val_accuracy: 0.6108
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7128 - accuracy: 0.6067 - val_loss: 1.7102 - val_accuracy: 0.6105
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7125 - accuracy: 0.6068 - val_loss: 1.7100 - val_accuracy: 0.6107
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 360/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7123 - accuracy: 0.6068 - val_loss: 1.7098 - val_accuracy: 0.6105
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7122 - accuracy: 0.6069 - val_loss: 1.7096 - val_accuracy: 0.6106
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7121 - accuracy: 0.6069 - val_loss: 1.7096 - val_accuracy: 0.6105
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7120 - accuracy: 0.6068 - val_loss: 1.7095 - val_accuracy: 0.6106
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7120 - accuracy: 0.6067 - val_loss: 1.7094 - val_accuracy: 0.6105
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 365/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7119 - accuracy: 0.6067 - val_loss: 1.7093 - val_accuracy: 0.6109
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7118 - accuracy: 0.6066 - val_loss: 1.7093 - val_accuracy: 0.6109
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.6066 - val_loss: 1.7093 - val_accuracy: 0.6111
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.6066 - val_loss: 1.7092 - val_accuracy: 0.6109
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7117 - accuracy: 0.6066 - val_loss: 1.7092 - val_accuracy: 0.6110
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7117 - accuracy: 0.6068 - val_loss: 1.7092 - val_accuracy: 0.6110
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7117 - accuracy: 0.6068 - val_loss: 1.7092 - val_accuracy: 0.6109
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7117 - accuracy: 0.6068 - val_loss: 1.7091 - val_accuracy: 0.6109
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 373/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7117 - accuracy: 0.6069 - val_loss: 1.7092 - val_accuracy: 0.6108
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 374/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7116 - accuracy: 0.6068 - val_loss: 1.7091 - val_accuracy: 0.6110
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7116 - accuracy: 0.6068 - val_loss: 1.7091 - val_accuracy: 0.6109
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7116 - accuracy: 0.6067 - val_loss: 1.7091 - val_accuracy: 0.6110
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 377/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7116 - accuracy: 0.6069 - val_loss: 1.7091 - val_accuracy: 0.6111
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 378/500
235/235 [==============================] - 2s 10ms/step - loss: 1.7116 - accuracy: 0.6069 - val_loss: 1.7091 - val_accuracy: 0.6111
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7116 - accuracy: 0.6068 - val_loss: 1.7091 - val_accuracy: 0.6110
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7090 - val_accuracy: 0.6111
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 381/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7090 - val_accuracy: 0.6111
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 382/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7091 - val_accuracy: 0.6110
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7091 - val_accuracy: 0.6112
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7090 - val_accuracy: 0.6111
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 385/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6070 - val_loss: 1.7090 - val_accuracy: 0.6112
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6112
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7115 - accuracy: 0.6070 - val_loss: 1.7090 - val_accuracy: 0.6112
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 388/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6112
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6072 - val_loss: 1.7090 - val_accuracy: 0.6114
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6115
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 391/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6114
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7114 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6115
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6113
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6072 - val_loss: 1.7090 - val_accuracy: 0.6114
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 395/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6072 - val_loss: 1.7090 - val_accuracy: 0.6114
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 396/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6114
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6071 - val_loss: 1.7089 - val_accuracy: 0.6114
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6072 - val_loss: 1.7089 - val_accuracy: 0.6114
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6117
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6072 - val_loss: 1.7090 - val_accuracy: 0.6115
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9843414699570815
Epoch 401/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7768 - accuracy: 0.5136 - val_loss: 1.7602 - val_accuracy: 0.5430
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 402/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7557 - accuracy: 0.5387 - val_loss: 1.7520 - val_accuracy: 0.5431
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 403/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7518 - accuracy: 0.5390 - val_loss: 1.7501 - val_accuracy: 0.5425
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 404/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7509 - accuracy: 0.5391 - val_loss: 1.7496 - val_accuracy: 0.5424
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7506 - accuracy: 0.5392 - val_loss: 1.7494 - val_accuracy: 0.5426
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 406/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5393 - val_loss: 1.7493 - val_accuracy: 0.5426
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 407/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5426
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 410/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 411/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 413/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 416/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 417/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 418/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5426
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5429
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 423/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5426
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 428/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 429/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 430/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 431/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 435/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 436/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 437/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 440/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5425
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 447/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 456/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9892871512875536
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 460/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 462/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 466/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9892871512875536
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5428
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 469/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 470/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 471/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 475/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 476/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 480/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 483/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 484/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5427
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 488/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 489/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5389 - val_loss: 1.7492 - val_accuracy: 0.5429
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5428
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9892871512875536
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 495/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9892871512875536
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9892871512875536
Epoch 1/500
235/235 [==============================] - 4s 9ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.2541 - val_accuracy: 0.9718
[ 0.         0.        -0.        ... -0.         0.5798692  0.       ]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3818e-04 - accuracy: 0.9997 - val_loss: 0.2485 - val_accuracy: 0.9722
[ 0.         0.        -0.        ... -0.         0.5817863 -0.       ]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8454e-04 - accuracy: 0.9999 - val_loss: 0.2441 - val_accuracy: 0.9742
[ 0.          0.         -0.         ... -0.          0.58657855
  0.        ]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6396e-05 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9737
[ 0.          0.         -0.         ... -0.          0.58737755
  0.        ]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8413e-05 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9739
[ 0.          0.         -0.         ...  0.          0.58724725
  0.        ]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4000e-05 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9743
[ 0.          0.         -0.         ... -0.          0.58759534
  0.        ]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1905e-05 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9745
[ 0.         0.        -0.        ... -0.         0.5878739  0.       ]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0487e-05 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9747
[ 0.         0.        -0.        ... -0.         0.5881475  0.       ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3982e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9748
[ 0.         0.        -0.        ... -0.         0.5884196  0.       ]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 8.5122e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9747
[ 0.         0.        -0.        ... -0.         0.5886924  0.       ]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7713e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9746
[ 0.          0.         -0.         ... -0.          0.58897007
  0.        ]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 7.1351e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9746
[ 0.          0.         -0.         ... -0.          0.58925337
  0.        ]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5787e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9746
[ 0.          0.         -0.         ... -0.          0.58954704
  0.        ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0861e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9747
[ 0.          0.         -0.         ...  0.          0.58984965
  0.        ]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 2s 9ms/step - loss: 5.6436e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9746
[ 0.         0.        -0.        ... -0.         0.5901675  0.       ]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 5.2441e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9744
[ 0.         0.        -0.        ... -0.         0.5904983  0.       ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 2s 10ms/step - loss: 4.8797e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9745
[ 0.          0.         -0.         ... -0.          0.59084624
  0.        ]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 4.5464e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9745
[ 0.          0.         -0.         ...  0.          0.59121466
  0.        ]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2397e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9746
[ 0.          0.         -0.         ... -0.          0.59160346
  0.        ]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9567e-06 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9744
[ 0.         0.        -0.        ... -0.         0.5920179  0.       ]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6934e-06 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9744
[ 0.          0.         -0.         ... -0.          0.59245455
  0.        ]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4487e-06 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9745
[ 0.         0.        -0.        ...  0.         0.5929268  0.       ]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2210e-06 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9745
[ 0.         0.        -0.        ... -0.         0.5934342  0.       ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0079e-06 - accuracy: 1.0000 - val_loss: 0.2417 - val_accuracy: 0.9745
[ 0.         0.        -0.        ... -0.         0.5939757  0.       ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8102e-06 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9743
[ 0.          0.         -0.         ... -0.          0.59456307
  0.        ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6242e-06 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9745
[ 0.         0.        -0.        ... -0.         0.5951844  0.       ]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4502e-06 - accuracy: 1.0000 - val_loss: 0.2421 - val_accuracy: 0.9745
[ 0.         0.        -0.        ... -0.         0.5958549  0.       ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2876e-06 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9742
[ 0.          0.         -0.         ... -0.          0.59657866
  0.        ]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1337e-06 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9742
[ 0.          0.         -0.         ... -0.          0.59734833
  0.        ]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9909e-06 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9740
[ 0.          0.         -0.         ... -0.          0.59817684
  0.        ]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8564e-06 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9740
[ 0.          0.         -0.         ...  0.          0.59905386
  0.        ]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7293e-06 - accuracy: 1.0000 - val_loss: 0.2432 - val_accuracy: 0.9739
[ 0.          0.         -0.         ...  0.          0.59998876
  0.        ]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6108e-06 - accuracy: 1.0000 - val_loss: 0.2435 - val_accuracy: 0.9739
[ 0.         0.        -0.        ...  0.         0.6009811  0.       ]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4997e-06 - accuracy: 1.0000 - val_loss: 0.2438 - val_accuracy: 0.9738
[ 0.          0.         -0.         ...  0.          0.60203713
  0.        ]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3952e-06 - accuracy: 1.0000 - val_loss: 0.2442 - val_accuracy: 0.9739
[ 0.         0.        -0.        ...  0.         0.6031557  0.       ]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2971e-06 - accuracy: 1.0000 - val_loss: 0.2446 - val_accuracy: 0.9739
[ 0.          0.         -0.         ...  0.          0.60432386
  0.        ]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2055e-06 - accuracy: 1.0000 - val_loss: 0.2450 - val_accuracy: 0.9739
[ 0.         0.        -0.        ...  0.         0.6055551  0.       ]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1196e-06 - accuracy: 1.0000 - val_loss: 0.2454 - val_accuracy: 0.9739
[ 0.         0.        -0.        ...  0.         0.6068444  0.       ]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0387e-06 - accuracy: 1.0000 - val_loss: 0.2459 - val_accuracy: 0.9738
[ 0.        0.       -0.       ...  0.        0.608189  0.      ]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 9.6378e-07 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9737
[ 0.         0.        -0.        ...  0.         0.6095982  0.       ]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 8.9336e-07 - accuracy: 1.0000 - val_loss: 0.2469 - val_accuracy: 0.9740
[ 0.          0.         -0.         ...  0.          0.61105037
  0.        ]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2757e-07 - accuracy: 1.0000 - val_loss: 0.2475 - val_accuracy: 0.9740
[ 0.          0.         -0.         ...  0.          0.61256397
  0.        ]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 7.6626e-07 - accuracy: 1.0000 - val_loss: 0.2481 - val_accuracy: 0.9740
[ 0.         0.        -0.        ...  0.         0.6141519  0.       ]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0855e-07 - accuracy: 1.0000 - val_loss: 0.2487 - val_accuracy: 0.9740
[ 0.          0.         -0.         ...  0.          0.61576706
  0.        ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5538e-07 - accuracy: 1.0000 - val_loss: 0.2494 - val_accuracy: 0.9740
[ 0.         0.        -0.        ...  0.         0.6174415  0.       ]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0573e-07 - accuracy: 1.0000 - val_loss: 0.2500 - val_accuracy: 0.9741
[ 0.          0.         -0.         ...  0.          0.61917865
  0.        ]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5909e-07 - accuracy: 1.0000 - val_loss: 0.2507 - val_accuracy: 0.9742
[ 0.         0.        -0.        ...  0.         0.6209467  0.       ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1612e-07 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9742
[ 0.          0.         -0.         ...  0.          0.62279207
  0.        ]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7616e-07 - accuracy: 1.0000 - val_loss: 0.2522 - val_accuracy: 0.9743
[ 0.          0.         -0.         ...  0.          0.62463796
  0.        ]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3905e-07 - accuracy: 1.0000 - val_loss: 0.2530 - val_accuracy: 0.9743
[ 0.         0.        -0.        ...  0.         0.6265392  0.       ]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0249 - accuracy: 0.9924 - val_loss: 0.1987 - val_accuracy: 0.9741
[ 0.  0. -0. ... -0. -0. -0.]
Sparsity at: 0.6458724517167382
Epoch 52/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.1947 - val_accuracy: 0.9745
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.6458724517167382
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0493e-04 - accuracy: 0.9999 - val_loss: 0.1921 - val_accuracy: 0.9753
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 3.6043e-04 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9752
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5860e-04 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9750
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1958e-04 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9750
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9434e-04 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9750
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7441e-04 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9748
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 59/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5811e-04 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9749
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4400e-04 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9749
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3169e-04 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9749
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2083e-04 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9749
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1108e-04 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9751
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0218e-04 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9753
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 9.4111e-05 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9755
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 8.6732e-05 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9755
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9925e-05 - accuracy: 1.0000 - val_loss: 0.1978 - val_accuracy: 0.9754
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3677e-05 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9754
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7878e-05 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9752
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2510e-05 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9752
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7529e-05 - accuracy: 1.0000 - val_loss: 0.2000 - val_accuracy: 0.9752
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2922e-05 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9753
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8663e-05 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9755
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4734e-05 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9756
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1061e-05 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9755
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7690e-05 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9756
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4597e-05 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9756
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1696e-05 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9760
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9006e-05 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9760
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6545e-05 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9759
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4278e-05 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9760
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2174e-05 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9759
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0260e-05 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9760
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8437e-05 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9760
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6790e-05 - accuracy: 1.0000 - val_loss: 0.2104 - val_accuracy: 0.9762
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5270e-05 - accuracy: 1.0000 - val_loss: 0.2113 - val_accuracy: 0.9761
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3900e-05 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.9761
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2608e-05 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9761
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1448e-05 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9762
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0371e-05 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9763
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 9.3953e-06 - accuracy: 1.0000 - val_loss: 0.2163 - val_accuracy: 0.9763
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 92/500
235/235 [==============================] - 2s 9ms/step - loss: 8.5032e-06 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9763
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 93/500
235/235 [==============================] - 2s 9ms/step - loss: 7.6918e-06 - accuracy: 1.0000 - val_loss: 0.2185 - val_accuracy: 0.9764
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 6.9502e-06 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9763
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2768e-06 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9763
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6628e-06 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.9762
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1007e-06 - accuracy: 1.0000 - val_loss: 0.2231 - val_accuracy: 0.9761
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6065e-06 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9761
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1407e-06 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 0.9760
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7283e-06 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.9760
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.6458724517167382
Epoch 101/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0397 - accuracy: 0.9885 - val_loss: 0.1696 - val_accuracy: 0.9715
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.759438707081545
Epoch 102/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.1657 - val_accuracy: 0.9714
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0052 - accuracy: 0.9987 - val_loss: 0.1649 - val_accuracy: 0.9719
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.1644 - val_accuracy: 0.9722
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9999 - val_loss: 0.1647 - val_accuracy: 0.9727
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1651 - val_accuracy: 0.9721
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1656 - val_accuracy: 0.9718
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1662 - val_accuracy: 0.9720
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1668 - val_accuracy: 0.9719
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1674 - val_accuracy: 0.9723
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3116e-04 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9727
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3286e-04 - accuracy: 1.0000 - val_loss: 0.1689 - val_accuracy: 0.9728
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 113/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5030e-04 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9730
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 6.7847e-04 - accuracy: 1.0000 - val_loss: 0.1704 - val_accuracy: 0.9729
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1487e-04 - accuracy: 1.0000 - val_loss: 0.1713 - val_accuracy: 0.9728
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6012e-04 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9730
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0944e-04 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9730
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6454e-04 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9729
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2337e-04 - accuracy: 1.0000 - val_loss: 0.1748 - val_accuracy: 0.9728
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8571e-04 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9729
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5163e-04 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9727
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2110e-04 - accuracy: 1.0000 - val_loss: 0.1780 - val_accuracy: 0.9729
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9283e-04 - accuracy: 1.0000 - val_loss: 0.1791 - val_accuracy: 0.9730
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6661e-04 - accuracy: 1.0000 - val_loss: 0.1802 - val_accuracy: 0.9729
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4297e-04 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9730
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2086e-04 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9730
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0093e-04 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9730
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8265e-04 - accuracy: 1.0000 - val_loss: 0.1852 - val_accuracy: 0.9731
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6589e-04 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9733
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5057e-04 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9731
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3675e-04 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9729
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2381e-04 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9728
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1222e-04 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9727
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0163e-04 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9725
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 9.1841e-05 - accuracy: 1.0000 - val_loss: 0.1956 - val_accuracy: 0.9725
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 8.3015e-05 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9727
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 7.4879e-05 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9727
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7743e-05 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9723
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1115e-05 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9724
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5203e-05 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9723
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9760e-05 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9725
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4851e-05 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9725
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0384e-05 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9724
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6411e-05 - accuracy: 1.0000 - val_loss: 0.2118 - val_accuracy: 0.9725
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2790e-05 - accuracy: 1.0000 - val_loss: 0.2137 - val_accuracy: 0.9725
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9464e-05 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9725
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6493e-05 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9726
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3852e-05 - accuracy: 1.0000 - val_loss: 0.2197 - val_accuracy: 0.9726
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1453e-05 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9726
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9262e-05 - accuracy: 1.0000 - val_loss: 0.2238 - val_accuracy: 0.9729
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.759438707081545
Epoch 151/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1001 - accuracy: 0.9732 - val_loss: 0.2054 - val_accuracy: 0.9622
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 152/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0400 - accuracy: 0.9870 - val_loss: 0.1927 - val_accuracy: 0.9642
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 153/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0300 - accuracy: 0.9902 - val_loss: 0.1868 - val_accuracy: 0.9646
[ 0.  0. -0. ... -0.  0. -0.]
Sparsity at: 0.8448229613733905
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0246 - accuracy: 0.9921 - val_loss: 0.1837 - val_accuracy: 0.9654
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0210 - accuracy: 0.9935 - val_loss: 0.1813 - val_accuracy: 0.9656
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0184 - accuracy: 0.9944 - val_loss: 0.1795 - val_accuracy: 0.9657
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0163 - accuracy: 0.9952 - val_loss: 0.1784 - val_accuracy: 0.9659
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0146 - accuracy: 0.9958 - val_loss: 0.1775 - val_accuracy: 0.9666
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0132 - accuracy: 0.9964 - val_loss: 0.1767 - val_accuracy: 0.9669
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0120 - accuracy: 0.9970 - val_loss: 0.1765 - val_accuracy: 0.9670
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0109 - accuracy: 0.9974 - val_loss: 0.1761 - val_accuracy: 0.9672
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0100 - accuracy: 0.9979 - val_loss: 0.1759 - val_accuracy: 0.9670
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.8448229613733905
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0092 - accuracy: 0.9982 - val_loss: 0.1761 - val_accuracy: 0.9664
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 164/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0085 - accuracy: 0.9985 - val_loss: 0.1764 - val_accuracy: 0.9663
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0079 - accuracy: 0.9987 - val_loss: 0.1767 - val_accuracy: 0.9663
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0074 - accuracy: 0.9989 - val_loss: 0.1771 - val_accuracy: 0.9661
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0069 - accuracy: 0.9991 - val_loss: 0.1779 - val_accuracy: 0.9659
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0064 - accuracy: 0.9993 - val_loss: 0.1783 - val_accuracy: 0.9656
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0060 - accuracy: 0.9995 - val_loss: 0.1789 - val_accuracy: 0.9655
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0056 - accuracy: 0.9996 - val_loss: 0.1799 - val_accuracy: 0.9658
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.8448229613733905
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0053 - accuracy: 0.9997 - val_loss: 0.1806 - val_accuracy: 0.9657
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0049 - accuracy: 0.9997 - val_loss: 0.1813 - val_accuracy: 0.9657
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9998 - val_loss: 0.1823 - val_accuracy: 0.9657
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0044 - accuracy: 0.9998 - val_loss: 0.1832 - val_accuracy: 0.9659
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0041 - accuracy: 0.9999 - val_loss: 0.1843 - val_accuracy: 0.9663
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0039 - accuracy: 0.9999 - val_loss: 0.1852 - val_accuracy: 0.9661
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9661
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9661
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 179/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9660
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 180/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9663
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9664
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9665
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9664
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9666
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9667
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9663
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9665
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9667
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9668
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9666
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9667
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9667
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9668
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9668
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9668
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9668
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9669
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.2170 - val_accuracy: 0.9668
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7319e-04 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.9666
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 9.1524e-04 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9666
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.8448229613733905
Epoch 201/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1814 - accuracy: 0.9504 - val_loss: 0.2344 - val_accuracy: 0.9500
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 202/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1017 - accuracy: 0.9680 - val_loss: 0.2128 - val_accuracy: 0.9536
[ 0.  0. -0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 203/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0857 - accuracy: 0.9726 - val_loss: 0.2022 - val_accuracy: 0.9546
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0766 - accuracy: 0.9749 - val_loss: 0.1956 - val_accuracy: 0.9558
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 205/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0702 - accuracy: 0.9766 - val_loss: 0.1907 - val_accuracy: 0.9565
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 206/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0655 - accuracy: 0.9780 - val_loss: 0.1871 - val_accuracy: 0.9570
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 207/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0617 - accuracy: 0.9788 - val_loss: 0.1841 - val_accuracy: 0.9578
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0586 - accuracy: 0.9799 - val_loss: 0.1816 - val_accuracy: 0.9581
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0560 - accuracy: 0.9807 - val_loss: 0.1795 - val_accuracy: 0.9587
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0538 - accuracy: 0.9816 - val_loss: 0.1778 - val_accuracy: 0.9587
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0519 - accuracy: 0.9822 - val_loss: 0.1762 - val_accuracy: 0.9593
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 212/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0503 - accuracy: 0.9830 - val_loss: 0.1749 - val_accuracy: 0.9593
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 213/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0488 - accuracy: 0.9836 - val_loss: 0.1737 - val_accuracy: 0.9598
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0475 - accuracy: 0.9839 - val_loss: 0.1728 - val_accuracy: 0.9603
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 215/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0463 - accuracy: 0.9844 - val_loss: 0.1719 - val_accuracy: 0.9602
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0452 - accuracy: 0.9846 - val_loss: 0.1712 - val_accuracy: 0.9603
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0442 - accuracy: 0.9853 - val_loss: 0.1705 - val_accuracy: 0.9600
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0433 - accuracy: 0.9855 - val_loss: 0.1699 - val_accuracy: 0.9601
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 219/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0425 - accuracy: 0.9858 - val_loss: 0.1694 - val_accuracy: 0.9603
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0417 - accuracy: 0.9860 - val_loss: 0.1690 - val_accuracy: 0.9602
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0410 - accuracy: 0.9863 - val_loss: 0.1686 - val_accuracy: 0.9605
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0403 - accuracy: 0.9867 - val_loss: 0.1683 - val_accuracy: 0.9608
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0396 - accuracy: 0.9870 - val_loss: 0.1680 - val_accuracy: 0.9610
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0390 - accuracy: 0.9873 - val_loss: 0.1678 - val_accuracy: 0.9611
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0384 - accuracy: 0.9876 - val_loss: 0.1676 - val_accuracy: 0.9612
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0379 - accuracy: 0.9877 - val_loss: 0.1675 - val_accuracy: 0.9614
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0374 - accuracy: 0.9880 - val_loss: 0.1673 - val_accuracy: 0.9615
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 228/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0369 - accuracy: 0.9880 - val_loss: 0.1672 - val_accuracy: 0.9618
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0364 - accuracy: 0.9883 - val_loss: 0.1671 - val_accuracy: 0.9621
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0359 - accuracy: 0.9886 - val_loss: 0.1671 - val_accuracy: 0.9619
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0355 - accuracy: 0.9887 - val_loss: 0.1671 - val_accuracy: 0.9618
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0350 - accuracy: 0.9888 - val_loss: 0.1671 - val_accuracy: 0.9621
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0346 - accuracy: 0.9889 - val_loss: 0.1672 - val_accuracy: 0.9620
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 234/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0342 - accuracy: 0.9891 - val_loss: 0.1672 - val_accuracy: 0.9623
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0338 - accuracy: 0.9894 - val_loss: 0.1673 - val_accuracy: 0.9624
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0334 - accuracy: 0.9895 - val_loss: 0.1674 - val_accuracy: 0.9623
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0331 - accuracy: 0.9897 - val_loss: 0.1675 - val_accuracy: 0.9620
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0327 - accuracy: 0.9898 - val_loss: 0.1676 - val_accuracy: 0.9622
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0324 - accuracy: 0.9901 - val_loss: 0.1678 - val_accuracy: 0.9622
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0320 - accuracy: 0.9903 - val_loss: 0.1680 - val_accuracy: 0.9624
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0317 - accuracy: 0.9904 - val_loss: 0.1681 - val_accuracy: 0.9625
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0314 - accuracy: 0.9905 - val_loss: 0.1683 - val_accuracy: 0.9623
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0311 - accuracy: 0.9906 - val_loss: 0.1685 - val_accuracy: 0.9622
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0308 - accuracy: 0.9907 - val_loss: 0.1688 - val_accuracy: 0.9624
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0305 - accuracy: 0.9908 - val_loss: 0.1690 - val_accuracy: 0.9626
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0302 - accuracy: 0.9909 - val_loss: 0.1692 - val_accuracy: 0.9628
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0299 - accuracy: 0.9911 - val_loss: 0.1695 - val_accuracy: 0.9627
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0296 - accuracy: 0.9911 - val_loss: 0.1697 - val_accuracy: 0.9624
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0293 - accuracy: 0.9912 - val_loss: 0.1700 - val_accuracy: 0.9624
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0291 - accuracy: 0.9913 - val_loss: 0.1703 - val_accuracy: 0.9624
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9059985246781116
Epoch 251/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4915 - accuracy: 0.8496 - val_loss: 0.3458 - val_accuracy: 0.8989
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2890 - accuracy: 0.9065 - val_loss: 0.2948 - val_accuracy: 0.9139
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2541 - accuracy: 0.9188 - val_loss: 0.2722 - val_accuracy: 0.9234
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2357 - accuracy: 0.9247 - val_loss: 0.2588 - val_accuracy: 0.9267
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2235 - accuracy: 0.9287 - val_loss: 0.2494 - val_accuracy: 0.9289
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2145 - accuracy: 0.9315 - val_loss: 0.2424 - val_accuracy: 0.9309
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2074 - accuracy: 0.9340 - val_loss: 0.2368 - val_accuracy: 0.9316
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2016 - accuracy: 0.9358 - val_loss: 0.2322 - val_accuracy: 0.9327
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 259/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1966 - accuracy: 0.9374 - val_loss: 0.2284 - val_accuracy: 0.9340
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1924 - accuracy: 0.9388 - val_loss: 0.2250 - val_accuracy: 0.9348
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1886 - accuracy: 0.9400 - val_loss: 0.2221 - val_accuracy: 0.9360
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1853 - accuracy: 0.9408 - val_loss: 0.2195 - val_accuracy: 0.9364
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1823 - accuracy: 0.9416 - val_loss: 0.2172 - val_accuracy: 0.9369
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1796 - accuracy: 0.9429 - val_loss: 0.2150 - val_accuracy: 0.9382
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1772 - accuracy: 0.9435 - val_loss: 0.2131 - val_accuracy: 0.9390
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1750 - accuracy: 0.9442 - val_loss: 0.2114 - val_accuracy: 0.9393
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1729 - accuracy: 0.9449 - val_loss: 0.2098 - val_accuracy: 0.9391
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 268/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1710 - accuracy: 0.9454 - val_loss: 0.2083 - val_accuracy: 0.9399
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 269/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1693 - accuracy: 0.9460 - val_loss: 0.2070 - val_accuracy: 0.9402
[ 0.  0. -0. ...  0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1677 - accuracy: 0.9467 - val_loss: 0.2057 - val_accuracy: 0.9413
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1662 - accuracy: 0.9474 - val_loss: 0.2045 - val_accuracy: 0.9417
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1648 - accuracy: 0.9478 - val_loss: 0.2033 - val_accuracy: 0.9420
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1635 - accuracy: 0.9482 - val_loss: 0.2023 - val_accuracy: 0.9424
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1622 - accuracy: 0.9485 - val_loss: 0.2013 - val_accuracy: 0.9424
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1611 - accuracy: 0.9488 - val_loss: 0.2004 - val_accuracy: 0.9425
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1600 - accuracy: 0.9491 - val_loss: 0.1995 - val_accuracy: 0.9427
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1590 - accuracy: 0.9495 - val_loss: 0.1987 - val_accuracy: 0.9426
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 278/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1581 - accuracy: 0.9499 - val_loss: 0.1980 - val_accuracy: 0.9430
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1571 - accuracy: 0.9501 - val_loss: 0.1972 - val_accuracy: 0.9431
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1563 - accuracy: 0.9505 - val_loss: 0.1965 - val_accuracy: 0.9432
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1555 - accuracy: 0.9509 - val_loss: 0.1959 - val_accuracy: 0.9435
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1547 - accuracy: 0.9512 - val_loss: 0.1953 - val_accuracy: 0.9438
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1540 - accuracy: 0.9514 - val_loss: 0.1947 - val_accuracy: 0.9441
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1532 - accuracy: 0.9516 - val_loss: 0.1941 - val_accuracy: 0.9440
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1526 - accuracy: 0.9517 - val_loss: 0.1935 - val_accuracy: 0.9442
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1519 - accuracy: 0.9519 - val_loss: 0.1930 - val_accuracy: 0.9449
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1513 - accuracy: 0.9520 - val_loss: 0.1925 - val_accuracy: 0.9450
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1507 - accuracy: 0.9520 - val_loss: 0.1921 - val_accuracy: 0.9451
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1501 - accuracy: 0.9523 - val_loss: 0.1916 - val_accuracy: 0.9451
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1495 - accuracy: 0.9527 - val_loss: 0.1912 - val_accuracy: 0.9454
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1490 - accuracy: 0.9527 - val_loss: 0.1907 - val_accuracy: 0.9456
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 292/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1485 - accuracy: 0.9530 - val_loss: 0.1903 - val_accuracy: 0.9458
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1480 - accuracy: 0.9532 - val_loss: 0.1900 - val_accuracy: 0.9461
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1475 - accuracy: 0.9533 - val_loss: 0.1895 - val_accuracy: 0.9463
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1470 - accuracy: 0.9532 - val_loss: 0.1891 - val_accuracy: 0.9464
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1465 - accuracy: 0.9535 - val_loss: 0.1888 - val_accuracy: 0.9465
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 297/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1461 - accuracy: 0.9536 - val_loss: 0.1884 - val_accuracy: 0.9467
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 298/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1457 - accuracy: 0.9537 - val_loss: 0.1881 - val_accuracy: 0.9469
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1453 - accuracy: 0.9539 - val_loss: 0.1878 - val_accuracy: 0.9471
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1448 - accuracy: 0.9541 - val_loss: 0.1874 - val_accuracy: 0.9473
[ 0.  0. -0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 301/500
235/235 [==============================] - 2s 8ms/step - loss: 0.9672 - accuracy: 0.6791 - val_loss: 0.8213 - val_accuracy: 0.7273
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 302/500
235/235 [==============================] - 2s 8ms/step - loss: 0.7850 - accuracy: 0.7347 - val_loss: 0.7587 - val_accuracy: 0.7489
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 0.7402 - accuracy: 0.7516 - val_loss: 0.7238 - val_accuracy: 0.7639
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 0.7102 - accuracy: 0.7639 - val_loss: 0.6985 - val_accuracy: 0.7744
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6882 - accuracy: 0.7723 - val_loss: 0.6802 - val_accuracy: 0.7826
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6726 - accuracy: 0.7772 - val_loss: 0.6661 - val_accuracy: 0.7878
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6605 - accuracy: 0.7821 - val_loss: 0.6544 - val_accuracy: 0.7916
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6508 - accuracy: 0.7859 - val_loss: 0.6453 - val_accuracy: 0.7931
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6431 - accuracy: 0.7883 - val_loss: 0.6382 - val_accuracy: 0.7945
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 310/500
235/235 [==============================] - 2s 9ms/step - loss: 0.6369 - accuracy: 0.7907 - val_loss: 0.6327 - val_accuracy: 0.7965
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6317 - accuracy: 0.7930 - val_loss: 0.6281 - val_accuracy: 0.7970
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6270 - accuracy: 0.7947 - val_loss: 0.6243 - val_accuracy: 0.7989
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 313/500
235/235 [==============================] - 2s 9ms/step - loss: 0.6228 - accuracy: 0.7965 - val_loss: 0.6210 - val_accuracy: 0.8001
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6192 - accuracy: 0.7976 - val_loss: 0.6183 - val_accuracy: 0.8013
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6160 - accuracy: 0.7988 - val_loss: 0.6159 - val_accuracy: 0.8021
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6133 - accuracy: 0.7999 - val_loss: 0.6139 - val_accuracy: 0.8020
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6108 - accuracy: 0.8009 - val_loss: 0.6120 - val_accuracy: 0.8028
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6086 - accuracy: 0.8015 - val_loss: 0.6104 - val_accuracy: 0.8031
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6066 - accuracy: 0.8017 - val_loss: 0.6089 - val_accuracy: 0.8037
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6047 - accuracy: 0.8023 - val_loss: 0.6075 - val_accuracy: 0.8038
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6030 - accuracy: 0.8027 - val_loss: 0.6062 - val_accuracy: 0.8039
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6015 - accuracy: 0.8032 - val_loss: 0.6051 - val_accuracy: 0.8042
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 0.6000 - accuracy: 0.8038 - val_loss: 0.6039 - val_accuracy: 0.8054
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 324/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5986 - accuracy: 0.8042 - val_loss: 0.6029 - val_accuracy: 0.8057
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5973 - accuracy: 0.8048 - val_loss: 0.6019 - val_accuracy: 0.8064
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5961 - accuracy: 0.8053 - val_loss: 0.6009 - val_accuracy: 0.8063
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5949 - accuracy: 0.8057 - val_loss: 0.6000 - val_accuracy: 0.8071
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5939 - accuracy: 0.8061 - val_loss: 0.5992 - val_accuracy: 0.8074
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5928 - accuracy: 0.8066 - val_loss: 0.5984 - val_accuracy: 0.8082
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5918 - accuracy: 0.8069 - val_loss: 0.5976 - val_accuracy: 0.8088
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5908 - accuracy: 0.8073 - val_loss: 0.5967 - val_accuracy: 0.8093
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 332/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5898 - accuracy: 0.8076 - val_loss: 0.5960 - val_accuracy: 0.8094
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5888 - accuracy: 0.8078 - val_loss: 0.5952 - val_accuracy: 0.8101
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5879 - accuracy: 0.8084 - val_loss: 0.5944 - val_accuracy: 0.8098
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5869 - accuracy: 0.8089 - val_loss: 0.5936 - val_accuracy: 0.8105
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5859 - accuracy: 0.8091 - val_loss: 0.5929 - val_accuracy: 0.8111
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5850 - accuracy: 0.8096 - val_loss: 0.5921 - val_accuracy: 0.8121
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5842 - accuracy: 0.8097 - val_loss: 0.5914 - val_accuracy: 0.8123
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5833 - accuracy: 0.8101 - val_loss: 0.5907 - val_accuracy: 0.8130
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5825 - accuracy: 0.8103 - val_loss: 0.5899 - val_accuracy: 0.8131
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5818 - accuracy: 0.8104 - val_loss: 0.5893 - val_accuracy: 0.8132
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 342/500
235/235 [==============================] - 2s 10ms/step - loss: 0.5811 - accuracy: 0.8105 - val_loss: 0.5886 - val_accuracy: 0.8135
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5804 - accuracy: 0.8110 - val_loss: 0.5880 - val_accuracy: 0.8139
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5798 - accuracy: 0.8112 - val_loss: 0.5874 - val_accuracy: 0.8137
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5792 - accuracy: 0.8114 - val_loss: 0.5868 - val_accuracy: 0.8142
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5786 - accuracy: 0.8115 - val_loss: 0.5863 - val_accuracy: 0.8141
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5781 - accuracy: 0.8119 - val_loss: 0.5858 - val_accuracy: 0.8144
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 348/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5776 - accuracy: 0.8121 - val_loss: 0.5853 - val_accuracy: 0.8141
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5770 - accuracy: 0.8122 - val_loss: 0.5848 - val_accuracy: 0.8140
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5765 - accuracy: 0.8124 - val_loss: 0.5843 - val_accuracy: 0.8145
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9718515289699571
Epoch 351/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8291 - accuracy: 0.4167 - val_loss: 1.5087 - val_accuracy: 0.4377
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 352/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5259 - accuracy: 0.4839 - val_loss: 1.4726 - val_accuracy: 0.4964
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5000 - accuracy: 0.4892 - val_loss: 1.4585 - val_accuracy: 0.4989
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4876 - accuracy: 0.5044 - val_loss: 1.4501 - val_accuracy: 0.4996
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4798 - accuracy: 0.5284 - val_loss: 1.4440 - val_accuracy: 0.5456
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4735 - accuracy: 0.5385 - val_loss: 1.4378 - val_accuracy: 0.5486
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4652 - accuracy: 0.5419 - val_loss: 1.4260 - val_accuracy: 0.5546
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4508 - accuracy: 0.5497 - val_loss: 1.4143 - val_accuracy: 0.5588
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4414 - accuracy: 0.5541 - val_loss: 1.4076 - val_accuracy: 0.5607
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4350 - accuracy: 0.5570 - val_loss: 1.4023 - val_accuracy: 0.5625
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4297 - accuracy: 0.5595 - val_loss: 1.3977 - val_accuracy: 0.5641
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4249 - accuracy: 0.5612 - val_loss: 1.3934 - val_accuracy: 0.5656
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4203 - accuracy: 0.5628 - val_loss: 1.3892 - val_accuracy: 0.5682
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4156 - accuracy: 0.5646 - val_loss: 1.3849 - val_accuracy: 0.5698
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4109 - accuracy: 0.5663 - val_loss: 1.3810 - val_accuracy: 0.5718
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4072 - accuracy: 0.5681 - val_loss: 1.3783 - val_accuracy: 0.5730
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4044 - accuracy: 0.5689 - val_loss: 1.3763 - val_accuracy: 0.5737
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4023 - accuracy: 0.5690 - val_loss: 1.3747 - val_accuracy: 0.5738
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4006 - accuracy: 0.5695 - val_loss: 1.3733 - val_accuracy: 0.5741
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3990 - accuracy: 0.5699 - val_loss: 1.3722 - val_accuracy: 0.5753
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3976 - accuracy: 0.5705 - val_loss: 1.3711 - val_accuracy: 0.5761
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3963 - accuracy: 0.5709 - val_loss: 1.3700 - val_accuracy: 0.5769
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3951 - accuracy: 0.5714 - val_loss: 1.3691 - val_accuracy: 0.5772
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3941 - accuracy: 0.5717 - val_loss: 1.3681 - val_accuracy: 0.5773
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3930 - accuracy: 0.5718 - val_loss: 1.3673 - val_accuracy: 0.5776
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3921 - accuracy: 0.5725 - val_loss: 1.3665 - val_accuracy: 0.5780
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3911 - accuracy: 0.5727 - val_loss: 1.3657 - val_accuracy: 0.5774
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3902 - accuracy: 0.5730 - val_loss: 1.3649 - val_accuracy: 0.5775
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3894 - accuracy: 0.5732 - val_loss: 1.3642 - val_accuracy: 0.5777
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3886 - accuracy: 0.5732 - val_loss: 1.3634 - val_accuracy: 0.5779
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3878 - accuracy: 0.5737 - val_loss: 1.3628 - val_accuracy: 0.5778
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3871 - accuracy: 0.5740 - val_loss: 1.3621 - val_accuracy: 0.5785
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3863 - accuracy: 0.5742 - val_loss: 1.3615 - val_accuracy: 0.5782
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3856 - accuracy: 0.5739 - val_loss: 1.3609 - val_accuracy: 0.5785
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3850 - accuracy: 0.5742 - val_loss: 1.3604 - val_accuracy: 0.5791
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 386/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3843 - accuracy: 0.5746 - val_loss: 1.3598 - val_accuracy: 0.5793
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3837 - accuracy: 0.5744 - val_loss: 1.3593 - val_accuracy: 0.5800
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3832 - accuracy: 0.5742 - val_loss: 1.3587 - val_accuracy: 0.5800
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3826 - accuracy: 0.5741 - val_loss: 1.3582 - val_accuracy: 0.5790
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3821 - accuracy: 0.5736 - val_loss: 1.3577 - val_accuracy: 0.5764
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 391/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3815 - accuracy: 0.5733 - val_loss: 1.3573 - val_accuracy: 0.5762
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3811 - accuracy: 0.5735 - val_loss: 1.3568 - val_accuracy: 0.5765
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3806 - accuracy: 0.5733 - val_loss: 1.3564 - val_accuracy: 0.5768
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3802 - accuracy: 0.5731 - val_loss: 1.3560 - val_accuracy: 0.5768
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3797 - accuracy: 0.5730 - val_loss: 1.3556 - val_accuracy: 0.5772
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3793 - accuracy: 0.5727 - val_loss: 1.3552 - val_accuracy: 0.5778
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 397/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3788 - accuracy: 0.5727 - val_loss: 1.3547 - val_accuracy: 0.5780
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9846097103004292
Epoch 398/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3784 - accuracy: 0.5727 - val_loss: 1.3542 - val_accuracy: 0.5782
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3780 - accuracy: 0.5729 - val_loss: 1.3538 - val_accuracy: 0.5788
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3775 - accuracy: 0.5730 - val_loss: 1.3533 - val_accuracy: 0.5795
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 401/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8041 - accuracy: 0.3775 - val_loss: 1.7264 - val_accuracy: 0.3880
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 402/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7310 - accuracy: 0.3974 - val_loss: 1.7223 - val_accuracy: 0.3890
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7278 - accuracy: 0.3990 - val_loss: 1.7205 - val_accuracy: 0.3885
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 404/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7258 - accuracy: 0.3988 - val_loss: 1.7192 - val_accuracy: 0.3881
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 405/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7244 - accuracy: 0.3995 - val_loss: 1.7182 - val_accuracy: 0.3880
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7231 - accuracy: 0.3987 - val_loss: 1.7174 - val_accuracy: 0.3886
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7222 - accuracy: 0.3996 - val_loss: 1.7167 - val_accuracy: 0.3885
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7214 - accuracy: 0.3993 - val_loss: 1.7162 - val_accuracy: 0.3887
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7209 - accuracy: 0.3994 - val_loss: 1.7158 - val_accuracy: 0.3888
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7204 - accuracy: 0.4005 - val_loss: 1.7155 - val_accuracy: 0.3887
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 411/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7201 - accuracy: 0.4003 - val_loss: 1.7152 - val_accuracy: 0.3892
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7198 - accuracy: 0.4006 - val_loss: 1.7149 - val_accuracy: 0.3892
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7195 - accuracy: 0.4006 - val_loss: 1.7147 - val_accuracy: 0.3890
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7193 - accuracy: 0.4002 - val_loss: 1.7145 - val_accuracy: 0.3890
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7191 - accuracy: 0.4007 - val_loss: 1.7142 - val_accuracy: 0.3893
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7189 - accuracy: 0.4014 - val_loss: 1.7141 - val_accuracy: 0.3896
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7187 - accuracy: 0.4007 - val_loss: 1.7139 - val_accuracy: 0.3896
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7185 - accuracy: 0.4004 - val_loss: 1.7138 - val_accuracy: 0.3899
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7184 - accuracy: 0.4012 - val_loss: 1.7136 - val_accuracy: 0.3898
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7182 - accuracy: 0.4010 - val_loss: 1.7135 - val_accuracy: 0.3897
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7181 - accuracy: 0.4018 - val_loss: 1.7133 - val_accuracy: 0.3896
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7180 - accuracy: 0.4011 - val_loss: 1.7132 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7178 - accuracy: 0.4015 - val_loss: 1.7131 - val_accuracy: 0.3903
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 424/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7177 - accuracy: 0.4005 - val_loss: 1.7130 - val_accuracy: 0.3903
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7176 - accuracy: 0.4011 - val_loss: 1.7129 - val_accuracy: 0.3903
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7175 - accuracy: 0.4018 - val_loss: 1.7128 - val_accuracy: 0.3903
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7173 - accuracy: 0.4016 - val_loss: 1.7127 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7172 - accuracy: 0.4018 - val_loss: 1.7126 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 429/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7171 - accuracy: 0.4016 - val_loss: 1.7125 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7170 - accuracy: 0.4022 - val_loss: 1.7124 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7169 - accuracy: 0.4019 - val_loss: 1.7124 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7168 - accuracy: 0.4017 - val_loss: 1.7123 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 433/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7167 - accuracy: 0.4017 - val_loss: 1.7122 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7166 - accuracy: 0.4023 - val_loss: 1.7121 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 435/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7165 - accuracy: 0.4026 - val_loss: 1.7121 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 436/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7164 - accuracy: 0.4025 - val_loss: 1.7120 - val_accuracy: 0.3899
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 437/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7162 - accuracy: 0.4023 - val_loss: 1.7119 - val_accuracy: 0.3899
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7162 - accuracy: 0.4027 - val_loss: 1.7119 - val_accuracy: 0.3899
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7161 - accuracy: 0.4028 - val_loss: 1.7118 - val_accuracy: 0.3898
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7160 - accuracy: 0.4026 - val_loss: 1.7117 - val_accuracy: 0.3898
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7159 - accuracy: 0.4023 - val_loss: 1.7117 - val_accuracy: 0.3900
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7158 - accuracy: 0.4031 - val_loss: 1.7116 - val_accuracy: 0.3899
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7157 - accuracy: 0.4026 - val_loss: 1.7116 - val_accuracy: 0.3900
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 444/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7157 - accuracy: 0.4031 - val_loss: 1.7115 - val_accuracy: 0.3900
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7156 - accuracy: 0.4028 - val_loss: 1.7114 - val_accuracy: 0.3900
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7155 - accuracy: 0.4028 - val_loss: 1.7113 - val_accuracy: 0.3899
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7154 - accuracy: 0.4029 - val_loss: 1.7113 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7153 - accuracy: 0.4033 - val_loss: 1.7113 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7153 - accuracy: 0.4030 - val_loss: 1.7112 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7152 - accuracy: 0.4036 - val_loss: 1.7111 - val_accuracy: 0.3900
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 451/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7151 - accuracy: 0.4036 - val_loss: 1.7111 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7150 - accuracy: 0.4030 - val_loss: 1.7110 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7149 - accuracy: 0.4035 - val_loss: 1.7110 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7149 - accuracy: 0.4035 - val_loss: 1.7109 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7148 - accuracy: 0.4030 - val_loss: 1.7109 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7147 - accuracy: 0.4037 - val_loss: 1.7108 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7146 - accuracy: 0.4028 - val_loss: 1.7107 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7145 - accuracy: 0.4036 - val_loss: 1.7107 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 459/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7144 - accuracy: 0.4035 - val_loss: 1.7106 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7144 - accuracy: 0.4036 - val_loss: 1.7105 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7143 - accuracy: 0.4040 - val_loss: 1.7105 - val_accuracy: 0.3904
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 462/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7142 - accuracy: 0.4037 - val_loss: 1.7104 - val_accuracy: 0.3903
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 463/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7141 - accuracy: 0.4038 - val_loss: 1.7103 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7140 - accuracy: 0.4041 - val_loss: 1.7102 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7138 - accuracy: 0.4034 - val_loss: 1.7101 - val_accuracy: 0.3903
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7137 - accuracy: 0.4044 - val_loss: 1.7099 - val_accuracy: 0.3903
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7136 - accuracy: 0.4039 - val_loss: 1.7098 - val_accuracy: 0.3900
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7134 - accuracy: 0.4040 - val_loss: 1.7096 - val_accuracy: 0.3902
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7132 - accuracy: 0.4041 - val_loss: 1.7094 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.4038 - val_loss: 1.7092 - val_accuracy: 0.3903
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 471/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7129 - accuracy: 0.4048 - val_loss: 1.7089 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7127 - accuracy: 0.4035 - val_loss: 1.7088 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7126 - accuracy: 0.4042 - val_loss: 1.7086 - val_accuracy: 0.3900
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7124 - accuracy: 0.4049 - val_loss: 1.7085 - val_accuracy: 0.3901
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7123 - accuracy: 0.4042 - val_loss: 1.7084 - val_accuracy: 0.3907
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7122 - accuracy: 0.4047 - val_loss: 1.7083 - val_accuracy: 0.3907
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7122 - accuracy: 0.4055 - val_loss: 1.7082 - val_accuracy: 0.3907
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7121 - accuracy: 0.4056 - val_loss: 1.7081 - val_accuracy: 0.3908
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7120 - accuracy: 0.4053 - val_loss: 1.7080 - val_accuracy: 0.3907
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7119 - accuracy: 0.4053 - val_loss: 1.7079 - val_accuracy: 0.3906
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.4055 - val_loss: 1.7078 - val_accuracy: 0.3909
[ 0.  0. -0. ...  0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.4058 - val_loss: 1.7077 - val_accuracy: 0.3909
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7117 - accuracy: 0.4049 - val_loss: 1.7076 - val_accuracy: 0.3908
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7116 - accuracy: 0.4052 - val_loss: 1.7075 - val_accuracy: 0.3908
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7116 - accuracy: 0.4055 - val_loss: 1.7074 - val_accuracy: 0.3909
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7115 - accuracy: 0.4054 - val_loss: 1.7073 - val_accuracy: 0.3910
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7115 - accuracy: 0.4057 - val_loss: 1.7072 - val_accuracy: 0.3910
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7114 - accuracy: 0.4062 - val_loss: 1.7071 - val_accuracy: 0.3912
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7114 - accuracy: 0.4053 - val_loss: 1.7071 - val_accuracy: 0.3914
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7113 - accuracy: 0.4061 - val_loss: 1.7070 - val_accuracy: 0.3914
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7112 - accuracy: 0.4057 - val_loss: 1.7069 - val_accuracy: 0.3914
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7112 - accuracy: 0.4056 - val_loss: 1.7069 - val_accuracy: 0.3914
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7111 - accuracy: 0.4054 - val_loss: 1.7068 - val_accuracy: 0.3913
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7111 - accuracy: 0.4056 - val_loss: 1.7068 - val_accuracy: 0.3914
[ 0.  0. -0. ...  0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7110 - accuracy: 0.4056 - val_loss: 1.7067 - val_accuracy: 0.3913
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 496/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7110 - accuracy: 0.4065 - val_loss: 1.7067 - val_accuracy: 0.3913
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7109 - accuracy: 0.4061 - val_loss: 1.7066 - val_accuracy: 0.3913
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7109 - accuracy: 0.4054 - val_loss: 1.7066 - val_accuracy: 0.3915
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.4060 - val_loss: 1.7066 - val_accuracy: 0.3915
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.4058 - val_loss: 1.7066 - val_accuracy: 0.3916
[ 0.  0. -0. ...  0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 1/200
235/235 [==============================] - 4s 14ms/step - loss: 2.2210 - accuracy: 0.9245 - val_loss: 1.5419 - val_accuracy: 0.8959
Epoch 2/200
235/235 [==============================] - 3s 13ms/step - loss: 0.4462 - accuracy: 0.9601 - val_loss: 0.4865 - val_accuracy: 0.9515
Epoch 3/200
235/235 [==============================] - 3s 13ms/step - loss: 0.3127 - accuracy: 0.9630 - val_loss: 0.3147 - val_accuracy: 0.9588
Epoch 4/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2773 - accuracy: 0.9666 - val_loss: 0.3279 - val_accuracy: 0.9445
Epoch 5/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2607 - accuracy: 0.9677 - val_loss: 0.3167 - val_accuracy: 0.9451
Epoch 6/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2469 - accuracy: 0.9691 - val_loss: 0.2823 - val_accuracy: 0.9528
Epoch 7/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2348 - accuracy: 0.9700 - val_loss: 0.2940 - val_accuracy: 0.9483
Epoch 8/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2305 - accuracy: 0.9706 - val_loss: 0.2694 - val_accuracy: 0.9551
Epoch 9/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2201 - accuracy: 0.9717 - val_loss: 0.2682 - val_accuracy: 0.9550
Epoch 10/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2160 - accuracy: 0.9708 - val_loss: 0.3005 - val_accuracy: 0.9433
Epoch 11/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2135 - accuracy: 0.9709 - val_loss: 0.2479 - val_accuracy: 0.9599
Epoch 12/200
235/235 [==============================] - 3s 13ms/step - loss: 0.2058 - accuracy: 0.9725 - val_loss: 0.2911 - val_accuracy: 0.9425
Epoch 13/200
235/235 [==============================] - 3s 14ms/step - loss: 0.2042 - accuracy: 0.9727 - val_loss: 0.2796 - val_accuracy: 0.9451
Epoch 14/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1971 - accuracy: 0.9724 - val_loss: 0.2358 - val_accuracy: 0.9592
Epoch 15/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1965 - accuracy: 0.9727 - val_loss: 0.2443 - val_accuracy: 0.9591
Epoch 16/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1983 - accuracy: 0.9719 - val_loss: 0.2603 - val_accuracy: 0.9516
Epoch 17/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1946 - accuracy: 0.9721 - val_loss: 0.2690 - val_accuracy: 0.9488
Epoch 18/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1879 - accuracy: 0.9737 - val_loss: 0.2379 - val_accuracy: 0.9562
Epoch 19/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1877 - accuracy: 0.9726 - val_loss: 0.2539 - val_accuracy: 0.9522
Epoch 20/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1844 - accuracy: 0.9738 - val_loss: 0.2654 - val_accuracy: 0.9481
Epoch 21/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1796 - accuracy: 0.9745 - val_loss: 0.2283 - val_accuracy: 0.9585
Epoch 22/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1822 - accuracy: 0.9731 - val_loss: 0.2313 - val_accuracy: 0.9578
Epoch 23/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1817 - accuracy: 0.9736 - val_loss: 0.2639 - val_accuracy: 0.9468
Epoch 24/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1767 - accuracy: 0.9744 - val_loss: 0.2216 - val_accuracy: 0.9627
Epoch 25/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1750 - accuracy: 0.9743 - val_loss: 0.2460 - val_accuracy: 0.9533
Epoch 26/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1787 - accuracy: 0.9740 - val_loss: 0.2144 - val_accuracy: 0.9627
Epoch 27/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1741 - accuracy: 0.9747 - val_loss: 0.2404 - val_accuracy: 0.9560
Epoch 28/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1725 - accuracy: 0.9746 - val_loss: 0.2374 - val_accuracy: 0.9547
Epoch 29/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1737 - accuracy: 0.9741 - val_loss: 0.2183 - val_accuracy: 0.9608
Epoch 30/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1701 - accuracy: 0.9751 - val_loss: 0.2454 - val_accuracy: 0.9509
Epoch 31/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1695 - accuracy: 0.9747 - val_loss: 0.2287 - val_accuracy: 0.9568
Epoch 32/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1691 - accuracy: 0.9743 - val_loss: 0.2136 - val_accuracy: 0.9627
Epoch 33/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1668 - accuracy: 0.9753 - val_loss: 0.2550 - val_accuracy: 0.9460
Epoch 34/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1707 - accuracy: 0.9745 - val_loss: 0.2225 - val_accuracy: 0.9581
Epoch 35/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1640 - accuracy: 0.9755 - val_loss: 0.2136 - val_accuracy: 0.9614
Epoch 36/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1656 - accuracy: 0.9751 - val_loss: 0.2163 - val_accuracy: 0.9605
Epoch 37/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1674 - accuracy: 0.9746 - val_loss: 0.1981 - val_accuracy: 0.9660
Epoch 38/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1676 - accuracy: 0.9745 - val_loss: 0.2280 - val_accuracy: 0.9579
Epoch 39/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1648 - accuracy: 0.9750 - val_loss: 0.2151 - val_accuracy: 0.9612
Epoch 40/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1603 - accuracy: 0.9761 - val_loss: 0.2118 - val_accuracy: 0.9609
Epoch 41/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1634 - accuracy: 0.9753 - val_loss: 0.2301 - val_accuracy: 0.9584
Epoch 42/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1640 - accuracy: 0.9760 - val_loss: 0.2093 - val_accuracy: 0.9612
Epoch 43/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1615 - accuracy: 0.9759 - val_loss: 0.2037 - val_accuracy: 0.9631
Epoch 44/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1604 - accuracy: 0.9756 - val_loss: 0.2205 - val_accuracy: 0.9599
Epoch 45/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1606 - accuracy: 0.9755 - val_loss: 0.2291 - val_accuracy: 0.9556
Epoch 46/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1607 - accuracy: 0.9762 - val_loss: 0.2714 - val_accuracy: 0.9395
Epoch 47/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1610 - accuracy: 0.9760 - val_loss: 0.2034 - val_accuracy: 0.9630
Epoch 48/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1557 - accuracy: 0.9762 - val_loss: 0.2247 - val_accuracy: 0.9576
Epoch 49/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1573 - accuracy: 0.9765 - val_loss: 0.2052 - val_accuracy: 0.9614
Epoch 50/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1570 - accuracy: 0.9764 - val_loss: 0.2251 - val_accuracy: 0.9572
Epoch 51/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1596 - accuracy: 0.9752 - val_loss: 0.2193 - val_accuracy: 0.9584
Epoch 52/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1589 - accuracy: 0.9761 - val_loss: 0.2396 - val_accuracy: 0.9515
Epoch 53/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1562 - accuracy: 0.9763 - val_loss: 0.2198 - val_accuracy: 0.9586
Epoch 54/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1577 - accuracy: 0.9754 - val_loss: 0.2284 - val_accuracy: 0.9548
Epoch 55/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1568 - accuracy: 0.9763 - val_loss: 0.2207 - val_accuracy: 0.9562
Epoch 56/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1590 - accuracy: 0.9758 - val_loss: 0.2193 - val_accuracy: 0.9577
Epoch 57/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1559 - accuracy: 0.9760 - val_loss: 0.2135 - val_accuracy: 0.9591
Epoch 58/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1524 - accuracy: 0.9775 - val_loss: 0.2061 - val_accuracy: 0.9613
Epoch 59/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1560 - accuracy: 0.9762 - val_loss: 0.2209 - val_accuracy: 0.9570
Epoch 60/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1521 - accuracy: 0.9767 - val_loss: 0.2308 - val_accuracy: 0.9544
Epoch 61/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1553 - accuracy: 0.9765 - val_loss: 0.2538 - val_accuracy: 0.9447
Epoch 62/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1528 - accuracy: 0.9769 - val_loss: 0.2233 - val_accuracy: 0.9548
Epoch 63/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1543 - accuracy: 0.9765 - val_loss: 0.1979 - val_accuracy: 0.9643
Epoch 64/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1511 - accuracy: 0.9780 - val_loss: 0.2314 - val_accuracy: 0.9539
Epoch 65/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1544 - accuracy: 0.9758 - val_loss: 0.2437 - val_accuracy: 0.9513
Epoch 66/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1494 - accuracy: 0.9782 - val_loss: 0.2271 - val_accuracy: 0.9548
Epoch 67/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1545 - accuracy: 0.9763 - val_loss: 0.2289 - val_accuracy: 0.9523
Epoch 68/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1537 - accuracy: 0.9758 - val_loss: 0.2406 - val_accuracy: 0.9509
Epoch 69/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1536 - accuracy: 0.9772 - val_loss: 0.2283 - val_accuracy: 0.9545
Epoch 70/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1510 - accuracy: 0.9775 - val_loss: 0.1912 - val_accuracy: 0.9638
Epoch 71/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1496 - accuracy: 0.9774 - val_loss: 0.2245 - val_accuracy: 0.9530
Epoch 72/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1484 - accuracy: 0.9774 - val_loss: 0.2160 - val_accuracy: 0.9599
Epoch 73/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1512 - accuracy: 0.9773 - val_loss: 0.2093 - val_accuracy: 0.9571
Epoch 74/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1514 - accuracy: 0.9772 - val_loss: 0.2104 - val_accuracy: 0.9587
Epoch 75/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1516 - accuracy: 0.9772 - val_loss: 0.2338 - val_accuracy: 0.9515
Epoch 76/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1527 - accuracy: 0.9771 - val_loss: 0.2060 - val_accuracy: 0.9603
Epoch 77/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1483 - accuracy: 0.9782 - val_loss: 0.2436 - val_accuracy: 0.9474
Epoch 78/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1539 - accuracy: 0.9764 - val_loss: 0.2044 - val_accuracy: 0.9634
Epoch 79/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1493 - accuracy: 0.9781 - val_loss: 0.2612 - val_accuracy: 0.9450
Epoch 80/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1534 - accuracy: 0.9773 - val_loss: 0.2063 - val_accuracy: 0.9601
Epoch 81/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1482 - accuracy: 0.9783 - val_loss: 0.2267 - val_accuracy: 0.9546
Epoch 82/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1472 - accuracy: 0.9777 - val_loss: 0.2476 - val_accuracy: 0.9477
Epoch 83/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1496 - accuracy: 0.9778 - val_loss: 0.2131 - val_accuracy: 0.9576
Epoch 84/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1485 - accuracy: 0.9777 - val_loss: 0.2229 - val_accuracy: 0.9563
Epoch 85/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1496 - accuracy: 0.9776 - val_loss: 0.2129 - val_accuracy: 0.9592
Epoch 86/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1491 - accuracy: 0.9774 - val_loss: 0.2350 - val_accuracy: 0.9534
Epoch 87/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1487 - accuracy: 0.9776 - val_loss: 0.2401 - val_accuracy: 0.9518
Epoch 88/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1475 - accuracy: 0.9776 - val_loss: 0.2088 - val_accuracy: 0.9611
Epoch 89/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1503 - accuracy: 0.9771 - val_loss: 0.2321 - val_accuracy: 0.9535
Epoch 90/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1515 - accuracy: 0.9764 - val_loss: 0.2070 - val_accuracy: 0.9625
Epoch 91/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1464 - accuracy: 0.9784 - val_loss: 0.2114 - val_accuracy: 0.9612
Epoch 92/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1449 - accuracy: 0.9782 - val_loss: 0.2460 - val_accuracy: 0.9481
Epoch 93/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1501 - accuracy: 0.9774 - val_loss: 0.2111 - val_accuracy: 0.9611
Epoch 94/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1465 - accuracy: 0.9780 - val_loss: 0.2113 - val_accuracy: 0.9594
Epoch 95/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1436 - accuracy: 0.9790 - val_loss: 0.2027 - val_accuracy: 0.9618
Epoch 96/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1475 - accuracy: 0.9777 - val_loss: 0.2012 - val_accuracy: 0.9619
Epoch 97/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1483 - accuracy: 0.9774 - val_loss: 0.2003 - val_accuracy: 0.9627
Epoch 98/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1470 - accuracy: 0.9781 - val_loss: 0.2171 - val_accuracy: 0.9578
Epoch 99/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1444 - accuracy: 0.9783 - val_loss: 0.2237 - val_accuracy: 0.9573
Epoch 100/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1432 - accuracy: 0.9797 - val_loss: 0.2283 - val_accuracy: 0.9535
Epoch 101/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1477 - accuracy: 0.9774 - val_loss: 0.2588 - val_accuracy: 0.9430
Epoch 102/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1467 - accuracy: 0.9775 - val_loss: 0.2297 - val_accuracy: 0.9544
Epoch 103/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1483 - accuracy: 0.9773 - val_loss: 0.2353 - val_accuracy: 0.9520
Epoch 104/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1488 - accuracy: 0.9771 - val_loss: 0.2555 - val_accuracy: 0.9479
Epoch 105/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1479 - accuracy: 0.9776 - val_loss: 0.2328 - val_accuracy: 0.9524
Epoch 106/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1466 - accuracy: 0.9778 - val_loss: 0.2349 - val_accuracy: 0.9520
Epoch 107/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1480 - accuracy: 0.9771 - val_loss: 0.2884 - val_accuracy: 0.9362
Epoch 108/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1492 - accuracy: 0.9772 - val_loss: 0.2068 - val_accuracy: 0.9602
Epoch 109/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1439 - accuracy: 0.9793 - val_loss: 0.2576 - val_accuracy: 0.9432
Epoch 110/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1464 - accuracy: 0.9784 - val_loss: 0.2048 - val_accuracy: 0.9605
Epoch 111/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1446 - accuracy: 0.9781 - val_loss: 0.2071 - val_accuracy: 0.9596
Epoch 112/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1409 - accuracy: 0.9786 - val_loss: 0.2263 - val_accuracy: 0.9543
Epoch 113/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1495 - accuracy: 0.9776 - val_loss: 0.2206 - val_accuracy: 0.9576
Epoch 114/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1433 - accuracy: 0.9786 - val_loss: 0.2057 - val_accuracy: 0.9619
Epoch 115/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1435 - accuracy: 0.9787 - val_loss: 0.2237 - val_accuracy: 0.9534
Epoch 116/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1403 - accuracy: 0.9792 - val_loss: 0.2151 - val_accuracy: 0.9589
Epoch 117/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1448 - accuracy: 0.9781 - val_loss: 0.2060 - val_accuracy: 0.9606
Epoch 118/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1465 - accuracy: 0.9784 - val_loss: 0.2463 - val_accuracy: 0.9502
Epoch 119/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1454 - accuracy: 0.9780 - val_loss: 0.2300 - val_accuracy: 0.9519
Epoch 120/200
235/235 [==============================] - 3s 12ms/step - loss: 0.1462 - accuracy: 0.9782 - val_loss: 0.2011 - val_accuracy: 0.9648
Epoch 121/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1434 - accuracy: 0.9791 - val_loss: 0.2214 - val_accuracy: 0.9579
Epoch 122/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1444 - accuracy: 0.9785 - val_loss: 0.1909 - val_accuracy: 0.9653
Epoch 123/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1469 - accuracy: 0.9778 - val_loss: 0.2274 - val_accuracy: 0.9535
Epoch 124/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1426 - accuracy: 0.9799 - val_loss: 0.2514 - val_accuracy: 0.9486
Epoch 125/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1444 - accuracy: 0.9781 - val_loss: 0.2224 - val_accuracy: 0.9575
Epoch 126/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1412 - accuracy: 0.9797 - val_loss: 0.2179 - val_accuracy: 0.9591
Epoch 127/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9790 - val_loss: 0.2824 - val_accuracy: 0.9378
Epoch 128/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1469 - accuracy: 0.9785 - val_loss: 0.2513 - val_accuracy: 0.9445
Epoch 129/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1443 - accuracy: 0.9784 - val_loss: 0.2546 - val_accuracy: 0.9450
Epoch 130/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.2515 - val_accuracy: 0.9478
Epoch 131/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1437 - accuracy: 0.9792 - val_loss: 0.2057 - val_accuracy: 0.9614
Epoch 132/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1408 - accuracy: 0.9795 - val_loss: 0.1892 - val_accuracy: 0.9660
Epoch 133/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1438 - accuracy: 0.9783 - val_loss: 0.2172 - val_accuracy: 0.9589
Epoch 134/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1486 - accuracy: 0.9773 - val_loss: 0.1984 - val_accuracy: 0.9615
Epoch 135/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9789 - val_loss: 0.2004 - val_accuracy: 0.9638
Epoch 136/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1457 - accuracy: 0.9780 - val_loss: 0.2167 - val_accuracy: 0.9560
Epoch 137/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1420 - accuracy: 0.9787 - val_loss: 0.2740 - val_accuracy: 0.9400
Epoch 138/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1411 - accuracy: 0.9790 - val_loss: 0.2354 - val_accuracy: 0.9523
Epoch 139/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1423 - accuracy: 0.9786 - val_loss: 0.2705 - val_accuracy: 0.9420
Epoch 140/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1427 - accuracy: 0.9789 - val_loss: 0.2017 - val_accuracy: 0.9639
Epoch 141/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1399 - accuracy: 0.9794 - val_loss: 0.1998 - val_accuracy: 0.9616
Epoch 142/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1435 - accuracy: 0.9782 - val_loss: 0.2286 - val_accuracy: 0.9535
Epoch 143/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1445 - accuracy: 0.9778 - val_loss: 0.2072 - val_accuracy: 0.9616
Epoch 144/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1419 - accuracy: 0.9788 - val_loss: 0.1928 - val_accuracy: 0.9642
Epoch 145/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1420 - accuracy: 0.9789 - val_loss: 0.2126 - val_accuracy: 0.9592
Epoch 146/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1423 - accuracy: 0.9789 - val_loss: 0.2234 - val_accuracy: 0.9574
Epoch 147/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1418 - accuracy: 0.9790 - val_loss: 0.2193 - val_accuracy: 0.9566
Epoch 148/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1432 - accuracy: 0.9784 - val_loss: 0.2026 - val_accuracy: 0.9630
Epoch 149/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1412 - accuracy: 0.9792 - val_loss: 0.2450 - val_accuracy: 0.9504
Epoch 150/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.2221 - val_accuracy: 0.9550
Epoch 151/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1415 - accuracy: 0.9790 - val_loss: 0.2343 - val_accuracy: 0.9529
Epoch 152/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9789 - val_loss: 0.2156 - val_accuracy: 0.9556
Epoch 153/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2119 - val_accuracy: 0.9576
Epoch 154/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9782 - val_loss: 0.2032 - val_accuracy: 0.9611
Epoch 155/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1403 - accuracy: 0.9791 - val_loss: 0.2076 - val_accuracy: 0.9598
Epoch 156/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1397 - accuracy: 0.9797 - val_loss: 0.2013 - val_accuracy: 0.9624
Epoch 157/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1407 - accuracy: 0.9790 - val_loss: 0.2254 - val_accuracy: 0.9545
Epoch 158/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9784 - val_loss: 0.2260 - val_accuracy: 0.9551
Epoch 159/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9790 - val_loss: 0.1849 - val_accuracy: 0.9671
Epoch 160/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1436 - accuracy: 0.9782 - val_loss: 0.2214 - val_accuracy: 0.9581
Epoch 161/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1474 - accuracy: 0.9781 - val_loss: 0.2178 - val_accuracy: 0.9583
Epoch 162/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1389 - accuracy: 0.9791 - val_loss: 0.2128 - val_accuracy: 0.9574
Epoch 163/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1422 - accuracy: 0.9786 - val_loss: 0.1974 - val_accuracy: 0.9616
Epoch 164/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1388 - accuracy: 0.9793 - val_loss: 0.2091 - val_accuracy: 0.9589
Epoch 165/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1437 - accuracy: 0.9780 - val_loss: 0.2026 - val_accuracy: 0.9633
Epoch 166/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9783 - val_loss: 0.1892 - val_accuracy: 0.9644
Epoch 167/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1387 - accuracy: 0.9790 - val_loss: 0.2008 - val_accuracy: 0.9601
Epoch 168/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1421 - accuracy: 0.9786 - val_loss: 0.2483 - val_accuracy: 0.9493
Epoch 169/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9790 - val_loss: 0.2105 - val_accuracy: 0.9591
Epoch 170/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9797 - val_loss: 0.2180 - val_accuracy: 0.9575
Epoch 171/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1401 - accuracy: 0.9790 - val_loss: 0.2305 - val_accuracy: 0.9543
Epoch 172/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1398 - accuracy: 0.9783 - val_loss: 0.2348 - val_accuracy: 0.9571
Epoch 173/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9791 - val_loss: 0.2252 - val_accuracy: 0.9518
Epoch 174/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9787 - val_loss: 0.2253 - val_accuracy: 0.9548
Epoch 175/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1415 - accuracy: 0.9790 - val_loss: 0.2382 - val_accuracy: 0.9509
Epoch 176/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1421 - accuracy: 0.9789 - val_loss: 0.2108 - val_accuracy: 0.9605
Epoch 177/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1377 - accuracy: 0.9797 - val_loss: 0.2197 - val_accuracy: 0.9570
Epoch 178/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1377 - accuracy: 0.9796 - val_loss: 0.2128 - val_accuracy: 0.9588
Epoch 179/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9793 - val_loss: 0.2187 - val_accuracy: 0.9575
Epoch 180/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1411 - accuracy: 0.9787 - val_loss: 0.2230 - val_accuracy: 0.9559
Epoch 181/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1422 - accuracy: 0.9783 - val_loss: 0.1977 - val_accuracy: 0.9626
Epoch 182/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1430 - accuracy: 0.9786 - val_loss: 0.1993 - val_accuracy: 0.9618
Epoch 183/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9798 - val_loss: 0.2013 - val_accuracy: 0.9615
Epoch 184/200
235/235 [==============================] - 3s 15ms/step - loss: 0.1384 - accuracy: 0.9794 - val_loss: 0.2141 - val_accuracy: 0.9599
Epoch 185/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.2185 - val_accuracy: 0.9582
Epoch 186/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1372 - accuracy: 0.9801 - val_loss: 0.2047 - val_accuracy: 0.9593
Epoch 187/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1393 - accuracy: 0.9789 - val_loss: 0.1874 - val_accuracy: 0.9672
Epoch 188/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1392 - accuracy: 0.9786 - val_loss: 0.2088 - val_accuracy: 0.9597
Epoch 189/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1427 - accuracy: 0.9787 - val_loss: 0.2622 - val_accuracy: 0.9447
Epoch 190/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1378 - accuracy: 0.9803 - val_loss: 0.2446 - val_accuracy: 0.9496
Epoch 191/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1457 - accuracy: 0.9776 - val_loss: 0.2227 - val_accuracy: 0.9574
Epoch 192/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9802 - val_loss: 0.2430 - val_accuracy: 0.9524
Epoch 193/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9789 - val_loss: 0.2285 - val_accuracy: 0.9548
Epoch 194/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1379 - accuracy: 0.9796 - val_loss: 0.2298 - val_accuracy: 0.9522
Epoch 195/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9797 - val_loss: 0.2863 - val_accuracy: 0.9400
Epoch 196/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1411 - accuracy: 0.9790 - val_loss: 0.2052 - val_accuracy: 0.9617
Epoch 197/200
235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9793 - val_loss: 0.2184 - val_accuracy: 0.9583
Epoch 198/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1416 - accuracy: 0.9790 - val_loss: 0.2374 - val_accuracy: 0.9501
Epoch 199/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1409 - accuracy: 0.9783 - val_loss: 0.2038 - val_accuracy: 0.9607
Epoch 200/200
235/235 [==============================] - 3s 13ms/step - loss: 0.1344 - accuracy: 0.9801 - val_loss: 0.2279 - val_accuracy: 0.9540
Epoch 1/200
235/235 [==============================] - 4s 13ms/step - loss: 0.2495 - accuracy: 0.9262 - val_loss: 0.2079 - val_accuracy: 0.9567
Epoch 2/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0868 - accuracy: 0.9753 - val_loss: 0.1044 - val_accuracy: 0.9674
Epoch 3/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0499 - accuracy: 0.9868 - val_loss: 0.0916 - val_accuracy: 0.9708
Epoch 4/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0306 - accuracy: 0.9921 - val_loss: 0.0944 - val_accuracy: 0.9699
Epoch 5/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0193 - accuracy: 0.9952 - val_loss: 0.1018 - val_accuracy: 0.9706
Epoch 6/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0140 - accuracy: 0.9967 - val_loss: 0.0871 - val_accuracy: 0.9748
Epoch 7/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0116 - accuracy: 0.9970 - val_loss: 0.0915 - val_accuracy: 0.9741
Epoch 8/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9973 - val_loss: 0.0917 - val_accuracy: 0.9750
Epoch 9/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0077 - accuracy: 0.9981 - val_loss: 0.0875 - val_accuracy: 0.9772
Epoch 10/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0077 - accuracy: 0.9978 - val_loss: 0.0893 - val_accuracy: 0.9770
Epoch 11/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0106 - accuracy: 0.9968 - val_loss: 0.1168 - val_accuracy: 0.9712
Epoch 12/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0114 - accuracy: 0.9963 - val_loss: 0.1047 - val_accuracy: 0.9737
Epoch 13/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0116 - accuracy: 0.9961 - val_loss: 0.0844 - val_accuracy: 0.9793
Epoch 14/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.0724 - val_accuracy: 0.9814
Epoch 15/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0031 - accuracy: 0.9994 - val_loss: 0.0749 - val_accuracy: 0.9819
Epoch 16/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.0735 - val_accuracy: 0.9839
Epoch 17/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0803 - val_accuracy: 0.9808
Epoch 18/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.1033 - val_accuracy: 0.9764
Epoch 19/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0088 - accuracy: 0.9971 - val_loss: 0.1169 - val_accuracy: 0.9723
Epoch 20/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0137 - accuracy: 0.9950 - val_loss: 0.0989 - val_accuracy: 0.9776
Epoch 21/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0085 - accuracy: 0.9970 - val_loss: 0.0910 - val_accuracy: 0.9789
Epoch 22/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0055 - accuracy: 0.9981 - val_loss: 0.0794 - val_accuracy: 0.9818
Epoch 23/200
235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0701 - val_accuracy: 0.9825
Epoch 24/200
235/235 [==============================] - 3s 13ms/step - loss: 8.4521e-04 - accuracy: 0.9999 - val_loss: 0.0682 - val_accuracy: 0.9855
Epoch 25/200
235/235 [==============================] - 3s 13ms/step - loss: 8.4762e-04 - accuracy: 0.9998 - val_loss: 0.0719 - val_accuracy: 0.9837
Epoch 26/200
235/235 [==============================] - 3s 13ms/step - loss: 8.8288e-04 - accuracy: 0.9999 - val_loss: 0.0707 - val_accuracy: 0.9844
Epoch 27/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1002 - val_accuracy: 0.9779
Epoch 28/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0088 - accuracy: 0.9969 - val_loss: 0.1079 - val_accuracy: 0.9759
Epoch 29/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0132 - accuracy: 0.9954 - val_loss: 0.0908 - val_accuracy: 0.9802
Epoch 30/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.0784 - val_accuracy: 0.9824
Epoch 31/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0776 - val_accuracy: 0.9839
Epoch 32/200
235/235 [==============================] - 3s 13ms/step - loss: 9.7138e-04 - accuracy: 0.9998 - val_loss: 0.0833 - val_accuracy: 0.9819
Epoch 33/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0676 - val_accuracy: 0.9845
Epoch 34/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0824 - val_accuracy: 0.9811
Epoch 35/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0812 - val_accuracy: 0.9830
Epoch 36/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.1075 - val_accuracy: 0.9761
Epoch 37/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0084 - accuracy: 0.9972 - val_loss: 0.1024 - val_accuracy: 0.9793
Epoch 38/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0058 - accuracy: 0.9982 - val_loss: 0.0927 - val_accuracy: 0.9792
Epoch 39/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.0835 - val_accuracy: 0.9817
Epoch 40/200
235/235 [==============================] - 3s 13ms/step - loss: 7.7238e-04 - accuracy: 0.9998 - val_loss: 0.0767 - val_accuracy: 0.9845
Epoch 41/200
235/235 [==============================] - 3s 13ms/step - loss: 2.9476e-04 - accuracy: 0.9999 - val_loss: 0.0728 - val_accuracy: 0.9853
Epoch 42/200
235/235 [==============================] - 3s 13ms/step - loss: 1.1323e-04 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9856
Epoch 43/200
235/235 [==============================] - 3s 13ms/step - loss: 8.4404e-05 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9857
Epoch 44/200
235/235 [==============================] - 3s 13ms/step - loss: 6.8946e-05 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9854
Epoch 45/200
235/235 [==============================] - 3s 13ms/step - loss: 5.6046e-05 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9856
Epoch 46/200
235/235 [==============================] - 3s 13ms/step - loss: 6.0134e-05 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9851
Epoch 47/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.2116 - val_accuracy: 0.9594
Epoch 48/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0183 - accuracy: 0.9940 - val_loss: 0.1024 - val_accuracy: 0.9795
Epoch 49/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0058 - accuracy: 0.9979 - val_loss: 0.0822 - val_accuracy: 0.9820
Epoch 50/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0729 - val_accuracy: 0.9837
Epoch 51/200
235/235 [==============================] - 3s 13ms/step - loss: 3.9099e-04 - accuracy: 1.0000 - val_loss: 0.0691 - val_accuracy: 0.9841
Epoch 52/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4446e-04 - accuracy: 1.0000 - val_loss: 0.0684 - val_accuracy: 0.9849
Epoch 53/200
235/235 [==============================] - 3s 13ms/step - loss: 1.1316e-04 - accuracy: 1.0000 - val_loss: 0.0686 - val_accuracy: 0.9848
Epoch 54/200
235/235 [==============================] - 3s 13ms/step - loss: 9.1030e-05 - accuracy: 1.0000 - val_loss: 0.0692 - val_accuracy: 0.9849
Epoch 55/200
235/235 [==============================] - 3s 13ms/step - loss: 7.4178e-05 - accuracy: 1.0000 - val_loss: 0.0690 - val_accuracy: 0.9852
Epoch 56/200
235/235 [==============================] - 3s 13ms/step - loss: 5.9493e-05 - accuracy: 1.0000 - val_loss: 0.0695 - val_accuracy: 0.9854
Epoch 57/200
235/235 [==============================] - 3s 13ms/step - loss: 5.0939e-05 - accuracy: 1.0000 - val_loss: 0.0695 - val_accuracy: 0.9855
Epoch 58/200
235/235 [==============================] - 3s 13ms/step - loss: 5.2808e-05 - accuracy: 1.0000 - val_loss: 0.0697 - val_accuracy: 0.9856
Epoch 59/200
235/235 [==============================] - 3s 13ms/step - loss: 4.2974e-05 - accuracy: 1.0000 - val_loss: 0.0698 - val_accuracy: 0.9856
Epoch 60/200
235/235 [==============================] - 3s 13ms/step - loss: 3.6341e-05 - accuracy: 1.0000 - val_loss: 0.0699 - val_accuracy: 0.9856
Epoch 61/200
235/235 [==============================] - 3s 13ms/step - loss: 3.3374e-05 - accuracy: 1.0000 - val_loss: 0.0702 - val_accuracy: 0.9856
Epoch 62/200
235/235 [==============================] - 3s 13ms/step - loss: 2.9721e-05 - accuracy: 1.0000 - val_loss: 0.0703 - val_accuracy: 0.9857
Epoch 63/200
235/235 [==============================] - 3s 13ms/step - loss: 2.4953e-05 - accuracy: 1.0000 - val_loss: 0.0705 - val_accuracy: 0.9854
Epoch 64/200
235/235 [==============================] - 3s 13ms/step - loss: 2.3776e-05 - accuracy: 1.0000 - val_loss: 0.0704 - val_accuracy: 0.9858
Epoch 65/200
235/235 [==============================] - 3s 13ms/step - loss: 2.1326e-05 - accuracy: 1.0000 - val_loss: 0.0711 - val_accuracy: 0.9859
Epoch 66/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0112 - accuracy: 0.9966 - val_loss: 0.2397 - val_accuracy: 0.9558
Epoch 67/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0231 - accuracy: 0.9927 - val_loss: 0.0844 - val_accuracy: 0.9819
Epoch 68/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0783 - val_accuracy: 0.9820
Epoch 69/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0788 - val_accuracy: 0.9839
Epoch 70/200
235/235 [==============================] - 3s 13ms/step - loss: 5.1554e-04 - accuracy: 0.9999 - val_loss: 0.0765 - val_accuracy: 0.9840
Epoch 71/200
235/235 [==============================] - 3s 13ms/step - loss: 4.1956e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9845
Epoch 72/200
235/235 [==============================] - 3s 13ms/step - loss: 1.8338e-04 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9842
Epoch 73/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4750e-04 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9848
Epoch 74/200
235/235 [==============================] - 3s 13ms/step - loss: 1.2276e-04 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9842
Epoch 75/200
235/235 [==============================] - 3s 13ms/step - loss: 9.5179e-05 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9847
Epoch 76/200
235/235 [==============================] - 3s 13ms/step - loss: 7.6908e-05 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9851
Epoch 77/200
235/235 [==============================] - 3s 13ms/step - loss: 6.8500e-05 - accuracy: 1.0000 - val_loss: 0.0742 - val_accuracy: 0.9849
Epoch 78/200
235/235 [==============================] - 3s 13ms/step - loss: 5.9088e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9853
Epoch 79/200
235/235 [==============================] - 3s 13ms/step - loss: 4.9866e-05 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9851
Epoch 80/200
235/235 [==============================] - 3s 13ms/step - loss: 4.3015e-05 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9852
Epoch 81/200
235/235 [==============================] - 3s 13ms/step - loss: 3.7795e-05 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9855
Epoch 82/200
235/235 [==============================] - 3s 13ms/step - loss: 3.6054e-05 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9851
Epoch 83/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1763 - val_accuracy: 0.9680
Epoch 84/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0207 - accuracy: 0.9933 - val_loss: 0.1008 - val_accuracy: 0.9811
Epoch 85/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0046 - accuracy: 0.9985 - val_loss: 0.0779 - val_accuracy: 0.9835
Epoch 86/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.0764 - val_accuracy: 0.9847
Epoch 87/200
235/235 [==============================] - 3s 13ms/step - loss: 8.4310e-04 - accuracy: 0.9998 - val_loss: 0.0794 - val_accuracy: 0.9847
Epoch 88/200
235/235 [==============================] - 3s 13ms/step - loss: 2.7549e-04 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9850
Epoch 89/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4048e-04 - accuracy: 1.0000 - val_loss: 0.0781 - val_accuracy: 0.9854
Epoch 90/200
235/235 [==============================] - 3s 13ms/step - loss: 9.5776e-05 - accuracy: 1.0000 - val_loss: 0.0779 - val_accuracy: 0.9856
Epoch 91/200
235/235 [==============================] - 3s 13ms/step - loss: 9.1525e-05 - accuracy: 1.0000 - val_loss: 0.0791 - val_accuracy: 0.9857
Epoch 92/200
235/235 [==============================] - 3s 13ms/step - loss: 7.5744e-05 - accuracy: 1.0000 - val_loss: 0.0784 - val_accuracy: 0.9854
Epoch 93/200
235/235 [==============================] - 3s 13ms/step - loss: 9.7711e-05 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9856
Epoch 94/200
235/235 [==============================] - 3s 13ms/step - loss: 6.1816e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9857
Epoch 95/200
235/235 [==============================] - 3s 13ms/step - loss: 5.2522e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9857
Epoch 96/200
235/235 [==============================] - 3s 13ms/step - loss: 4.1268e-05 - accuracy: 1.0000 - val_loss: 0.0806 - val_accuracy: 0.9858
Epoch 97/200
235/235 [==============================] - 3s 13ms/step - loss: 4.0060e-05 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9855
Epoch 98/200
235/235 [==============================] - 3s 13ms/step - loss: 3.5270e-05 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9854
Epoch 99/200
235/235 [==============================] - 3s 13ms/step - loss: 3.2687e-05 - accuracy: 1.0000 - val_loss: 0.0826 - val_accuracy: 0.9856
Epoch 100/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0105 - accuracy: 0.9970 - val_loss: 0.1348 - val_accuracy: 0.9776
Epoch 101/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0099 - accuracy: 0.9967 - val_loss: 0.0933 - val_accuracy: 0.9818
Epoch 102/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.0836 - val_accuracy: 0.9852
Epoch 103/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0844 - val_accuracy: 0.9841
Epoch 104/200
235/235 [==============================] - 3s 13ms/step - loss: 4.8876e-04 - accuracy: 0.9999 - val_loss: 0.0888 - val_accuracy: 0.9844
Epoch 105/200
235/235 [==============================] - 3s 13ms/step - loss: 2.4737e-04 - accuracy: 0.9999 - val_loss: 0.0850 - val_accuracy: 0.9851
Epoch 106/200
235/235 [==============================] - 3s 13ms/step - loss: 1.1254e-04 - accuracy: 1.0000 - val_loss: 0.0845 - val_accuracy: 0.9847
Epoch 107/200
235/235 [==============================] - 3s 13ms/step - loss: 8.2048e-05 - accuracy: 1.0000 - val_loss: 0.0834 - val_accuracy: 0.9854
Epoch 108/200
235/235 [==============================] - 3s 13ms/step - loss: 9.6765e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9854
Epoch 109/200
235/235 [==============================] - 3s 13ms/step - loss: 6.9901e-05 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9852
Epoch 110/200
235/235 [==============================] - 3s 13ms/step - loss: 2.3757e-04 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9832
Epoch 111/200
235/235 [==============================] - 3s 13ms/step - loss: 3.8595e-04 - accuracy: 0.9999 - val_loss: 0.0869 - val_accuracy: 0.9851
Epoch 112/200
235/235 [==============================] - 3s 13ms/step - loss: 7.6914e-04 - accuracy: 0.9998 - val_loss: 0.1115 - val_accuracy: 0.9791
Epoch 113/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 0.1308 - val_accuracy: 0.9792
Epoch 114/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0078 - accuracy: 0.9976 - val_loss: 0.1129 - val_accuracy: 0.9784
Epoch 115/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0021 - accuracy: 0.9993 - val_loss: 0.0981 - val_accuracy: 0.9836
Epoch 116/200
235/235 [==============================] - 3s 13ms/step - loss: 4.8056e-04 - accuracy: 0.9999 - val_loss: 0.0938 - val_accuracy: 0.9835
Epoch 117/200
235/235 [==============================] - 3s 13ms/step - loss: 4.0246e-04 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9847
Epoch 118/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0179e-04 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9842
Epoch 119/200
235/235 [==============================] - 3s 13ms/step - loss: 6.1090e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9846
Epoch 120/200
235/235 [==============================] - 3s 13ms/step - loss: 4.8848e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9849
Epoch 121/200
235/235 [==============================] - 3s 13ms/step - loss: 4.5009e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9850
Epoch 122/200
235/235 [==============================] - 3s 13ms/step - loss: 3.6391e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9851
Epoch 123/200
235/235 [==============================] - 3s 13ms/step - loss: 6.0180e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9845
Epoch 124/200
235/235 [==============================] - 3s 13ms/step - loss: 1.7638e-04 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9846
Epoch 125/200
235/235 [==============================] - 3s 14ms/step - loss: 4.3902e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9849
Epoch 126/200
235/235 [==============================] - 3s 13ms/step - loss: 4.7388e-05 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9849
Epoch 127/200
235/235 [==============================] - 3s 13ms/step - loss: 4.2317e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9843
Epoch 128/200
235/235 [==============================] - 3s 13ms/step - loss: 4.0008e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9844
Epoch 129/200
235/235 [==============================] - 3s 13ms/step - loss: 4.5626e-05 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9848
Epoch 130/200
235/235 [==============================] - 3s 13ms/step - loss: 2.7936e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9850
Epoch 131/200
235/235 [==============================] - 3s 13ms/step - loss: 1.8518e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9852
Epoch 132/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4753e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9848
Epoch 133/200
235/235 [==============================] - 3s 13ms/step - loss: 1.2536e-05 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9852
Epoch 134/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4484e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9850
Epoch 135/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0062 - accuracy: 0.9985 - val_loss: 0.2044 - val_accuracy: 0.9660
Epoch 136/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0137 - accuracy: 0.9954 - val_loss: 0.1035 - val_accuracy: 0.9813
Epoch 137/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0033 - accuracy: 0.9989 - val_loss: 0.0918 - val_accuracy: 0.9845
Epoch 138/200
235/235 [==============================] - 3s 13ms/step - loss: 6.6264e-04 - accuracy: 0.9998 - val_loss: 0.0902 - val_accuracy: 0.9848
Epoch 139/200
235/235 [==============================] - 3s 13ms/step - loss: 1.9397e-04 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9847
Epoch 140/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0510e-04 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9847
Epoch 141/200
235/235 [==============================] - 3s 13ms/step - loss: 9.9239e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9851
Epoch 142/200
235/235 [==============================] - 3s 13ms/step - loss: 6.7293e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9855
Epoch 143/200
235/235 [==============================] - 3s 13ms/step - loss: 5.5097e-05 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9858
Epoch 144/200
235/235 [==============================] - 3s 13ms/step - loss: 4.6285e-05 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9856
Epoch 145/200
235/235 [==============================] - 3s 13ms/step - loss: 3.6648e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9856
Epoch 146/200
235/235 [==============================] - 3s 13ms/step - loss: 3.5971e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9856
Epoch 147/200
235/235 [==============================] - 3s 13ms/step - loss: 1.5527e-04 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9847
Epoch 148/200
235/235 [==============================] - 3s 13ms/step - loss: 4.9190e-04 - accuracy: 0.9999 - val_loss: 0.1057 - val_accuracy: 0.9816
Epoch 149/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1167 - val_accuracy: 0.9806
Epoch 150/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.1388 - val_accuracy: 0.9766
Epoch 151/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1012 - val_accuracy: 0.9837
Epoch 152/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1001 - val_accuracy: 0.9839
Epoch 153/200
235/235 [==============================] - 3s 13ms/step - loss: 9.2819e-04 - accuracy: 0.9998 - val_loss: 0.0958 - val_accuracy: 0.9842
Epoch 154/200
235/235 [==============================] - 3s 13ms/step - loss: 4.6041e-04 - accuracy: 0.9998 - val_loss: 0.0938 - val_accuracy: 0.9849
Epoch 155/200
235/235 [==============================] - 3s 13ms/step - loss: 1.7391e-04 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9844
Epoch 156/200
235/235 [==============================] - 3s 13ms/step - loss: 1.1294e-04 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9846
Epoch 157/200
235/235 [==============================] - 3s 13ms/step - loss: 4.4710e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9844
Epoch 158/200
235/235 [==============================] - 3s 14ms/step - loss: 3.8477e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9845
Epoch 159/200
235/235 [==============================] - 3s 14ms/step - loss: 2.5454e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9847
Epoch 160/200
235/235 [==============================] - 3s 13ms/step - loss: 5.5570e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9848
Epoch 161/200
235/235 [==============================] - 3s 13ms/step - loss: 2.6717e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9848
Epoch 162/200
235/235 [==============================] - 3s 13ms/step - loss: 1.6721e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9849
Epoch 163/200
235/235 [==============================] - 3s 13ms/step - loss: 1.5726e-05 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9851
Epoch 164/200
235/235 [==============================] - 3s 13ms/step - loss: 1.4019e-05 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9851
Epoch 165/200
235/235 [==============================] - 3s 13ms/step - loss: 1.2199e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9852
Epoch 166/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0879e-05 - accuracy: 1.0000 - val_loss: 0.0945 - val_accuracy: 0.9847
Epoch 167/200
235/235 [==============================] - 3s 13ms/step - loss: 9.7453e-06 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9849
Epoch 168/200
235/235 [==============================] - 3s 13ms/step - loss: 9.1384e-06 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9852
Epoch 169/200
235/235 [==============================] - 3s 13ms/step - loss: 8.5547e-06 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9852
Epoch 170/200
235/235 [==============================] - 3s 13ms/step - loss: 9.2934e-06 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9854
Epoch 171/200
235/235 [==============================] - 3s 13ms/step - loss: 1.0096e-05 - accuracy: 1.0000 - val_loss: 0.0945 - val_accuracy: 0.9855
Epoch 172/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0074 - accuracy: 0.9979 - val_loss: 0.1340 - val_accuracy: 0.9779
Epoch 173/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0069 - accuracy: 0.9978 - val_loss: 0.0964 - val_accuracy: 0.9823
Epoch 174/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0851 - val_accuracy: 0.9840
Epoch 175/200
235/235 [==============================] - 3s 13ms/step - loss: 3.5873e-04 - accuracy: 0.9999 - val_loss: 0.0860 - val_accuracy: 0.9846
Epoch 176/200
235/235 [==============================] - 3s 11ms/step - loss: 1.9366e-04 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9855
Epoch 177/200
235/235 [==============================] - 3s 13ms/step - loss: 1.7167e-04 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9841
Epoch 178/200
235/235 [==============================] - 3s 13ms/step - loss: 1.1794e-04 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9849
Epoch 179/200
235/235 [==============================] - 3s 13ms/step - loss: 5.8223e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9850
Epoch 180/200
235/235 [==============================] - 3s 13ms/step - loss: 3.9376e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9855
Epoch 181/200
235/235 [==============================] - 3s 13ms/step - loss: 3.0428e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9853
Epoch 182/200
235/235 [==============================] - 3s 13ms/step - loss: 1.1627e-04 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9850
Epoch 183/200
235/235 [==============================] - 3s 13ms/step - loss: 5.1923e-04 - accuracy: 0.9998 - val_loss: 0.0861 - val_accuracy: 0.9852
Epoch 184/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0956 - val_accuracy: 0.9834
Epoch 185/200
235/235 [==============================] - 3s 13ms/step - loss: 4.8009e-04 - accuracy: 0.9998 - val_loss: 0.1044 - val_accuracy: 0.9825
Epoch 186/200
235/235 [==============================] - 3s 13ms/step - loss: 3.4035e-04 - accuracy: 0.9999 - val_loss: 0.0965 - val_accuracy: 0.9843
Epoch 187/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9997 - val_loss: 0.1210 - val_accuracy: 0.9818
Epoch 188/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1170 - val_accuracy: 0.9803
Epoch 189/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.1071 - val_accuracy: 0.9811
Epoch 190/200
235/235 [==============================] - 3s 11ms/step - loss: 8.0930e-04 - accuracy: 0.9998 - val_loss: 0.0968 - val_accuracy: 0.9842
Epoch 191/200
235/235 [==============================] - 3s 13ms/step - loss: 5.8281e-04 - accuracy: 0.9998 - val_loss: 0.0972 - val_accuracy: 0.9835
Epoch 192/200
235/235 [==============================] - 3s 15ms/step - loss: 1.0011e-04 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9852
Epoch 193/200
235/235 [==============================] - 3s 14ms/step - loss: 3.7858e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9854
Epoch 194/200
235/235 [==============================] - 3s 13ms/step - loss: 3.2141e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9853
Epoch 195/200
235/235 [==============================] - 3s 13ms/step - loss: 2.1073e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9855
Epoch 196/200
235/235 [==============================] - 3s 13ms/step - loss: 4.3826e-04 - accuracy: 0.9999 - val_loss: 0.1007 - val_accuracy: 0.9839
Epoch 197/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1066 - val_accuracy: 0.9827
Epoch 198/200
235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.1102 - val_accuracy: 0.9831
Epoch 199/200
235/235 [==============================] - 3s 13ms/step - loss: 8.2480e-04 - accuracy: 0.9997 - val_loss: 0.1104 - val_accuracy: 0.9845
Epoch 200/200
235/235 [==============================] - 3s 13ms/step - loss: 1.3436e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9841
Epoch 1/200
235/235 [==============================] - 2s 8ms/step - loss: 1.5677 - accuracy: 0.8573 - val_loss: 0.9285 - val_accuracy: 0.9024
Epoch 2/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8756 - accuracy: 0.8970 - val_loss: 0.8291 - val_accuracy: 0.9011
Epoch 3/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8345 - accuracy: 0.8976 - val_loss: 0.8145 - val_accuracy: 0.8990
Epoch 4/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8238 - accuracy: 0.8982 - val_loss: 0.8064 - val_accuracy: 0.8990
Epoch 5/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8180 - accuracy: 0.8984 - val_loss: 0.8017 - val_accuracy: 0.8995
Epoch 6/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8151 - accuracy: 0.8983 - val_loss: 0.8003 - val_accuracy: 0.8990
Epoch 7/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8128 - accuracy: 0.8987 - val_loss: 0.7978 - val_accuracy: 0.9000
Epoch 8/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8111 - accuracy: 0.8988 - val_loss: 0.7963 - val_accuracy: 0.8999
Epoch 9/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8104 - accuracy: 0.8988 - val_loss: 0.7949 - val_accuracy: 0.9008
Epoch 10/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8095 - accuracy: 0.8989 - val_loss: 0.7950 - val_accuracy: 0.9003
Epoch 11/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8087 - accuracy: 0.8993 - val_loss: 0.7939 - val_accuracy: 0.9007
Epoch 12/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8083 - accuracy: 0.8993 - val_loss: 0.7942 - val_accuracy: 0.9006
Epoch 13/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8079 - accuracy: 0.8995 - val_loss: 0.7938 - val_accuracy: 0.8999
Epoch 14/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8077 - accuracy: 0.8990 - val_loss: 0.7937 - val_accuracy: 0.9011
Epoch 15/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8070 - accuracy: 0.9000 - val_loss: 0.7927 - val_accuracy: 0.9012
Epoch 16/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8071 - accuracy: 0.8997 - val_loss: 0.7920 - val_accuracy: 0.9016
Epoch 17/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8067 - accuracy: 0.8994 - val_loss: 0.7919 - val_accuracy: 0.9018
Epoch 18/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8062 - accuracy: 0.9001 - val_loss: 0.7919 - val_accuracy: 0.9016
Epoch 19/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8061 - accuracy: 0.8998 - val_loss: 0.7915 - val_accuracy: 0.9018
Epoch 20/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8059 - accuracy: 0.8999 - val_loss: 0.7924 - val_accuracy: 0.9008
Epoch 21/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8059 - accuracy: 0.9001 - val_loss: 0.7909 - val_accuracy: 0.9013
Epoch 22/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8057 - accuracy: 0.9000 - val_loss: 0.7912 - val_accuracy: 0.9017
Epoch 23/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8055 - accuracy: 0.8997 - val_loss: 0.7907 - val_accuracy: 0.9016
Epoch 24/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8056 - accuracy: 0.9002 - val_loss: 0.7910 - val_accuracy: 0.9022
Epoch 25/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8052 - accuracy: 0.9002 - val_loss: 0.7903 - val_accuracy: 0.9023
Epoch 26/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9001 - val_loss: 0.7906 - val_accuracy: 0.9020
Epoch 27/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9002 - val_loss: 0.7906 - val_accuracy: 0.9021
Epoch 28/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.9002 - val_loss: 0.7910 - val_accuracy: 0.9010
Epoch 29/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9008 - val_loss: 0.7892 - val_accuracy: 0.9034
Epoch 30/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.9003 - val_loss: 0.7895 - val_accuracy: 0.9023
Epoch 31/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9006 - val_loss: 0.7904 - val_accuracy: 0.9025
Epoch 32/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9004 - val_loss: 0.7905 - val_accuracy: 0.9021
Epoch 33/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9001 - val_loss: 0.7904 - val_accuracy: 0.9019
Epoch 34/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9004 - val_loss: 0.7894 - val_accuracy: 0.9030
Epoch 35/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9006 - val_loss: 0.7892 - val_accuracy: 0.9028
Epoch 36/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9005 - val_loss: 0.7896 - val_accuracy: 0.9028
Epoch 37/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9007 - val_loss: 0.7893 - val_accuracy: 0.9030
Epoch 38/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9009 - val_loss: 0.7902 - val_accuracy: 0.9022
Epoch 39/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9006 - val_loss: 0.7893 - val_accuracy: 0.9026
Epoch 40/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9006 - val_loss: 0.7894 - val_accuracy: 0.9024
Epoch 41/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9005 - val_loss: 0.7900 - val_accuracy: 0.9019
Epoch 42/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9007 - val_loss: 0.7901 - val_accuracy: 0.9025
Epoch 43/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9006 - val_loss: 0.7896 - val_accuracy: 0.9021
Epoch 44/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9006 - val_loss: 0.7900 - val_accuracy: 0.9026
Epoch 45/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9005 - val_loss: 0.7885 - val_accuracy: 0.9034
Epoch 46/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7890 - val_accuracy: 0.9030
Epoch 47/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9003 - val_loss: 0.7883 - val_accuracy: 0.9025
Epoch 48/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9006 - val_loss: 0.7886 - val_accuracy: 0.9025
Epoch 49/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7904 - val_accuracy: 0.9019
Epoch 50/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7884 - val_accuracy: 0.9032
Epoch 51/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9003 - val_loss: 0.7886 - val_accuracy: 0.9023
Epoch 52/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7891 - val_accuracy: 0.9027
Epoch 53/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9004 - val_loss: 0.7893 - val_accuracy: 0.9031
Epoch 54/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9004 - val_loss: 0.7887 - val_accuracy: 0.9030
Epoch 55/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7888 - val_accuracy: 0.9034
Epoch 56/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7899 - val_accuracy: 0.9021
Epoch 57/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9005 - val_loss: 0.7877 - val_accuracy: 0.9037
Epoch 58/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9006 - val_loss: 0.7881 - val_accuracy: 0.9032
Epoch 59/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7886 - val_accuracy: 0.9032
Epoch 60/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7888 - val_accuracy: 0.9030
Epoch 61/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.9006 - val_loss: 0.7883 - val_accuracy: 0.9034
Epoch 62/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7884 - val_accuracy: 0.9030
Epoch 63/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7881 - val_accuracy: 0.9033
Epoch 64/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9013 - val_loss: 0.7890 - val_accuracy: 0.9031
Epoch 65/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7893 - val_accuracy: 0.9026
Epoch 66/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9027
Epoch 67/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9035
Epoch 68/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7891 - val_accuracy: 0.9030
Epoch 69/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7887 - val_accuracy: 0.9029
Epoch 70/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7888 - val_accuracy: 0.9032
Epoch 71/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7892 - val_accuracy: 0.9035
Epoch 72/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7887 - val_accuracy: 0.9034
Epoch 73/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7882 - val_accuracy: 0.9028
Epoch 74/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7887 - val_accuracy: 0.9030
Epoch 75/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7883 - val_accuracy: 0.9027
Epoch 76/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9030
Epoch 77/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9027
Epoch 78/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9024
Epoch 79/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9032
Epoch 80/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7883 - val_accuracy: 0.9031
Epoch 81/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9020
Epoch 82/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9011 - val_loss: 0.7890 - val_accuracy: 0.9031
Epoch 83/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9005 - val_loss: 0.7889 - val_accuracy: 0.9027
Epoch 84/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7889 - val_accuracy: 0.9029
Epoch 85/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7893 - val_accuracy: 0.9025
Epoch 86/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9027
Epoch 87/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7888 - val_accuracy: 0.9030
Epoch 88/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9003 - val_loss: 0.7888 - val_accuracy: 0.9023
Epoch 89/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9028
Epoch 90/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9029
Epoch 91/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9008 - val_loss: 0.7888 - val_accuracy: 0.9024
Epoch 92/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7890 - val_accuracy: 0.9026
Epoch 93/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7891 - val_accuracy: 0.9025
Epoch 94/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7889 - val_accuracy: 0.9029
Epoch 95/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7883 - val_accuracy: 0.9024
Epoch 96/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9008 - val_loss: 0.7892 - val_accuracy: 0.9032
Epoch 97/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9005 - val_loss: 0.7881 - val_accuracy: 0.9027
Epoch 98/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7882 - val_accuracy: 0.9026
Epoch 99/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9028
Epoch 100/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9005 - val_loss: 0.7893 - val_accuracy: 0.9026
Epoch 101/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7887 - val_accuracy: 0.9025
Epoch 102/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9005 - val_loss: 0.7882 - val_accuracy: 0.9036
Epoch 103/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9029
Epoch 104/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7886 - val_accuracy: 0.9028
Epoch 105/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7896 - val_accuracy: 0.9025
Epoch 106/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7887 - val_accuracy: 0.9024
Epoch 107/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7883 - val_accuracy: 0.9034
Epoch 108/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9006 - val_loss: 0.7890 - val_accuracy: 0.9032
Epoch 109/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7890 - val_accuracy: 0.9024
Epoch 110/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9030
Epoch 111/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9033
Epoch 112/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9025
Epoch 113/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7892 - val_accuracy: 0.9018
Epoch 114/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7884 - val_accuracy: 0.9032
Epoch 115/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9023
Epoch 116/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7887 - val_accuracy: 0.9025
Epoch 117/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7883 - val_accuracy: 0.9028
Epoch 118/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9029
Epoch 119/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9029
Epoch 120/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7887 - val_accuracy: 0.9026
Epoch 121/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9031
Epoch 122/200
235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7889 - val_accuracy: 0.9027
Epoch 123/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7888 - val_accuracy: 0.9025
Epoch 124/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7881 - val_accuracy: 0.9032
Epoch 125/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7898 - val_accuracy: 0.9016
Epoch 126/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7885 - val_accuracy: 0.9031
Epoch 127/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9033
Epoch 128/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7892 - val_accuracy: 0.9029
Epoch 129/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9022
Epoch 130/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7889 - val_accuracy: 0.9031
Epoch 131/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7889 - val_accuracy: 0.9027
Epoch 132/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7882 - val_accuracy: 0.9029
Epoch 133/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9029
Epoch 134/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9024
Epoch 135/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7884 - val_accuracy: 0.9029
Epoch 136/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7893 - val_accuracy: 0.9023
Epoch 137/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7888 - val_accuracy: 0.9024
Epoch 138/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9030
Epoch 139/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9021
Epoch 140/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7899 - val_accuracy: 0.9024
Epoch 141/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9029
Epoch 142/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7888 - val_accuracy: 0.9028
Epoch 143/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7891 - val_accuracy: 0.9019
Epoch 144/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7887 - val_accuracy: 0.9024
Epoch 145/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7892 - val_accuracy: 0.9024
Epoch 146/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9028
Epoch 147/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7889 - val_accuracy: 0.9029
Epoch 148/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7880 - val_accuracy: 0.9027
Epoch 149/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9024
Epoch 150/200
235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7901 - val_accuracy: 0.9017
Epoch 151/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9005 - val_loss: 0.7887 - val_accuracy: 0.9028
Epoch 152/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7889 - val_accuracy: 0.9030
Epoch 153/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9029
Epoch 154/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9026
Epoch 155/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7893 - val_accuracy: 0.9026
Epoch 156/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7891 - val_accuracy: 0.9029
Epoch 157/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7887 - val_accuracy: 0.9032
Epoch 158/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7873 - val_accuracy: 0.9034
Epoch 159/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9028
Epoch 160/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9030
Epoch 161/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8029 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9034
Epoch 162/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7886 - val_accuracy: 0.9027
Epoch 163/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7887 - val_accuracy: 0.9030
Epoch 164/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7884 - val_accuracy: 0.9029
Epoch 165/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7887 - val_accuracy: 0.9028
Epoch 166/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7887 - val_accuracy: 0.9027
Epoch 167/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7889 - val_accuracy: 0.9017
Epoch 168/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9005 - val_loss: 0.7897 - val_accuracy: 0.9024
Epoch 169/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9006 - val_loss: 0.7885 - val_accuracy: 0.9033
Epoch 170/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9032
Epoch 171/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7883 - val_accuracy: 0.9024
Epoch 172/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7880 - val_accuracy: 0.9026
Epoch 173/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9035
Epoch 174/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7889 - val_accuracy: 0.9029
Epoch 175/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9028
Epoch 176/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9029
Epoch 177/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7897 - val_accuracy: 0.9025
Epoch 178/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9025
Epoch 179/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8029 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9024
Epoch 180/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7893 - val_accuracy: 0.9022
Epoch 181/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7892 - val_accuracy: 0.9025
Epoch 182/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9032
Epoch 183/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9006 - val_loss: 0.7881 - val_accuracy: 0.9034
Epoch 184/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9023
Epoch 185/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7886 - val_accuracy: 0.9017
Epoch 186/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9042
Epoch 187/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9032
Epoch 188/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9042
Epoch 189/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9028
Epoch 190/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7889 - val_accuracy: 0.9031
Epoch 191/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9027
Epoch 192/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9031
Epoch 193/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7891 - val_accuracy: 0.9022
Epoch 194/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9029
Epoch 195/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7885 - val_accuracy: 0.9032
Epoch 196/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9036
Epoch 197/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9036
Epoch 198/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9043
Epoch 199/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7878 - val_accuracy: 0.9027
Epoch 200/200
235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9029
Epoch 1/200
235/235 [==============================] - 2s 8ms/step - loss: 0.4638 - accuracy: 0.8712 - val_loss: 0.2498 - val_accuracy: 0.9265
Epoch 2/200
235/235 [==============================] - 2s 8ms/step - loss: 0.2248 - accuracy: 0.9355 - val_loss: 0.1868 - val_accuracy: 0.9447
Epoch 3/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1700 - accuracy: 0.9509 - val_loss: 0.1531 - val_accuracy: 0.9532
Epoch 4/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1361 - accuracy: 0.9603 - val_loss: 0.1330 - val_accuracy: 0.9584
Epoch 5/200
235/235 [==============================] - 2s 8ms/step - loss: 0.1122 - accuracy: 0.9676 - val_loss: 0.1196 - val_accuracy: 0.9624
Epoch 6/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0941 - accuracy: 0.9723 - val_loss: 0.1110 - val_accuracy: 0.9649
Epoch 7/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0801 - accuracy: 0.9768 - val_loss: 0.1045 - val_accuracy: 0.9665
Epoch 8/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0690 - accuracy: 0.9801 - val_loss: 0.0999 - val_accuracy: 0.9679
Epoch 9/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0599 - accuracy: 0.9832 - val_loss: 0.0988 - val_accuracy: 0.9684
Epoch 10/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0522 - accuracy: 0.9856 - val_loss: 0.0983 - val_accuracy: 0.9690
Epoch 11/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0453 - accuracy: 0.9879 - val_loss: 0.0983 - val_accuracy: 0.9693
Epoch 12/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0396 - accuracy: 0.9896 - val_loss: 0.1005 - val_accuracy: 0.9690
Epoch 13/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0346 - accuracy: 0.9911 - val_loss: 0.1027 - val_accuracy: 0.9693
Epoch 14/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0300 - accuracy: 0.9926 - val_loss: 0.1043 - val_accuracy: 0.9690
Epoch 15/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0259 - accuracy: 0.9939 - val_loss: 0.1056 - val_accuracy: 0.9704
Epoch 16/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0225 - accuracy: 0.9949 - val_loss: 0.1075 - val_accuracy: 0.9698
Epoch 17/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0195 - accuracy: 0.9956 - val_loss: 0.1099 - val_accuracy: 0.9702
Epoch 18/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0170 - accuracy: 0.9964 - val_loss: 0.1143 - val_accuracy: 0.9699
Epoch 19/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0146 - accuracy: 0.9971 - val_loss: 0.1145 - val_accuracy: 0.9702
Epoch 20/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0124 - accuracy: 0.9977 - val_loss: 0.1195 - val_accuracy: 0.9691
Epoch 21/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0109 - accuracy: 0.9981 - val_loss: 0.1280 - val_accuracy: 0.9676
Epoch 22/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0095 - accuracy: 0.9984 - val_loss: 0.1367 - val_accuracy: 0.9672
Epoch 23/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9987 - val_loss: 0.1303 - val_accuracy: 0.9692
Epoch 24/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0086 - accuracy: 0.9983 - val_loss: 0.1309 - val_accuracy: 0.9697
Epoch 25/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0093 - accuracy: 0.9976 - val_loss: 0.1278 - val_accuracy: 0.9715
Epoch 26/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.1296 - val_accuracy: 0.9724
Epoch 27/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0089 - accuracy: 0.9975 - val_loss: 0.1314 - val_accuracy: 0.9713
Epoch 28/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0090 - accuracy: 0.9975 - val_loss: 0.1380 - val_accuracy: 0.9726
Epoch 29/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0081 - accuracy: 0.9977 - val_loss: 0.1569 - val_accuracy: 0.9666
Epoch 30/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0063 - accuracy: 0.9983 - val_loss: 0.1258 - val_accuracy: 0.9731
Epoch 31/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0048 - accuracy: 0.9989 - val_loss: 0.1397 - val_accuracy: 0.9710
Epoch 32/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0035 - accuracy: 0.9994 - val_loss: 0.1303 - val_accuracy: 0.9744
Epoch 33/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9994 - val_loss: 0.1317 - val_accuracy: 0.9737
Epoch 34/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 0.9997 - val_loss: 0.1424 - val_accuracy: 0.9721
Epoch 35/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 0.1484 - val_accuracy: 0.9711
Epoch 36/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.1349 - val_accuracy: 0.9754
Epoch 37/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1460 - val_accuracy: 0.9731
Epoch 38/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0075 - accuracy: 0.9974 - val_loss: 0.1567 - val_accuracy: 0.9712
Epoch 39/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0090 - accuracy: 0.9971 - val_loss: 0.1587 - val_accuracy: 0.9720
Epoch 40/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.1458 - val_accuracy: 0.9742
Epoch 41/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 0.9995 - val_loss: 0.1435 - val_accuracy: 0.9747
Epoch 42/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 0.1354 - val_accuracy: 0.9758
Epoch 43/200
235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1322 - val_accuracy: 0.9771
Epoch 44/200
235/235 [==============================] - 2s 8ms/step - loss: 8.4953e-04 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9764
Epoch 45/200
235/235 [==============================] - 2s 8ms/step - loss: 5.0979e-04 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9768
Epoch 46/200
235/235 [==============================] - 2s 8ms/step - loss: 3.7012e-04 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9764
Epoch 47/200
235/235 [==============================] - 2s 9ms/step - loss: 2.9299e-04 - accuracy: 1.0000 - val_loss: 0.1362 - val_accuracy: 0.9766
Epoch 48/200
235/235 [==============================] - 2s 9ms/step - loss: 2.5237e-04 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9766
Epoch 49/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2193e-04 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9764
Epoch 50/200
235/235 [==============================] - 2s 8ms/step - loss: 1.9869e-04 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9765
Epoch 51/200
235/235 [==============================] - 2s 8ms/step - loss: 1.7906e-04 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9767
Epoch 52/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6246e-04 - accuracy: 1.0000 - val_loss: 0.1399 - val_accuracy: 0.9767
Epoch 53/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4785e-04 - accuracy: 1.0000 - val_loss: 0.1409 - val_accuracy: 0.9768
Epoch 54/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3440e-04 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9768
Epoch 55/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2254e-04 - accuracy: 1.0000 - val_loss: 0.1431 - val_accuracy: 0.9767
Epoch 56/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1139e-04 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9765
Epoch 57/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0141e-04 - accuracy: 1.0000 - val_loss: 0.1455 - val_accuracy: 0.9765
Epoch 58/200
235/235 [==============================] - 2s 8ms/step - loss: 9.2156e-05 - accuracy: 1.0000 - val_loss: 0.1467 - val_accuracy: 0.9765
Epoch 59/200
235/235 [==============================] - 2s 8ms/step - loss: 8.3571e-05 - accuracy: 1.0000 - val_loss: 0.1481 - val_accuracy: 0.9765
Epoch 60/200
235/235 [==============================] - 2s 8ms/step - loss: 7.5801e-05 - accuracy: 1.0000 - val_loss: 0.1495 - val_accuracy: 0.9763
Epoch 61/200
235/235 [==============================] - 2s 8ms/step - loss: 6.8629e-05 - accuracy: 1.0000 - val_loss: 0.1508 - val_accuracy: 0.9762
Epoch 62/200
235/235 [==============================] - 2s 8ms/step - loss: 6.2011e-05 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9761
Epoch 63/200
235/235 [==============================] - 2s 8ms/step - loss: 5.5940e-05 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9760
Epoch 64/200
235/235 [==============================] - 2s 8ms/step - loss: 5.0451e-05 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9759
Epoch 65/200
235/235 [==============================] - 2s 8ms/step - loss: 4.5384e-05 - accuracy: 1.0000 - val_loss: 0.1567 - val_accuracy: 0.9759
Epoch 66/200
235/235 [==============================] - 2s 8ms/step - loss: 4.0767e-05 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9759
Epoch 67/200
235/235 [==============================] - 2s 8ms/step - loss: 3.6582e-05 - accuracy: 1.0000 - val_loss: 0.1598 - val_accuracy: 0.9759
Epoch 68/200
235/235 [==============================] - 2s 8ms/step - loss: 3.2753e-05 - accuracy: 1.0000 - val_loss: 0.1614 - val_accuracy: 0.9757
Epoch 69/200
235/235 [==============================] - 2s 8ms/step - loss: 2.9328e-05 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9757
Epoch 70/200
235/235 [==============================] - 2s 8ms/step - loss: 2.6200e-05 - accuracy: 1.0000 - val_loss: 0.1647 - val_accuracy: 0.9758
Epoch 71/200
235/235 [==============================] - 2s 8ms/step - loss: 2.3433e-05 - accuracy: 1.0000 - val_loss: 0.1664 - val_accuracy: 0.9758
Epoch 72/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0853e-05 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9758
Epoch 73/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8606e-05 - accuracy: 1.0000 - val_loss: 0.1698 - val_accuracy: 0.9760
Epoch 74/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6546e-05 - accuracy: 1.0000 - val_loss: 0.1716 - val_accuracy: 0.9760
Epoch 75/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4721e-05 - accuracy: 1.0000 - val_loss: 0.1733 - val_accuracy: 0.9760
Epoch 76/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3059e-05 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9760
Epoch 77/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1588e-05 - accuracy: 1.0000 - val_loss: 0.1768 - val_accuracy: 0.9759
Epoch 78/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0296e-05 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9759
Epoch 79/200
235/235 [==============================] - 2s 8ms/step - loss: 9.1215e-06 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9758
Epoch 80/200
235/235 [==============================] - 2s 8ms/step - loss: 8.0643e-06 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9758
Epoch 81/200
235/235 [==============================] - 2s 8ms/step - loss: 7.1555e-06 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9758
Epoch 82/200
235/235 [==============================] - 2s 8ms/step - loss: 6.3320e-06 - accuracy: 1.0000 - val_loss: 0.1858 - val_accuracy: 0.9758
Epoch 83/200
235/235 [==============================] - 2s 8ms/step - loss: 5.6113e-06 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9758
Epoch 84/200
235/235 [==============================] - 2s 8ms/step - loss: 4.9556e-06 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9758
Epoch 85/200
235/235 [==============================] - 2s 8ms/step - loss: 4.3823e-06 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9757
Epoch 86/200
235/235 [==============================] - 2s 8ms/step - loss: 3.8796e-06 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9756
Epoch 87/200
235/235 [==============================] - 2s 8ms/step - loss: 3.4264e-06 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9754
Epoch 88/200
235/235 [==============================] - 2s 8ms/step - loss: 3.0346e-06 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9754
Epoch 89/200
235/235 [==============================] - 2s 8ms/step - loss: 2.6798e-06 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9754
Epoch 90/200
235/235 [==============================] - 2s 8ms/step - loss: 2.3682e-06 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9753
Epoch 91/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0967e-06 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9754
Epoch 92/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8517e-06 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9755
Epoch 93/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6378e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9755
Epoch 94/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4507e-06 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9756
Epoch 95/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2838e-06 - accuracy: 1.0000 - val_loss: 0.2091 - val_accuracy: 0.9755
Epoch 96/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1365e-06 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9755
Epoch 97/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0071e-06 - accuracy: 1.0000 - val_loss: 0.2127 - val_accuracy: 0.9756
Epoch 98/200
235/235 [==============================] - 2s 8ms/step - loss: 8.9258e-07 - accuracy: 1.0000 - val_loss: 0.2144 - val_accuracy: 0.9754
Epoch 99/200
235/235 [==============================] - 2s 8ms/step - loss: 7.9194e-07 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9752
Epoch 100/200
235/235 [==============================] - 2s 8ms/step - loss: 7.0228e-07 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9751
Epoch 101/200
235/235 [==============================] - 2s 8ms/step - loss: 6.2214e-07 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 0.9752
Epoch 102/200
235/235 [==============================] - 2s 8ms/step - loss: 5.5315e-07 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9751
Epoch 103/200
235/235 [==============================] - 2s 8ms/step - loss: 4.9180e-07 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9751
Epoch 104/200
235/235 [==============================] - 2s 8ms/step - loss: 4.3821e-07 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9751
Epoch 105/200
235/235 [==============================] - 2s 8ms/step - loss: 3.9014e-07 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9751
Epoch 106/200
235/235 [==============================] - 2s 8ms/step - loss: 3.4907e-07 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 0.9750
Epoch 107/200
235/235 [==============================] - 2s 8ms/step - loss: 3.1133e-07 - accuracy: 1.0000 - val_loss: 0.2293 - val_accuracy: 0.9751
Epoch 108/200
235/235 [==============================] - 2s 8ms/step - loss: 2.7862e-07 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9751
Epoch 109/200
235/235 [==============================] - 2s 8ms/step - loss: 2.4944e-07 - accuracy: 1.0000 - val_loss: 0.2326 - val_accuracy: 0.9750
Epoch 110/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2375e-07 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9751
Epoch 111/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0102e-07 - accuracy: 1.0000 - val_loss: 0.2355 - val_accuracy: 0.9752
Epoch 112/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8113e-07 - accuracy: 1.0000 - val_loss: 0.2370 - val_accuracy: 0.9750
Epoch 113/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6325e-07 - accuracy: 1.0000 - val_loss: 0.2383 - val_accuracy: 0.9750
Epoch 114/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4751e-07 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9749
Epoch 115/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3340e-07 - accuracy: 1.0000 - val_loss: 0.2410 - val_accuracy: 0.9749
Epoch 116/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2088e-07 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9748
Epoch 117/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0987e-07 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9749
Epoch 118/200
235/235 [==============================] - 2s 8ms/step - loss: 9.9933e-08 - accuracy: 1.0000 - val_loss: 0.2448 - val_accuracy: 0.9748
Epoch 119/200
235/235 [==============================] - 2s 8ms/step - loss: 9.1151e-08 - accuracy: 1.0000 - val_loss: 0.2461 - val_accuracy: 0.9748
Epoch 120/200
235/235 [==============================] - 2s 8ms/step - loss: 8.3413e-08 - accuracy: 1.0000 - val_loss: 0.2473 - val_accuracy: 0.9748
Epoch 121/200
235/235 [==============================] - 2s 8ms/step - loss: 7.6330e-08 - accuracy: 1.0000 - val_loss: 0.2482 - val_accuracy: 0.9748
Epoch 122/200
235/235 [==============================] - 2s 8ms/step - loss: 7.0268e-08 - accuracy: 1.0000 - val_loss: 0.2495 - val_accuracy: 0.9747
Epoch 123/200
235/235 [==============================] - 2s 8ms/step - loss: 6.4804e-08 - accuracy: 1.0000 - val_loss: 0.2507 - val_accuracy: 0.9748
Epoch 124/200
235/235 [==============================] - 2s 8ms/step - loss: 5.9696e-08 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9748
Epoch 125/200
235/235 [==============================] - 2s 8ms/step - loss: 5.5347e-08 - accuracy: 1.0000 - val_loss: 0.2525 - val_accuracy: 0.9747
Epoch 126/200
235/235 [==============================] - 2s 8ms/step - loss: 5.1389e-08 - accuracy: 1.0000 - val_loss: 0.2536 - val_accuracy: 0.9748
Epoch 127/200
235/235 [==============================] - 2s 8ms/step - loss: 4.7648e-08 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9748
Epoch 128/200
235/235 [==============================] - 2s 8ms/step - loss: 4.4401e-08 - accuracy: 1.0000 - val_loss: 0.2552 - val_accuracy: 0.9750
Epoch 129/200
235/235 [==============================] - 2s 8ms/step - loss: 4.1570e-08 - accuracy: 1.0000 - val_loss: 0.2561 - val_accuracy: 0.9747
Epoch 130/200
235/235 [==============================] - 2s 8ms/step - loss: 3.8848e-08 - accuracy: 1.0000 - val_loss: 0.2569 - val_accuracy: 0.9748
Epoch 131/200
235/235 [==============================] - 2s 8ms/step - loss: 3.6373e-08 - accuracy: 1.0000 - val_loss: 0.2576 - val_accuracy: 0.9747
Epoch 132/200
235/235 [==============================] - 2s 8ms/step - loss: 3.4205e-08 - accuracy: 1.0000 - val_loss: 0.2583 - val_accuracy: 0.9746
Epoch 133/200
235/235 [==============================] - 2s 8ms/step - loss: 3.2196e-08 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9746
Epoch 134/200
235/235 [==============================] - 2s 8ms/step - loss: 3.0398e-08 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9746
Epoch 135/200
235/235 [==============================] - 2s 8ms/step - loss: 2.8725e-08 - accuracy: 1.0000 - val_loss: 0.2605 - val_accuracy: 0.9747
Epoch 136/200
235/235 [==============================] - 2s 8ms/step - loss: 2.7271e-08 - accuracy: 1.0000 - val_loss: 0.2611 - val_accuracy: 0.9747
Epoch 137/200
235/235 [==============================] - 2s 8ms/step - loss: 2.5934e-08 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9747
Epoch 138/200
235/235 [==============================] - 2s 8ms/step - loss: 2.4686e-08 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9747
Epoch 139/200
235/235 [==============================] - 2s 8ms/step - loss: 2.3409e-08 - accuracy: 1.0000 - val_loss: 0.2629 - val_accuracy: 0.9747
Epoch 140/200
235/235 [==============================] - 2s 8ms/step - loss: 2.2366e-08 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9747
Epoch 141/200
235/235 [==============================] - 2s 8ms/step - loss: 2.1380e-08 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9746
Epoch 142/200
235/235 [==============================] - 2s 8ms/step - loss: 2.0470e-08 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9745
Epoch 143/200
235/235 [==============================] - 2s 8ms/step - loss: 1.9560e-08 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9745
Epoch 144/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8742e-08 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9745
Epoch 145/200
235/235 [==============================] - 2s 8ms/step - loss: 1.8015e-08 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9745
Epoch 146/200
235/235 [==============================] - 2s 8ms/step - loss: 1.7315e-08 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9745
Epoch 147/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6709e-08 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9744
Epoch 148/200
235/235 [==============================] - 2s 8ms/step - loss: 1.6183e-08 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9744
Epoch 149/200
235/235 [==============================] - 2s 8ms/step - loss: 1.5581e-08 - accuracy: 1.0000 - val_loss: 0.2677 - val_accuracy: 0.9744
Epoch 150/200
235/235 [==============================] - 2s 8ms/step - loss: 1.5094e-08 - accuracy: 1.0000 - val_loss: 0.2681 - val_accuracy: 0.9745
Epoch 151/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4516e-08 - accuracy: 1.0000 - val_loss: 0.2684 - val_accuracy: 0.9745
Epoch 152/200
235/235 [==============================] - 2s 8ms/step - loss: 1.4065e-08 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9745
Epoch 153/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3592e-08 - accuracy: 1.0000 - val_loss: 0.2692 - val_accuracy: 0.9745
Epoch 154/200
235/235 [==============================] - 2s 8ms/step - loss: 1.3212e-08 - accuracy: 1.0000 - val_loss: 0.2696 - val_accuracy: 0.9743
Epoch 155/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2785e-08 - accuracy: 1.0000 - val_loss: 0.2699 - val_accuracy: 0.9744
Epoch 156/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2416e-08 - accuracy: 1.0000 - val_loss: 0.2703 - val_accuracy: 0.9743
Epoch 157/200
235/235 [==============================] - 2s 8ms/step - loss: 1.2068e-08 - accuracy: 1.0000 - val_loss: 0.2706 - val_accuracy: 0.9744
Epoch 158/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1754e-08 - accuracy: 1.0000 - val_loss: 0.2709 - val_accuracy: 0.9744
Epoch 159/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1361e-08 - accuracy: 1.0000 - val_loss: 0.2711 - val_accuracy: 0.9743
Epoch 160/200
235/235 [==============================] - 2s 8ms/step - loss: 1.1057e-08 - accuracy: 1.0000 - val_loss: 0.2714 - val_accuracy: 0.9744
Epoch 161/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0788e-08 - accuracy: 1.0000 - val_loss: 0.2716 - val_accuracy: 0.9743
Epoch 162/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0478e-08 - accuracy: 1.0000 - val_loss: 0.2719 - val_accuracy: 0.9743
Epoch 163/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0276e-08 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9744
Epoch 164/200
235/235 [==============================] - 2s 8ms/step - loss: 1.0024e-08 - accuracy: 1.0000 - val_loss: 0.2723 - val_accuracy: 0.9742
Epoch 165/200
235/235 [==============================] - 2s 8ms/step - loss: 9.7613e-09 - accuracy: 1.0000 - val_loss: 0.2725 - val_accuracy: 0.9743
Epoch 166/200
235/235 [==============================] - 2s 8ms/step - loss: 9.5526e-09 - accuracy: 1.0000 - val_loss: 0.2727 - val_accuracy: 0.9743
Epoch 167/200
235/235 [==============================] - 2s 9ms/step - loss: 9.3182e-09 - accuracy: 1.0000 - val_loss: 0.2729 - val_accuracy: 0.9744
Epoch 168/200
235/235 [==============================] - 2s 9ms/step - loss: 9.1553e-09 - accuracy: 1.0000 - val_loss: 0.2731 - val_accuracy: 0.9744
Epoch 169/200
235/235 [==============================] - 2s 8ms/step - loss: 8.9208e-09 - accuracy: 1.0000 - val_loss: 0.2733 - val_accuracy: 0.9744
Epoch 170/200
235/235 [==============================] - 2s 8ms/step - loss: 8.7440e-09 - accuracy: 1.0000 - val_loss: 0.2734 - val_accuracy: 0.9743
Epoch 171/200
235/235 [==============================] - 2s 8ms/step - loss: 8.5572e-09 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9743
Epoch 172/200
235/235 [==============================] - 2s 8ms/step - loss: 8.3784e-09 - accuracy: 1.0000 - val_loss: 0.2737 - val_accuracy: 0.9743
Epoch 173/200
235/235 [==============================] - 2s 8ms/step - loss: 8.2155e-09 - accuracy: 1.0000 - val_loss: 0.2738 - val_accuracy: 0.9743
Epoch 174/200
235/235 [==============================] - 2s 8ms/step - loss: 8.0367e-09 - accuracy: 1.0000 - val_loss: 0.2740 - val_accuracy: 0.9745
Epoch 175/200
235/235 [==============================] - 2s 8ms/step - loss: 7.8460e-09 - accuracy: 1.0000 - val_loss: 0.2741 - val_accuracy: 0.9745
Epoch 176/200
235/235 [==============================] - 2s 8ms/step - loss: 7.7049e-09 - accuracy: 1.0000 - val_loss: 0.2743 - val_accuracy: 0.9746
Epoch 177/200
235/235 [==============================] - 2s 8ms/step - loss: 7.5698e-09 - accuracy: 1.0000 - val_loss: 0.2745 - val_accuracy: 0.9746
Epoch 178/200
235/235 [==============================] - 2s 8ms/step - loss: 7.4367e-09 - accuracy: 1.0000 - val_loss: 0.2746 - val_accuracy: 0.9745
Epoch 179/200
235/235 [==============================] - 2s 8ms/step - loss: 7.2996e-09 - accuracy: 1.0000 - val_loss: 0.2747 - val_accuracy: 0.9746
Epoch 180/200
235/235 [==============================] - 2s 8ms/step - loss: 7.1645e-09 - accuracy: 1.0000 - val_loss: 0.2748 - val_accuracy: 0.9746
Epoch 181/200
235/235 [==============================] - 2s 8ms/step - loss: 6.9857e-09 - accuracy: 1.0000 - val_loss: 0.2750 - val_accuracy: 0.9746
Epoch 182/200
235/235 [==============================] - 2s 8ms/step - loss: 6.9022e-09 - accuracy: 1.0000 - val_loss: 0.2751 - val_accuracy: 0.9746
Epoch 183/200
235/235 [==============================] - 2s 8ms/step - loss: 6.7830e-09 - accuracy: 1.0000 - val_loss: 0.2752 - val_accuracy: 0.9746
Epoch 184/200
235/235 [==============================] - 2s 8ms/step - loss: 6.6141e-09 - accuracy: 1.0000 - val_loss: 0.2753 - val_accuracy: 0.9747
Epoch 185/200
235/235 [==============================] - 2s 8ms/step - loss: 6.5943e-09 - accuracy: 1.0000 - val_loss: 0.2754 - val_accuracy: 0.9748
Epoch 186/200
235/235 [==============================] - 2s 9ms/step - loss: 6.4552e-09 - accuracy: 1.0000 - val_loss: 0.2755 - val_accuracy: 0.9748
Epoch 187/200
235/235 [==============================] - 2s 8ms/step - loss: 6.2724e-09 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 0.9748
Epoch 188/200
235/235 [==============================] - 2s 8ms/step - loss: 6.2466e-09 - accuracy: 1.0000 - val_loss: 0.2758 - val_accuracy: 0.9748
Epoch 189/200
235/235 [==============================] - 2s 8ms/step - loss: 6.1115e-09 - accuracy: 1.0000 - val_loss: 0.2759 - val_accuracy: 0.9750
Epoch 190/200
235/235 [==============================] - 2s 8ms/step - loss: 6.0002e-09 - accuracy: 1.0000 - val_loss: 0.2759 - val_accuracy: 0.9750
Epoch 191/200
235/235 [==============================] - 2s 8ms/step - loss: 5.8929e-09 - accuracy: 1.0000 - val_loss: 0.2760 - val_accuracy: 0.9750
Epoch 192/200
235/235 [==============================] - 2s 8ms/step - loss: 5.8194e-09 - accuracy: 1.0000 - val_loss: 0.2761 - val_accuracy: 0.9750
Epoch 193/200
235/235 [==============================] - 2s 8ms/step - loss: 5.7121e-09 - accuracy: 1.0000 - val_loss: 0.2762 - val_accuracy: 0.9750
Epoch 194/200
235/235 [==============================] - 2s 8ms/step - loss: 5.5989e-09 - accuracy: 1.0000 - val_loss: 0.2763 - val_accuracy: 0.9750
Epoch 195/200
235/235 [==============================] - 2s 8ms/step - loss: 5.5532e-09 - accuracy: 1.0000 - val_loss: 0.2763 - val_accuracy: 0.9749
Epoch 196/200
235/235 [==============================] - 2s 8ms/step - loss: 5.4896e-09 - accuracy: 1.0000 - val_loss: 0.2764 - val_accuracy: 0.9749
Epoch 197/200
235/235 [==============================] - 2s 8ms/step - loss: 5.3465e-09 - accuracy: 1.0000 - val_loss: 0.2765 - val_accuracy: 0.9748
Epoch 198/200
235/235 [==============================] - 2s 8ms/step - loss: 5.2909e-09 - accuracy: 1.0000 - val_loss: 0.2765 - val_accuracy: 0.9748
Epoch 199/200
235/235 [==============================] - 2s 8ms/step - loss: 5.1936e-09 - accuracy: 1.0000 - val_loss: 0.2766 - val_accuracy: 0.9748
Epoch 200/200
235/235 [==============================] - 2s 8ms/step - loss: 5.1379e-09 - accuracy: 1.0000 - val_loss: 0.2767 - val_accuracy: 0.9748
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.03714788891375065
Thresholhold 0.06566019356250763
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.5
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 1. ... 1. 0. 0.]
 [0. 0. 1. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 1. 1.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 1. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.06050238758325577
Thresholhold -0.09211743623018265
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.1136733777821064
Thresholhold 0.0008880794048309326
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
  5/235 [..............................] - ETA: 3s - loss: 7.3886 - accuracy: 0.3617     WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0138s vs `on_train_batch_begin` time: 11.2385s). Check your callbacks.
235/235 [==============================] - 71s 13ms/step - loss: 2.2185 - accuracy: 0.9145 - val_loss: 1.8999 - val_accuracy: 0.6117
[1.6624929e-06 3.2097237e-06 2.1899720e-07 ... 9.3255341e-02 1.3094884e-01
 2.7217984e-02]
Sparsity at: 0.44178812922614574
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4550 - accuracy: 0.9610 - val_loss: 0.6002 - val_accuracy: 0.9487
[2.8944019e-12 9.3174340e-12 1.2026992e-12 ... 4.5141589e-02 8.6929470e-02
 3.0727102e-03]
Sparsity at: 0.44178812922614574
Epoch 3/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2909 - accuracy: 0.9676 - val_loss: 0.3207 - val_accuracy: 0.9528
[ 2.0241179e-17  4.9652535e-17  3.3510400e-19 ...  1.2248518e-02
  6.9727145e-02 -1.1147665e-02]
Sparsity at: 0.44178812922614574
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2583 - accuracy: 0.9688 - val_loss: 0.2643 - val_accuracy: 0.9624
[-1.0361830e-22  3.6511893e-22  3.9754488e-23 ... -5.1917536e-03
  5.8777627e-02 -1.5626829e-02]
Sparsity at: 0.44178812922614574
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2394 - accuracy: 0.9707 - val_loss: 0.2734 - val_accuracy: 0.9583
[-6.5022505e-28 -1.0774632e-27  4.2780378e-29 ... -1.5886651e-02
  4.9290005e-02 -1.8585239e-02]
Sparsity at: 0.44178812922614574
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2287 - accuracy: 0.9716 - val_loss: 0.2598 - val_accuracy: 0.9590
[ 3.3639677e-33 -3.9719219e-33  3.0523619e-34 ... -1.8727832e-02
  3.4742780e-02 -1.4041513e-02]
Sparsity at: 0.44178812922614574
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2180 - accuracy: 0.9733 - val_loss: 0.2510 - val_accuracy: 0.9593
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.5441591e-02
  2.8049611e-02 -1.0453678e-02]
Sparsity at: 0.4417918858001503
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2125 - accuracy: 0.9729 - val_loss: 0.2344 - val_accuracy: 0.9648
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.6361793e-02
  7.1320422e-03 -7.4196788e-03]
Sparsity at: 0.44179564237415475
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2087 - accuracy: 0.9737 - val_loss: 0.2479 - val_accuracy: 0.9576
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.9167842e-02
 -5.3540105e-03 -4.9064197e-03]
Sparsity at: 0.44179564237415475
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2023 - accuracy: 0.9747 - val_loss: 0.2367 - val_accuracy: 0.9615
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.8079091e-02
 -1.5780313e-02 -5.0926567e-03]
Sparsity at: 0.44179564237415475
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1996 - accuracy: 0.9738 - val_loss: 0.2331 - val_accuracy: 0.9621
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -3.7705369e-02
 -2.1619046e-02 -1.3931784e-03]
Sparsity at: 0.4417993989481593
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1917 - accuracy: 0.9749 - val_loss: 0.2242 - val_accuracy: 0.9635
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -3.0437108e-02
 -2.9820310e-02 -2.9129798e-03]
Sparsity at: 0.4417993989481593
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1874 - accuracy: 0.9751 - val_loss: 0.2072 - val_accuracy: 0.9687
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -3.2768909e-02
 -3.3885043e-02 -1.2541483e-03]
Sparsity at: 0.4417993989481593
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1856 - accuracy: 0.9752 - val_loss: 0.2300 - val_accuracy: 0.9603
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -3.0562755e-02
 -3.5466906e-02 -1.0007619e-03]
Sparsity at: 0.44180315552216376
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1821 - accuracy: 0.9757 - val_loss: 0.2253 - val_accuracy: 0.9602
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -3.5053778e-02
 -3.3153541e-02 -1.7603629e-04]
Sparsity at: 0.44180315552216376
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1836 - accuracy: 0.9750 - val_loss: 0.2137 - val_accuracy: 0.9655
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -3.0226177e-02
 -3.1429261e-02 -6.3466090e-03]
Sparsity at: 0.44180315552216376
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1754 - accuracy: 0.9768 - val_loss: 0.2126 - val_accuracy: 0.9628
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -3.0496808e-02
 -2.9177219e-02  9.8887214e-04]
Sparsity at: 0.44180315552216376
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1743 - accuracy: 0.9764 - val_loss: 0.2120 - val_accuracy: 0.9640
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.7454864e-02
 -3.3434249e-02  1.7972835e-03]
Sparsity at: 0.4417993989481593
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1728 - accuracy: 0.9762 - val_loss: 0.2190 - val_accuracy: 0.9610
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.6760485e-02
 -3.3223599e-02  2.9191773e-03]
Sparsity at: 0.4417993989481593
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1683 - accuracy: 0.9771 - val_loss: 0.2002 - val_accuracy: 0.9676
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.5979055e-02
 -3.1245666e-02  4.0384433e-03]
Sparsity at: 0.4417993989481593
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1699 - accuracy: 0.9758 - val_loss: 0.2247 - val_accuracy: 0.9579
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.8438160e-02
 -2.7736817e-02  3.9808271e-03]
Sparsity at: 0.4417993989481593
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1657 - accuracy: 0.9779 - val_loss: 0.2125 - val_accuracy: 0.9612
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.4062922e-02
 -3.0230768e-02  4.2583174e-03]
Sparsity at: 0.4417993989481593
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1651 - accuracy: 0.9768 - val_loss: 0.2082 - val_accuracy: 0.9636
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -3.1682074e-02
 -2.8027184e-02  2.7294154e-03]
Sparsity at: 0.4417993989481593
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1628 - accuracy: 0.9777 - val_loss: 0.2271 - val_accuracy: 0.9569
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.7358564e-02
 -3.1847972e-02  5.3230035e-03]
Sparsity at: 0.4417993989481593
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1626 - accuracy: 0.9772 - val_loss: 0.1951 - val_accuracy: 0.9649
[-5.9413406e-34 -3.3333806e-34  3.0523619e-34 ... -2.3396580e-02
 -2.7448196e-02  2.4411208e-03]
Sparsity at: 0.4417993989481593
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1590 - accuracy: 0.9783 - val_loss: 0.2211 - val_accuracy: 0.9602
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3923736e-02
 -2.7993280e-02  3.6120310e-03]
Sparsity at: 0.4417993989481593
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1597 - accuracy: 0.9774 - val_loss: 0.2095 - val_accuracy: 0.9639
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.5788357e-02
 -2.4359895e-02  8.9152381e-03]
Sparsity at: 0.4417993989481593
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1611 - accuracy: 0.9776 - val_loss: 0.2147 - val_accuracy: 0.9614
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.9488184e-02
 -2.5801526e-02  8.3187940e-03]
Sparsity at: 0.4417993989481593
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1538 - accuracy: 0.9789 - val_loss: 0.1973 - val_accuracy: 0.9664
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3674978e-02
 -2.3095697e-02  5.4526119e-03]
Sparsity at: 0.4417993989481593
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1576 - accuracy: 0.9776 - val_loss: 0.1999 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.5240008e-02
 -2.6234364e-02  2.8713746e-03]
Sparsity at: 0.4417993989481593
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1554 - accuracy: 0.9785 - val_loss: 0.2043 - val_accuracy: 0.9626
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.2578320e-02
 -2.8285490e-02  1.8022847e-03]
Sparsity at: 0.4417993989481593
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1545 - accuracy: 0.9782 - val_loss: 0.1982 - val_accuracy: 0.9636
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.7176179e-02
 -2.6150517e-02  2.4725511e-03]
Sparsity at: 0.4417993989481593
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1528 - accuracy: 0.9783 - val_loss: 0.2193 - val_accuracy: 0.9614
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2632781e-02
 -2.2746364e-02 -1.7931310e-03]
Sparsity at: 0.4417993989481593
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1533 - accuracy: 0.9781 - val_loss: 0.1895 - val_accuracy: 0.9658
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5802259e-02
 -2.4988521e-02  4.7082440e-03]
Sparsity at: 0.4417993989481593
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1529 - accuracy: 0.9781 - val_loss: 0.1874 - val_accuracy: 0.9664
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2290499e-02
 -2.1302354e-02 -8.3416700e-05]
Sparsity at: 0.4417993989481593
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1523 - accuracy: 0.9784 - val_loss: 0.2378 - val_accuracy: 0.9542
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.9026682e-03
 -2.1507312e-02  4.2525008e-03]
Sparsity at: 0.4417993989481593
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1508 - accuracy: 0.9785 - val_loss: 0.2200 - val_accuracy: 0.9575
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.3448283e-03
 -2.1004416e-02  3.9713625e-03]
Sparsity at: 0.4417993989481593
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1497 - accuracy: 0.9780 - val_loss: 0.1950 - val_accuracy: 0.9651
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1725971e-02
 -2.5112376e-02  8.9863949e-03]
Sparsity at: 0.4417993989481593
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1480 - accuracy: 0.9786 - val_loss: 0.1933 - val_accuracy: 0.9654
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.8046100e-02
 -2.6297163e-02  8.2598040e-03]
Sparsity at: 0.4417993989481593
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1512 - accuracy: 0.9775 - val_loss: 0.1989 - val_accuracy: 0.9636
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.4174120e-02
 -2.5194939e-02  6.1688498e-03]
Sparsity at: 0.4417993989481593
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1464 - accuracy: 0.9793 - val_loss: 0.2063 - val_accuracy: 0.9622
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4970554e-02
 -2.2326620e-02  5.6374054e-03]
Sparsity at: 0.4417993989481593
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1501 - accuracy: 0.9778 - val_loss: 0.2102 - val_accuracy: 0.9588
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.7817944e-02
 -2.5036179e-02  7.9446919e-03]
Sparsity at: 0.4417993989481593
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1505 - accuracy: 0.9783 - val_loss: 0.2065 - val_accuracy: 0.9629
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6724825e-02
 -2.1994717e-02  7.2345664e-03]
Sparsity at: 0.4417993989481593
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1483 - accuracy: 0.9786 - val_loss: 0.2036 - val_accuracy: 0.9623
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6407726e-02
 -2.1818722e-02  1.1958867e-02]
Sparsity at: 0.4417993989481593
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1453 - accuracy: 0.9793 - val_loss: 0.2011 - val_accuracy: 0.9645
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.8652368e-02
 -2.1983163e-02  1.5262198e-02]
Sparsity at: 0.4417993989481593
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9796 - val_loss: 0.2065 - val_accuracy: 0.9608
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1827088e-02
 -1.6235866e-02  1.0372529e-02]
Sparsity at: 0.4417993989481593
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1481 - accuracy: 0.9783 - val_loss: 0.1985 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5075979e-02
 -1.3160142e-02  8.8828318e-03]
Sparsity at: 0.4417993989481593
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1476 - accuracy: 0.9779 - val_loss: 0.1923 - val_accuracy: 0.9659
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.8134743e-02
 -1.2282557e-02  5.5955360e-03]
Sparsity at: 0.4417993989481593
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1452 - accuracy: 0.9784 - val_loss: 0.1963 - val_accuracy: 0.9645
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3187683e-02
 -1.8805426e-02  8.3560804e-03]
Sparsity at: 0.4417993989481593
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1416 - accuracy: 0.9803 - val_loss: 0.2029 - val_accuracy: 0.9638
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4784321e-02
 -2.1386396e-02  1.0538180e-02]
Sparsity at: 0.4417993989481593
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 1.951142549968065e-34
Thresholhold 4.774037043704479e-34
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.0001698937390694627
Thresholhold 7.183215302575263e-07
Using suggest threshold.
Applying new mask
Percentage zeros 0.3452
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.028607948215186862
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 208s 12ms/step - loss: 0.1440 - accuracy: 0.9788 - val_loss: 0.1923 - val_accuracy: 0.9637
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3009785e-02
 -2.1778932e-02  1.0672458e-02]
Sparsity at: 0.609564237415477
Epoch 52/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9798 - val_loss: 0.1800 - val_accuracy: 0.9692
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1169086e-02
 -2.0858640e-02  5.8950577e-03]
Sparsity at: 0.609564237415477
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9797 - val_loss: 0.1787 - val_accuracy: 0.9675
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4858063e-02
 -1.6606886e-02  8.7690596e-03]
Sparsity at: 0.609564237415477
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1400 - accuracy: 0.9800 - val_loss: 0.1853 - val_accuracy: 0.9674
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6757669e-02
 -2.1829134e-02  1.0400970e-02]
Sparsity at: 0.609564237415477
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9786 - val_loss: 0.2177 - val_accuracy: 0.9564
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.8442413e-02
 -2.3665896e-02  1.4300141e-02]
Sparsity at: 0.609564237415477
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9802 - val_loss: 0.2033 - val_accuracy: 0.9616
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5399406e-02
 -2.4490070e-02  7.9507884e-03]
Sparsity at: 0.609564237415477
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9793 - val_loss: 0.1954 - val_accuracy: 0.9654
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4826988e-02
 -2.1621186e-02  1.0209977e-02]
Sparsity at: 0.609564237415477
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9804 - val_loss: 0.1915 - val_accuracy: 0.9662
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1701760e-02
 -1.9501088e-02  7.5450740e-03]
Sparsity at: 0.609564237415477
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1424 - accuracy: 0.9792 - val_loss: 0.1952 - val_accuracy: 0.9647
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.0480463e-02
 -1.5338058e-02  6.5389788e-03]
Sparsity at: 0.609564237415477
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9805 - val_loss: 0.1839 - val_accuracy: 0.9670
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.6285473e-02
 -1.9495077e-02  2.8717907e-03]
Sparsity at: 0.609564237415477
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1424 - accuracy: 0.9788 - val_loss: 0.2238 - val_accuracy: 0.9551
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.1886345e-02
 -1.2139132e-02 -2.6857383e-03]
Sparsity at: 0.609564237415477
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9801 - val_loss: 0.1849 - val_accuracy: 0.9680
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.8650474e-02
 -9.2257550e-03 -4.3910984e-03]
Sparsity at: 0.609564237415477
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9795 - val_loss: 0.1921 - val_accuracy: 0.9658
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3111241e-02
 -6.0814638e-03  5.2646971e-03]
Sparsity at: 0.609564237415477
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.2307 - val_accuracy: 0.9501
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3017526e-02
 -1.1560500e-02  7.4404064e-03]
Sparsity at: 0.609564237415477
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1429 - accuracy: 0.9789 - val_loss: 0.1797 - val_accuracy: 0.9686
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3956325e-02
 -1.1294445e-02  8.2174537e-04]
Sparsity at: 0.609564237415477
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9817 - val_loss: 0.1830 - val_accuracy: 0.9680
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5785404e-02
 -8.9000156e-03  8.1034834e-03]
Sparsity at: 0.609564237415477
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9796 - val_loss: 0.1915 - val_accuracy: 0.9651
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4858944e-02
 -1.5344942e-02  8.3576106e-03]
Sparsity at: 0.609564237415477
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9797 - val_loss: 0.1873 - val_accuracy: 0.9640
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6639698e-02
 -1.4613044e-02  6.3805543e-03]
Sparsity at: 0.609564237415477
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9798 - val_loss: 0.1986 - val_accuracy: 0.9606
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.7299695e-02
 -2.2622205e-02  1.3763225e-02]
Sparsity at: 0.609564237415477
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9792 - val_loss: 0.2194 - val_accuracy: 0.9576
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.9209603e-02
 -1.7317712e-02  7.2026351e-03]
Sparsity at: 0.609564237415477
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9797 - val_loss: 0.1869 - val_accuracy: 0.9673
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.2166521e-03
 -1.6770680e-02  3.1982108e-03]
Sparsity at: 0.609564237415477
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9791 - val_loss: 0.2114 - val_accuracy: 0.9582
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.9663267e-03
 -1.5658598e-02  6.7525562e-03]
Sparsity at: 0.609564237415477
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9815 - val_loss: 0.2225 - val_accuracy: 0.9562
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.9517953e-03
 -2.5125485e-02  9.5243147e-03]
Sparsity at: 0.609564237415477
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9803 - val_loss: 0.1934 - val_accuracy: 0.9629
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2206450e-02
 -1.8285898e-02  9.9423276e-03]
Sparsity at: 0.609564237415477
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9793 - val_loss: 0.2201 - val_accuracy: 0.9557
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.8073155e-02
 -1.0135039e-02  4.3367790e-03]
Sparsity at: 0.609564237415477
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9804 - val_loss: 0.1884 - val_accuracy: 0.9657
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3424759e-02
 -6.7816973e-03  1.5085875e-03]
Sparsity at: 0.609564237415477
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9801 - val_loss: 0.1994 - val_accuracy: 0.9636
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.4456926e-03
 -1.6168116e-02 -3.2373641e-03]
Sparsity at: 0.609564237415477
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9800 - val_loss: 0.2038 - val_accuracy: 0.9608
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ... -1.55844782e-02
 -1.43230595e-02  1.94526673e-03]
Sparsity at: 0.609564237415477
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9802 - val_loss: 0.1872 - val_accuracy: 0.9645
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5931122e-02
 -1.4050139e-02  8.1684312e-04]
Sparsity at: 0.609564237415477
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9802 - val_loss: 0.2077 - val_accuracy: 0.9614
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4816974e-02
 -1.1518358e-02 -7.6088663e-03]
Sparsity at: 0.609564237415477
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9796 - val_loss: 0.1904 - val_accuracy: 0.9647
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5567947e-02
 -2.0023316e-02 -1.0892926e-03]
Sparsity at: 0.609564237415477
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9802 - val_loss: 0.2020 - val_accuracy: 0.9602
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.0700304e-03
 -1.1783031e-02 -1.0664175e-02]
Sparsity at: 0.609564237415477
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9795 - val_loss: 0.2073 - val_accuracy: 0.9589
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5626241e-02
 -9.4826575e-03  5.9962687e-03]
Sparsity at: 0.609564237415477
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9807 - val_loss: 0.1922 - val_accuracy: 0.9644
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.7109467e-02
 -1.6862454e-02  2.9079670e-03]
Sparsity at: 0.609564237415477
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9806 - val_loss: 0.1842 - val_accuracy: 0.9679
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.1482121e-02
 -1.2760659e-02  1.0351932e-02]
Sparsity at: 0.609564237415477
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9803 - val_loss: 0.1878 - val_accuracy: 0.9643
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.7338617e-02
 -1.2926840e-02  8.9103477e-03]
Sparsity at: 0.609564237415477
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9801 - val_loss: 0.1984 - val_accuracy: 0.9616
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6082766e-02
 -6.8079256e-03  1.3587286e-03]
Sparsity at: 0.609564237415477
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9791 - val_loss: 0.1841 - val_accuracy: 0.9655
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ... -1.33801885e-02
 -9.66754742e-04 -1.67849252e-03]
Sparsity at: 0.609564237415477
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9803 - val_loss: 0.1868 - val_accuracy: 0.9663
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1817823e-02
 -7.4084168e-03 -4.1128765e-03]
Sparsity at: 0.609564237415477
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9810 - val_loss: 0.1896 - val_accuracy: 0.9651
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.6747297e-03
 -7.8829834e-03 -1.4410415e-03]
Sparsity at: 0.609564237415477
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9802 - val_loss: 0.2034 - val_accuracy: 0.9606
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.7388102e-03
 -1.1696183e-02 -4.7276444e-03]
Sparsity at: 0.609564237415477
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9805 - val_loss: 0.1947 - val_accuracy: 0.9628
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.0497353e-02
 -1.1285525e-02 -1.0306014e-02]
Sparsity at: 0.609564237415477
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9814 - val_loss: 0.2067 - val_accuracy: 0.9573
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.6336232e-04
 -1.1688329e-02 -1.2427630e-02]
Sparsity at: 0.609564237415477
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9802 - val_loss: 0.2315 - val_accuracy: 0.9477
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.9722578e-03
 -1.3162703e-02 -7.8375265e-03]
Sparsity at: 0.609564237415477
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9803 - val_loss: 0.2116 - val_accuracy: 0.9568
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.2654590e-03
 -1.2726340e-02 -2.5439321e-03]
Sparsity at: 0.609564237415477
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9807 - val_loss: 0.2018 - val_accuracy: 0.9608
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.7463215e-03
 -1.8807502e-02  1.4351456e-03]
Sparsity at: 0.609564237415477
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9811 - val_loss: 0.2265 - val_accuracy: 0.9535
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.4009713e-03
 -2.3617532e-02  4.5516384e-03]
Sparsity at: 0.609564237415477
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9809 - val_loss: 0.1988 - val_accuracy: 0.9619
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.9528639e-03
 -2.0288454e-02  1.5092628e-03]
Sparsity at: 0.609564237415477
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9793 - val_loss: 0.1737 - val_accuracy: 0.9680
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5291902e-03
 -2.1310160e-02  3.9539309e-03]
Sparsity at: 0.609564237415477
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9808 - val_loss: 0.1900 - val_accuracy: 0.9645
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.5850094e-03
 -2.4055630e-02  1.0568907e-02]
Sparsity at: 0.609564237415477
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 3.143354577822847e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.0001877470280517192
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.3452
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [0. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.03581394905041746
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 181s 12ms/step - loss: 0.1336 - accuracy: 0.9799 - val_loss: 0.2019 - val_accuracy: 0.9608
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.4823935e-04
 -2.3401303e-02  4.6648826e-03]
Sparsity at: 0.609564237415477
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9812 - val_loss: 0.2020 - val_accuracy: 0.9604
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.8889233e-05
 -1.8374164e-02  6.2184692e-03]
Sparsity at: 0.609564237415477
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9807 - val_loss: 0.1869 - val_accuracy: 0.9655
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.5065197e-03
 -1.9441303e-02  5.5381665e-03]
Sparsity at: 0.609564237415477
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9812 - val_loss: 0.2105 - val_accuracy: 0.9589
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.6966999e-03
 -1.9471336e-02  5.4654172e-03]
Sparsity at: 0.609564237415477
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9816 - val_loss: 0.1755 - val_accuracy: 0.9683
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.1432833e-03
 -2.2547727e-02  4.8612226e-03]
Sparsity at: 0.609564237415477
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1322 - accuracy: 0.9808 - val_loss: 0.1912 - val_accuracy: 0.9636
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.6854007e-03
 -2.9992647e-02 -2.2462234e-03]
Sparsity at: 0.609564237415477
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9807 - val_loss: 0.1757 - val_accuracy: 0.9680
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.7667999e-03
 -2.1794068e-02 -6.7512627e-04]
Sparsity at: 0.609564237415477
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9811 - val_loss: 0.2024 - val_accuracy: 0.9590
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.9457803e-03
 -2.3391439e-02 -1.6904756e-03]
Sparsity at: 0.609564237415477
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9808 - val_loss: 0.1784 - val_accuracy: 0.9668
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.9724092e-03
 -1.7304018e-02  1.4380713e-03]
Sparsity at: 0.609564237415477
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9810 - val_loss: 0.1874 - val_accuracy: 0.9667
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.3984419e-03
 -2.0233029e-02  8.3106281e-03]
Sparsity at: 0.609564237415477
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9807 - val_loss: 0.2073 - val_accuracy: 0.9596
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0145960e-02
 -2.0343944e-02  1.3624638e-02]
Sparsity at: 0.609564237415477
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9811 - val_loss: 0.1789 - val_accuracy: 0.9670
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.0046390e-03
 -1.7215738e-02  1.5086930e-02]
Sparsity at: 0.609564237415477
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9811 - val_loss: 0.1902 - val_accuracy: 0.9637
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.0807937e-03
 -1.5181925e-02  1.1114713e-02]
Sparsity at: 0.609564237415477
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9805 - val_loss: 0.1711 - val_accuracy: 0.9701
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.5210806e-03
 -2.4222001e-02  1.0298101e-02]
Sparsity at: 0.609564237415477
Epoch 115/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9815 - val_loss: 0.1973 - val_accuracy: 0.9610
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.3107986e-03
 -2.2243725e-02  1.0053537e-02]
Sparsity at: 0.609564237415477
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9801 - val_loss: 0.1874 - val_accuracy: 0.9640
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.3004605e-02
 -2.7334949e-02  1.0722823e-03]
Sparsity at: 0.609564237415477
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9811 - val_loss: 0.1782 - val_accuracy: 0.9681
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  1.03437435e-02
 -2.56974343e-02  1.09554990e-03]
Sparsity at: 0.609564237415477
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9803 - val_loss: 0.2044 - val_accuracy: 0.9602
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.7269507e-03
 -2.8701954e-02 -4.6813823e-03]
Sparsity at: 0.609564237415477
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9800 - val_loss: 0.1990 - val_accuracy: 0.9617
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.2997838e-03
 -1.4830215e-02 -1.7447248e-02]
Sparsity at: 0.609564237415477
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9814 - val_loss: 0.2033 - val_accuracy: 0.9612
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.3973093e-03
 -1.8140521e-02 -1.9303140e-03]
Sparsity at: 0.609564237415477
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9800 - val_loss: 0.1821 - val_accuracy: 0.9689
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0888088e-02
 -1.7075807e-02 -5.5667997e-04]
Sparsity at: 0.609564237415477
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9807 - val_loss: 0.2255 - val_accuracy: 0.9517
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.2573063e-02
 -2.0306544e-02 -2.9153950e-03]
Sparsity at: 0.609564237415477
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9810 - val_loss: 0.1816 - val_accuracy: 0.9639
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.4670531e-03
 -2.3215013e-02  4.2451546e-03]
Sparsity at: 0.609564237415477
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9813 - val_loss: 0.2023 - val_accuracy: 0.9595
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.4331760e-03
 -1.8110007e-02  3.7053684e-03]
Sparsity at: 0.609564237415477
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9811 - val_loss: 0.2223 - val_accuracy: 0.9545
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0646124e-02
 -1.9441204e-02 -4.5627235e-03]
Sparsity at: 0.609564237415477
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9814 - val_loss: 0.1882 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5151498e-02
 -2.0334542e-02 -1.2307422e-02]
Sparsity at: 0.609564237415477
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9809 - val_loss: 0.2036 - val_accuracy: 0.9608
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.4600339e-02
 -8.7510487e-03 -1.2940705e-02]
Sparsity at: 0.609564237415477
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9812 - val_loss: 0.1869 - val_accuracy: 0.9635
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8031683e-02
 -1.7309848e-02 -9.7919749e-03]
Sparsity at: 0.609564237415477
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9803 - val_loss: 0.1950 - val_accuracy: 0.9629
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.1298809e-03
 -9.9104326e-03 -2.0670968e-03]
Sparsity at: 0.609564237415477
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9816 - val_loss: 0.1865 - val_accuracy: 0.9661
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.1596423e-03
 -9.3852123e-03  2.2791282e-03]
Sparsity at: 0.609564237415477
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9821 - val_loss: 0.1914 - val_accuracy: 0.9646
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.6036219e-03
 -7.3813237e-03  7.4062804e-03]
Sparsity at: 0.609564237415477
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9804 - val_loss: 0.1770 - val_accuracy: 0.9685
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.4174972e-04
 -1.0090131e-02  6.4816782e-03]
Sparsity at: 0.609564237415477
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9813 - val_loss: 0.1863 - val_accuracy: 0.9641
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.7401869e-03
 -1.5400289e-02  8.1983460e-03]
Sparsity at: 0.609564237415477
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9806 - val_loss: 0.1790 - val_accuracy: 0.9695
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.9802860e-03
 -2.4115946e-03 -8.4511638e-03]
Sparsity at: 0.609564237415477
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9818 - val_loss: 0.1879 - val_accuracy: 0.9644
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.2199311e-03
 -8.0453418e-03  1.0639328e-02]
Sparsity at: 0.609564237415477
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9807 - val_loss: 0.1842 - val_accuracy: 0.9660
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.0330224e-03
 -6.7035807e-03 -1.1171241e-03]
Sparsity at: 0.609564237415477
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9813 - val_loss: 0.2048 - val_accuracy: 0.9587
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1704954e-02
 -3.7501610e-03 -6.6961451e-03]
Sparsity at: 0.609564237415477
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9810 - val_loss: 0.1840 - val_accuracy: 0.9651
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.7679686e-02
 -8.5701933e-03 -7.8159301e-03]
Sparsity at: 0.609564237415477
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9815 - val_loss: 0.1936 - val_accuracy: 0.9631
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5709355e-02
 -1.5282063e-02 -1.1058649e-02]
Sparsity at: 0.609564237415477
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9802 - val_loss: 0.2136 - val_accuracy: 0.9569
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9556612e-02
 -1.2737467e-02 -9.5474264e-03]
Sparsity at: 0.609564237415477
Epoch 141/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9812 - val_loss: 0.1848 - val_accuracy: 0.9665
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5110875e-02
 -1.4725075e-02 -1.3271408e-02]
Sparsity at: 0.609564237415477
Epoch 142/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9805 - val_loss: 0.1909 - val_accuracy: 0.9633
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.6393173e-02
 -4.2465837e-03 -4.7134715e-03]
Sparsity at: 0.609564237415477
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9823 - val_loss: 0.2004 - val_accuracy: 0.9626
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9536164e-02
 -9.0902001e-03 -7.4065295e-03]
Sparsity at: 0.609564237415477
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9805 - val_loss: 0.1898 - val_accuracy: 0.9639
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1743926e-02
 -3.2926616e-03 -4.1449949e-04]
Sparsity at: 0.609564237415477
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9799 - val_loss: 0.1891 - val_accuracy: 0.9642
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.2724106e-02
 -7.9840105e-03 -4.0265890e-03]
Sparsity at: 0.609564237415477
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9813 - val_loss: 0.1984 - val_accuracy: 0.9625
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1116811e-02
 -2.6243231e-03 -8.8118725e-03]
Sparsity at: 0.609564237415477
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9814 - val_loss: 0.1738 - val_accuracy: 0.9693
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0035450e-02
 -5.5858968e-03 -4.2165699e-03]
Sparsity at: 0.609564237415477
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9805 - val_loss: 0.1900 - val_accuracy: 0.9653
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1338446e-02
 -1.0841298e-02 -4.2213215e-03]
Sparsity at: 0.609564237415477
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9814 - val_loss: 0.1887 - val_accuracy: 0.9650
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.5217479e-03
 -8.2926173e-03 -5.6361225e-03]
Sparsity at: 0.609564237415477
Epoch 150/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9813 - val_loss: 0.1778 - val_accuracy: 0.9665
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.5717590e-03
 -1.2287069e-02 -1.3745189e-02]
Sparsity at: 0.609564237415477
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 4.286602193017832e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.0006942666406397138
Thresholhold 4.403433062248983e-34
Using suggest threshold.
Applying new mask
Percentage zeros 0.49263334
tf.Tensor(
[[1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.046016209979620415
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 173s 12ms/step - loss: 0.1238 - accuracy: 0.9820 - val_loss: 0.1909 - val_accuracy: 0.9634
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5519885e-02
 -9.4163073e-03 -5.7254005e-03]
Sparsity at: 0.6261795642374155
Epoch 152/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1311 - accuracy: 0.9799 - val_loss: 0.1908 - val_accuracy: 0.9620
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5231688e-02
 -3.2589317e-03 -5.2067703e-03]
Sparsity at: 0.6261795642374155
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9814 - val_loss: 0.2210 - val_accuracy: 0.9554
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5006252e-02
  9.2909153e-04 -1.1809093e-02]
Sparsity at: 0.6261795642374155
Epoch 154/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9803 - val_loss: 0.1957 - val_accuracy: 0.9618
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.7560357e-02
 -4.0490050e-03 -4.7095772e-03]
Sparsity at: 0.6261795642374155
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9813 - val_loss: 0.2034 - val_accuracy: 0.9592
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.6040945e-02
 -8.1000123e-03 -1.6245890e-02]
Sparsity at: 0.6261795642374155
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9801 - val_loss: 0.2014 - val_accuracy: 0.9616
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.0611338e-02
 -8.1535401e-03 -1.6556516e-02]
Sparsity at: 0.6261795642374155
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9816 - val_loss: 0.1921 - val_accuracy: 0.9625
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.4411822e-02
  4.8476253e-03 -2.3491455e-02]
Sparsity at: 0.6261795642374155
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1285 - accuracy: 0.9809 - val_loss: 0.1705 - val_accuracy: 0.9695
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  1.01361685e-02
  2.61429953e-03 -1.35191055e-02]
Sparsity at: 0.6261795642374155
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9809 - val_loss: 0.1960 - val_accuracy: 0.9618
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.4342676e-03
  5.6050955e-03 -9.1069788e-03]
Sparsity at: 0.6261795642374155
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9815 - val_loss: 0.1799 - val_accuracy: 0.9657
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.8714428e-03
 -5.7288347e-04 -2.2965923e-02]
Sparsity at: 0.6261795642374155
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9803 - val_loss: 0.1837 - val_accuracy: 0.9680
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.6158879e-02
  9.2642391e-03 -1.3545258e-02]
Sparsity at: 0.6261795642374155
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9813 - val_loss: 0.1823 - val_accuracy: 0.9662
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.2168765e-03
  3.2511912e-03 -1.1982426e-02]
Sparsity at: 0.6261795642374155
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9806 - val_loss: 0.1931 - val_accuracy: 0.9643
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.3157080e-03
  3.8613491e-03 -1.0941069e-02]
Sparsity at: 0.6261795642374155
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1270 - accuracy: 0.9814 - val_loss: 0.1812 - val_accuracy: 0.9660
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.5654183e-03
 -1.0532666e-03 -1.8705919e-02]
Sparsity at: 0.6261795642374155
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9804 - val_loss: 0.2060 - val_accuracy: 0.9604
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.1441427e-03
  1.8915758e-02 -1.4099847e-02]
Sparsity at: 0.6261795642374155
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9813 - val_loss: 0.1806 - val_accuracy: 0.9667
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.2066663e-03
  4.6999636e-03 -1.3829215e-03]
Sparsity at: 0.6261795642374155
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9814 - val_loss: 0.1732 - val_accuracy: 0.9690
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.4425271e-03
  3.0999986e-04 -3.3983466e-04]
Sparsity at: 0.6261795642374155
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9819 - val_loss: 0.1776 - val_accuracy: 0.9673
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.4769482e-03
  1.7992296e-04 -4.5202480e-04]
Sparsity at: 0.6261795642374155
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9812 - val_loss: 0.1806 - val_accuracy: 0.9642
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.8535316e-04
  1.6661637e-02 -4.6046562e-03]
Sparsity at: 0.6261795642374155
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9802 - val_loss: 0.1748 - val_accuracy: 0.9679
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.3108786e-03
  5.8515370e-03 -3.8334096e-03]
Sparsity at: 0.6261795642374155
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9804 - val_loss: 0.1983 - val_accuracy: 0.9634
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.4222871e-03
  8.6901216e-03 -7.2909202e-03]
Sparsity at: 0.6261795642374155
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9799 - val_loss: 0.1992 - val_accuracy: 0.9613
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.4708571e-03
  8.6650234e-03 -1.3376051e-02]
Sparsity at: 0.6261795642374155
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9822 - val_loss: 0.1830 - val_accuracy: 0.9656
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.9831867e-03
  5.7156947e-03 -9.5424950e-03]
Sparsity at: 0.62618332081142
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9808 - val_loss: 0.1952 - val_accuracy: 0.9633
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.7980115e-03
  6.2589627e-03 -9.4819246e-03]
Sparsity at: 0.62618332081142
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9810 - val_loss: 0.1999 - val_accuracy: 0.9595
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.2533810e-03
  8.6162658e-03 -1.6495805e-02]
Sparsity at: 0.62618332081142
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9804 - val_loss: 0.1872 - val_accuracy: 0.9640
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.5227003e-03
  1.4301439e-02 -2.1752520e-02]
Sparsity at: 0.62618332081142
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9814 - val_loss: 0.2079 - val_accuracy: 0.9577
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2052265e-03
  1.8723499e-02 -1.7717831e-02]
Sparsity at: 0.62618332081142
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9799 - val_loss: 0.2121 - val_accuracy: 0.9565
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.8268504e-03
  2.0275349e-02 -1.1362681e-02]
Sparsity at: 0.62618332081142
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9814 - val_loss: 0.1906 - val_accuracy: 0.9638
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.2024892e-03
  2.5076723e-02 -1.3171487e-02]
Sparsity at: 0.62618332081142
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9812 - val_loss: 0.1834 - val_accuracy: 0.9664
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.1486600e-03
  2.2469476e-02 -1.9878138e-02]
Sparsity at: 0.62618332081142
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9805 - val_loss: 0.1881 - val_accuracy: 0.9657
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.4482042e-03
  1.6082549e-02 -1.5716419e-02]
Sparsity at: 0.62618332081142
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9804 - val_loss: 0.2043 - val_accuracy: 0.9582
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.4179320e-03
  1.4549229e-02 -2.4454787e-02]
Sparsity at: 0.62618332081142
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9819 - val_loss: 0.2166 - val_accuracy: 0.9552
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.9060830e-03
  1.5420340e-02 -2.3104817e-02]
Sparsity at: 0.62618332081142
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9813 - val_loss: 0.1682 - val_accuracy: 0.9695
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.0513197e-04
  1.4882351e-02 -2.1866664e-02]
Sparsity at: 0.62618332081142
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1252 - accuracy: 0.9817 - val_loss: 0.1895 - val_accuracy: 0.9634
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.1987975e-03
  1.2093968e-02 -3.0487541e-02]
Sparsity at: 0.62618332081142
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1263 - accuracy: 0.9815 - val_loss: 0.1826 - val_accuracy: 0.9669
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.2185082e-03
  1.6403848e-02 -2.5547681e-02]
Sparsity at: 0.62618332081142
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9818 - val_loss: 0.2186 - val_accuracy: 0.9565
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1358790e-03
  1.2848096e-02 -2.0030247e-02]
Sparsity at: 0.62618332081142
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9808 - val_loss: 0.1914 - val_accuracy: 0.9631
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.6328462e-04
  2.1268273e-02 -1.3198181e-02]
Sparsity at: 0.62618332081142
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1265 - accuracy: 0.9812 - val_loss: 0.1843 - val_accuracy: 0.9655
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  3.41131189e-03
  1.49679985e-02 -1.98633596e-02]
Sparsity at: 0.62618332081142
Epoch 190/500
235/235 [==============================] - 3s 12ms/step - loss: 0.1239 - accuracy: 0.9818 - val_loss: 0.1741 - val_accuracy: 0.9702
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6764369e-03
  2.2871250e-02 -1.8276498e-02]
Sparsity at: 0.62618332081142
Epoch 191/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1268 - accuracy: 0.9810 - val_loss: 0.1949 - val_accuracy: 0.9609
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1533813e-03
  2.5014626e-02 -1.1585600e-02]
Sparsity at: 0.62618332081142
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9804 - val_loss: 0.2037 - val_accuracy: 0.9610
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.3686553e-04
  2.8828869e-02 -1.5381028e-02]
Sparsity at: 0.62618332081142
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9815 - val_loss: 0.1783 - val_accuracy: 0.9658
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.3907274e-04
  1.9462006e-02 -1.0560043e-02]
Sparsity at: 0.62618332081142
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9809 - val_loss: 0.1785 - val_accuracy: 0.9676
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.0319581e-03
  9.5394058e-03 -2.2121089e-02]
Sparsity at: 0.62618332081142
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9811 - val_loss: 0.2113 - val_accuracy: 0.9593
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.2561303e-02
  1.4265097e-02 -1.9453447e-02]
Sparsity at: 0.62618332081142
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1257 - accuracy: 0.9816 - val_loss: 0.1694 - val_accuracy: 0.9694
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9396411e-03
  2.2716163e-02 -1.5544225e-02]
Sparsity at: 0.62618332081142
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9805 - val_loss: 0.2196 - val_accuracy: 0.9552
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.8276341e-03
  1.9026967e-02 -1.4434419e-02]
Sparsity at: 0.62618332081142
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9818 - val_loss: 0.1949 - val_accuracy: 0.9624
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.4475386e-03
  1.0460013e-02 -8.3177444e-03]
Sparsity at: 0.62618332081142
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1257 - accuracy: 0.9809 - val_loss: 0.1899 - val_accuracy: 0.9640
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.0418394e-04
  1.7433405e-02 -1.4085965e-02]
Sparsity at: 0.62618332081142
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1259 - accuracy: 0.9808 - val_loss: 0.1827 - val_accuracy: 0.9641
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.6393368e-03
  2.1374164e-02 -1.1409851e-02]
Sparsity at: 0.62618332081142
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 5.425175404986997e-34
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.004295962923096641
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.49263334
tf.Tensor(
[[1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.05768549657616262
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 172s 12ms/step - loss: 0.1241 - accuracy: 0.9822 - val_loss: 0.1918 - val_accuracy: 0.9636
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.3261542e-03
  1.9397806e-02 -9.1561060e-03]
Sparsity at: 0.62618332081142
Epoch 202/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1289 - accuracy: 0.9804 - val_loss: 0.1930 - val_accuracy: 0.9619
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.0386746e-03
  1.7317858e-02 -1.2023304e-02]
Sparsity at: 0.62618332081142
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9817 - val_loss: 0.1944 - val_accuracy: 0.9608
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5512047e-02
  1.0508889e-02 -8.6801825e-03]
Sparsity at: 0.62618332081142
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1258 - accuracy: 0.9808 - val_loss: 0.1853 - val_accuracy: 0.9641
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.3857188e-03
  1.4301135e-02 -1.3361643e-02]
Sparsity at: 0.62618332081142
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9819 - val_loss: 0.2109 - val_accuracy: 0.9582
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.7737503e-03
  5.8528553e-03 -1.1496742e-02]
Sparsity at: 0.62618332081142
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9804 - val_loss: 0.2018 - val_accuracy: 0.9619
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.1104310e-03
  1.0868287e-02 -7.7997963e-03]
Sparsity at: 0.62618332081142
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9817 - val_loss: 0.1722 - val_accuracy: 0.9680
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.5278788e-03
  4.3465844e-03 -6.4720227e-03]
Sparsity at: 0.62618332081142
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9812 - val_loss: 0.2055 - val_accuracy: 0.9578
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.0254121e-04
  7.5755953e-03 -9.6710622e-03]
Sparsity at: 0.62618332081142
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9817 - val_loss: 0.2088 - val_accuracy: 0.9590
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.2469594e-03
  3.0613991e-03 -4.3878113e-03]
Sparsity at: 0.62618332081142
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9813 - val_loss: 0.1696 - val_accuracy: 0.9707
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.0439180e-03
  1.2659993e-04  2.0115450e-03]
Sparsity at: 0.62618332081142
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9815 - val_loss: 0.2015 - val_accuracy: 0.9590
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.8356230e-03
  3.3753214e-03 -6.5748538e-03]
Sparsity at: 0.62618332081142
Epoch 212/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9816 - val_loss: 0.2061 - val_accuracy: 0.9594
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.3798134e-03
  1.0486190e-03  4.3362859e-03]
Sparsity at: 0.62618332081142
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9820 - val_loss: 0.1762 - val_accuracy: 0.9681
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  1.17129332e-03
  3.42493202e-03 -1.11116385e-02]
Sparsity at: 0.62618332081142
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9805 - val_loss: 0.1959 - val_accuracy: 0.9623
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.5408812e-03
  6.0132970e-03 -1.3358512e-02]
Sparsity at: 0.62618332081142
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9814 - val_loss: 0.1880 - val_accuracy: 0.9640
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.6388051e-03
  1.3041613e-02 -9.4592283e-03]
Sparsity at: 0.62618332081142
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9820 - val_loss: 0.2253 - val_accuracy: 0.9557
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.6695828e-03
  7.7870898e-03 -1.4348381e-02]
Sparsity at: 0.62618332081142
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9811 - val_loss: 0.1836 - val_accuracy: 0.9674
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.8147050e-03
 -1.6465879e-03 -8.8195875e-03]
Sparsity at: 0.62618332081142
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9821 - val_loss: 0.1962 - val_accuracy: 0.9627
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.2033186e-04
 -3.7436301e-05 -1.7933849e-02]
Sparsity at: 0.62618332081142
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9822 - val_loss: 0.2013 - val_accuracy: 0.9624
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.2709750e-03
 -2.6593131e-03 -1.7878983e-02]
Sparsity at: 0.62618332081142
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9819 - val_loss: 0.1914 - val_accuracy: 0.9609
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.6397514e-04
  9.8603533e-04 -1.7742151e-02]
Sparsity at: 0.62618332081142
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9818 - val_loss: 0.2112 - val_accuracy: 0.9584
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.4710206e-03
 -1.4860501e-03 -1.7573990e-02]
Sparsity at: 0.62618332081142
Epoch 222/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1296 - accuracy: 0.9805 - val_loss: 0.2092 - val_accuracy: 0.9605
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.4352625e-02
  4.5264703e-03 -1.3222669e-02]
Sparsity at: 0.62618332081142
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9824 - val_loss: 0.2125 - val_accuracy: 0.9581
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0451489e-02
  4.1457117e-03 -4.7546444e-03]
Sparsity at: 0.62618332081142
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9813 - val_loss: 0.2047 - val_accuracy: 0.9602
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0195646e-02
 -3.2532644e-03 -1.0184709e-02]
Sparsity at: 0.62618332081142
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9807 - val_loss: 0.1922 - val_accuracy: 0.9652
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  1.07967425e-02
  1.56634522e-03 -8.91325437e-03]
Sparsity at: 0.62618332081142
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9819 - val_loss: 0.2015 - val_accuracy: 0.9591
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.7093113e-03
 -5.9368419e-03  5.8954568e-03]
Sparsity at: 0.62618332081142
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9821 - val_loss: 0.2153 - val_accuracy: 0.9588
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0180203e-02
 -2.8415869e-03 -7.4210786e-04]
Sparsity at: 0.62618332081142
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9811 - val_loss: 0.2141 - val_accuracy: 0.9560
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.2034571e-03
 -8.9245511e-04 -2.0890515e-02]
Sparsity at: 0.62618332081142
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9821 - val_loss: 0.2160 - val_accuracy: 0.9571
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.6546267e-03
 -7.3704490e-04 -2.2584060e-02]
Sparsity at: 0.62618332081142
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9815 - val_loss: 0.1796 - val_accuracy: 0.9680
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.8043345e-03
 -1.4098302e-02 -6.0914732e-03]
Sparsity at: 0.62618332081142
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9811 - val_loss: 0.1780 - val_accuracy: 0.9672
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.5598390e-03
 -1.6809989e-02 -2.4797142e-04]
Sparsity at: 0.62618332081142
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9818 - val_loss: 0.2093 - val_accuracy: 0.9591
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.6214581e-02
 -1.3725693e-02  4.2767334e-03]
Sparsity at: 0.62618332081142
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9808 - val_loss: 0.1850 - val_accuracy: 0.9665
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1406381e-02
 -1.3063782e-03  7.0335576e-04]
Sparsity at: 0.62618332081142
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9801 - val_loss: 0.1816 - val_accuracy: 0.9665
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.2443610e-03
  5.2379095e-04  7.3178867e-03]
Sparsity at: 0.62618332081142
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1252 - accuracy: 0.9810 - val_loss: 0.1929 - val_accuracy: 0.9635
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.9272211e-03
  2.3080837e-03 -7.4494244e-03]
Sparsity at: 0.62618332081142
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9817 - val_loss: 0.2098 - val_accuracy: 0.9595
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  1.03148995e-02
 -3.30290012e-03 -3.74076422e-03]
Sparsity at: 0.62618332081142
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9804 - val_loss: 0.1933 - val_accuracy: 0.9639
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1077386e-03
 -3.5976588e-03 -2.0820191e-02]
Sparsity at: 0.62618332081142
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9821 - val_loss: 0.1740 - val_accuracy: 0.9677
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.6579880e-03
 -1.4505644e-03 -1.0685987e-02]
Sparsity at: 0.62618332081142
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9822 - val_loss: 0.1929 - val_accuracy: 0.9615
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.7821847e-03
  9.7606806e-03 -4.7083753e-03]
Sparsity at: 0.62618332081142
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9810 - val_loss: 0.1888 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.8382086e-03
  5.0148163e-03 -1.2554180e-02]
Sparsity at: 0.62618332081142
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9802 - val_loss: 0.2015 - val_accuracy: 0.9606
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.1008080e-03
  2.6247201e-03 -1.4206396e-02]
Sparsity at: 0.62618332081142
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9808 - val_loss: 0.1818 - val_accuracy: 0.9671
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.3446617e-03
 -1.7197767e-03 -1.6599132e-02]
Sparsity at: 0.62618332081142
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9818 - val_loss: 0.1874 - val_accuracy: 0.9624
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.1932829e-04
  1.9987579e-02 -1.2273332e-02]
Sparsity at: 0.62618332081142
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9816 - val_loss: 0.1947 - val_accuracy: 0.9634
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.1086864e-03
  4.4933497e-03 -1.1754180e-03]
Sparsity at: 0.62618332081142
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1247 - accuracy: 0.9814 - val_loss: 0.1871 - val_accuracy: 0.9646
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.6874881e-03
  1.3728549e-03 -1.0242327e-02]
Sparsity at: 0.62618332081142
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1258 - accuracy: 0.9808 - val_loss: 0.2025 - val_accuracy: 0.9603
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.3569218e-03
  7.2018970e-03 -4.4557173e-03]
Sparsity at: 0.62618332081142
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9812 - val_loss: 0.2012 - val_accuracy: 0.9596
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.0460682e-03
  4.2725466e-03 -1.5053412e-02]
Sparsity at: 0.62618332081142
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9807 - val_loss: 0.1716 - val_accuracy: 0.9690
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.9643390e-05
  1.2978757e-03 -5.5835755e-03]
Sparsity at: 0.62618332081142
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9805 - val_loss: 0.2177 - val_accuracy: 0.9573
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.3013643e-03
  2.4729588e-03 -1.7711516e-02]
Sparsity at: 0.62618332081142
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1244 - accuracy: 0.9813 - val_loss: 0.1934 - val_accuracy: 0.9619
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.8091870e-03
  1.0649992e-02 -1.1276876e-02]
Sparsity at: 0.62618332081142
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.0016325676645354698
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.013562465444595162
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.49263334
tf.Tensor(
[[1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.06700058851578738
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 176s 12ms/step - loss: 0.1241 - accuracy: 0.9813 - val_loss: 0.1865 - val_accuracy: 0.9643
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ... -1.36007834e-02
  1.03300745e-02 -1.20017789e-02]
Sparsity at: 0.62618332081142
Epoch 252/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1227 - accuracy: 0.9819 - val_loss: 0.1952 - val_accuracy: 0.9616
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.0257553e-03
  1.5583335e-02 -1.5734153e-02]
Sparsity at: 0.62618332081142
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9813 - val_loss: 0.2208 - val_accuracy: 0.9537
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.0893583e-03
  4.7535687e-03 -1.1814730e-02]
Sparsity at: 0.62618332081142
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9814 - val_loss: 0.1919 - val_accuracy: 0.9623
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2156360e-04
 -2.1419533e-04 -1.2901199e-02]
Sparsity at: 0.62618332081142
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9814 - val_loss: 0.1900 - val_accuracy: 0.9636
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.3655390e-03
 -1.0269799e-02 -5.2906433e-03]
Sparsity at: 0.62618332081142
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9811 - val_loss: 0.2297 - val_accuracy: 0.9522
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.1479555e-03
 -9.4493385e-03 -3.9317450e-03]
Sparsity at: 0.62618332081142
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9819 - val_loss: 0.2418 - val_accuracy: 0.9508
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.9159542e-03
 -9.0247201e-04 -1.9772795e-03]
Sparsity at: 0.62618332081142
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9808 - val_loss: 0.1921 - val_accuracy: 0.9627
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.9747595e-03
 -2.0622911e-03  1.4803831e-03]
Sparsity at: 0.62618332081142
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9823 - val_loss: 0.1696 - val_accuracy: 0.9702
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9401954e-03
  1.1356978e-03  9.1278035e-04]
Sparsity at: 0.62618332081142
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9817 - val_loss: 0.1919 - val_accuracy: 0.9641
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.0165551e-04
 -1.2073382e-03  1.7780502e-03]
Sparsity at: 0.62618332081142
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1258 - accuracy: 0.9809 - val_loss: 0.2438 - val_accuracy: 0.9490
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.4383984e-04
  1.4280566e-04 -3.7241151e-04]
Sparsity at: 0.62618332081142
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9811 - val_loss: 0.2036 - val_accuracy: 0.9610
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0069177e-03
 -1.9545346e-03  1.8310361e-03]
Sparsity at: 0.62618332081142
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9815 - val_loss: 0.2373 - val_accuracy: 0.9519
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.3566113e-04
 -5.2661251e-04 -2.6152132e-04]
Sparsity at: 0.62618332081142
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9815 - val_loss: 0.2053 - val_accuracy: 0.9601
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.6779182e-03
  2.2994676e-03  7.4217940e-04]
Sparsity at: 0.62618332081142
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9812 - val_loss: 0.2069 - val_accuracy: 0.9620
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3075378e-03
  1.2018655e-04  1.6090585e-04]
Sparsity at: 0.62618332081142
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9813 - val_loss: 0.1802 - val_accuracy: 0.9655
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.5354944e-04
 -1.3296193e-03 -3.3454865e-04]
Sparsity at: 0.62618332081142
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9821 - val_loss: 0.1782 - val_accuracy: 0.9666
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.8861600e-04
 -3.3175960e-04  3.0204447e-03]
Sparsity at: 0.62618332081142
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9815 - val_loss: 0.2057 - val_accuracy: 0.9570
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.6831126e-04
 -7.8248733e-04  3.2432235e-03]
Sparsity at: 0.62618332081142
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9818 - val_loss: 0.2239 - val_accuracy: 0.9524
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.2685211e-04
  1.1002128e-03  7.9990551e-04]
Sparsity at: 0.62618332081142
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9819 - val_loss: 0.2065 - val_accuracy: 0.9591
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5090625e-03
 -2.1318310e-04 -4.4188061e-04]
Sparsity at: 0.62618332081142
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9802 - val_loss: 0.2002 - val_accuracy: 0.9639
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.9291797e-04
  7.9376285e-04  6.5323984e-04]
Sparsity at: 0.62618332081142
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1244 - accuracy: 0.9811 - val_loss: 0.1835 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0484312e-04
  1.2608478e-03  4.6374853e-04]
Sparsity at: 0.62618332081142
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9818 - val_loss: 0.2055 - val_accuracy: 0.9600
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.0306240e-04
  5.5919634e-04  1.4188844e-03]
Sparsity at: 0.62618332081142
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1285 - accuracy: 0.9800 - val_loss: 0.1713 - val_accuracy: 0.9679
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.2447572e-05
  7.8261219e-05  6.5835548e-04]
Sparsity at: 0.62618332081142
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9818 - val_loss: 0.2013 - val_accuracy: 0.9594
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.0110240e-05
 -3.3821564e-04 -2.7876886e-05]
Sparsity at: 0.62618332081142
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9815 - val_loss: 0.2113 - val_accuracy: 0.9566
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5102809e-04
  7.7305587e-05 -4.2653430e-04]
Sparsity at: 0.62618332081142
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1243 - accuracy: 0.9811 - val_loss: 0.1948 - val_accuracy: 0.9615
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ... -1.03467784e-04
  4.70967789e-05  1.82280797e-04]
Sparsity at: 0.62618332081142
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9807 - val_loss: 0.1754 - val_accuracy: 0.9696
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.4890644e-05
 -1.8937873e-05 -1.1591794e-05]
Sparsity at: 0.62618332081142
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1259 - accuracy: 0.9808 - val_loss: 0.1851 - val_accuracy: 0.9651
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8048511e-05
 -1.5665177e-05 -1.0994325e-05]
Sparsity at: 0.62618332081142
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9811 - val_loss: 0.1812 - val_accuracy: 0.9650
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.6204299e-06
 -8.9500300e-06  8.7827866e-06]
Sparsity at: 0.62618332081142
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9812 - val_loss: 0.1878 - val_accuracy: 0.9640
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.3256264e-06
 -4.4084408e-07 -9.7197500e-08]
Sparsity at: 0.62618332081142
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9817 - val_loss: 0.1859 - val_accuracy: 0.9657
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.7798621e-07
  2.3471124e-08  2.6843422e-08]
Sparsity at: 0.62618332081142
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9821 - val_loss: 0.1964 - val_accuracy: 0.9595
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.6548379e-10
  6.4321561e-09 -4.5154702e-09]
Sparsity at: 0.62618332081142
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9822 - val_loss: 0.1944 - val_accuracy: 0.9624
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.2624654e-12
 -6.0257927e-12  1.5398592e-11]
Sparsity at: 0.62618332081142
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9803 - val_loss: 0.1751 - val_accuracy: 0.9680
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  4.26731201e-14
 -1.21835415e-14 -3.95867556e-14]
Sparsity at: 0.62618332081142
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9817 - val_loss: 0.2062 - val_accuracy: 0.9595
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.3930374e-18
  2.0098957e-17 -2.9837325e-17]
Sparsity at: 0.62618332081142
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9816 - val_loss: 0.1960 - val_accuracy: 0.9627
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.3413001e-20
 -5.7890720e-20 -8.1707568e-21]
Sparsity at: 0.62618332081142
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9817 - val_loss: 0.1897 - val_accuracy: 0.9653
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.4285402e-22
  6.7802552e-22  5.0615827e-23]
Sparsity at: 0.62618332081142
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9820 - val_loss: 0.1945 - val_accuracy: 0.9633
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.7099418e-25
  6.5863895e-25  5.8208823e-25]
Sparsity at: 0.62618332081142
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9806 - val_loss: 0.1957 - val_accuracy: 0.9623
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.0947868e-26
 -1.0519708e-25  3.2940696e-26]
Sparsity at: 0.62618332081142
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1263 - accuracy: 0.9807 - val_loss: 0.1755 - val_accuracy: 0.9666
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.7800488e-27
  1.0427074e-26 -2.4608433e-27]
Sparsity at: 0.62618332081142
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9819 - val_loss: 0.1993 - val_accuracy: 0.9590
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.3013629e-28
  1.3340842e-27 -4.5087233e-28]
Sparsity at: 0.62618332081142
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9820 - val_loss: 0.1691 - val_accuracy: 0.9698
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.5594622e-29
 -1.5813824e-27  1.6074361e-27]
Sparsity at: 0.62618332081142
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9817 - val_loss: 0.1903 - val_accuracy: 0.9622
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.0317882e-28
 -1.9824429e-27  2.2826260e-27]
Sparsity at: 0.6261870773854245
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9822 - val_loss: 0.1816 - val_accuracy: 0.9678
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.6416768e-27
  5.7386118e-27 -3.5225436e-27]
Sparsity at: 0.6261870773854245
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1259 - accuracy: 0.9806 - val_loss: 0.2033 - val_accuracy: 0.9592
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.6979961e-25
  1.3025396e-24 -6.7200398e-25]
Sparsity at: 0.6261870773854245
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9815 - val_loss: 0.1916 - val_accuracy: 0.9613
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6373226e-24
  4.4082466e-24 -5.5904778e-24]
Sparsity at: 0.6261870773854245
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9822 - val_loss: 0.1969 - val_accuracy: 0.9604
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.0260517e-22
  8.0440275e-22 -2.4066397e-22]
Sparsity at: 0.6261870773854245
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9816 - val_loss: 0.1950 - val_accuracy: 0.9591
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  6.18865919e-21
 -1.49034989e-20  1.03465795e-20]
Sparsity at: 0.6261870773854245
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9815 - val_loss: 0.1890 - val_accuracy: 0.9657
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3620004e-17
  2.5221280e-17  6.1532357e-18]
Sparsity at: 0.6261870773854245
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.008926816751783417
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.023912666991317888
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.49263334
tf.Tensor(
[[1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.0773397535943765
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 176s 12ms/step - loss: 0.1219 - accuracy: 0.9816 - val_loss: 0.1964 - val_accuracy: 0.9619
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.2738165e-15
  7.7147939e-15  3.9899339e-16]
Sparsity at: 0.6261870773854245
Epoch 302/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1211 - accuracy: 0.9822 - val_loss: 0.1736 - val_accuracy: 0.9659
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8041202e-11
 -1.5425322e-10  5.8118906e-11]
Sparsity at: 0.6261870773854245
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9807 - val_loss: 0.2200 - val_accuracy: 0.9552
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.9847009e-06
  4.7622884e-06 -8.1369199e-06]
Sparsity at: 0.6261870773854245
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9803 - val_loss: 0.1820 - val_accuracy: 0.9672
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8025470e-03
  3.3352962e-03 -1.7578114e-03]
Sparsity at: 0.6261870773854245
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1252 - accuracy: 0.9806 - val_loss: 0.1891 - val_accuracy: 0.9631
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.6869064e-05
  1.3899141e-03 -7.2385202e-04]
Sparsity at: 0.6261870773854245
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9818 - val_loss: 0.1860 - val_accuracy: 0.9641
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.9526684e-06
 -2.8877095e-05  1.0870221e-04]
Sparsity at: 0.6261870773854245
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9820 - val_loss: 0.2222 - val_accuracy: 0.9552
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.2533588e-04
  1.0467250e-04 -2.1782349e-04]
Sparsity at: 0.6261870773854245
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9812 - val_loss: 0.2039 - val_accuracy: 0.9592
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.8660154e-04
 -7.1801146e-04  5.0761353e-04]
Sparsity at: 0.6261870773854245
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9814 - val_loss: 0.1837 - val_accuracy: 0.9653
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.9951999e-04
 -1.9011059e-03  6.7118858e-04]
Sparsity at: 0.6261870773854245
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9821 - val_loss: 0.1917 - val_accuracy: 0.9637
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.2753360e-06
 -1.0912327e-05 -6.7855799e-06]
Sparsity at: 0.6261870773854245
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9821 - val_loss: 0.1925 - val_accuracy: 0.9606
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.8384658e-06
  1.3288690e-05 -1.5786109e-05]
Sparsity at: 0.6261870773854245
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9818 - val_loss: 0.2087 - val_accuracy: 0.9590
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.8873877e-05
  7.4357486e-05 -1.3246863e-06]
Sparsity at: 0.6261870773854245
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9810 - val_loss: 0.1899 - val_accuracy: 0.9646
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.5693011e-03
  7.6196087e-04 -1.4167209e-03]
Sparsity at: 0.6261870773854245
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9826 - val_loss: 0.1930 - val_accuracy: 0.9611
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.4455798e-05
 -3.3750996e-04  3.1208617e-04]
Sparsity at: 0.6261870773854245
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9825 - val_loss: 0.1884 - val_accuracy: 0.9624
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.3372552e-06
  4.4128861e-05 -3.8046983e-05]
Sparsity at: 0.6261870773854245
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9810 - val_loss: 0.2202 - val_accuracy: 0.9569
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.8588313e-08
  1.1908221e-06 -1.2511344e-07]
Sparsity at: 0.6261870773854245
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9812 - val_loss: 0.1892 - val_accuracy: 0.9635
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4118820e-06
 -1.8683791e-06 -4.2766817e-07]
Sparsity at: 0.6261870773854245
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9821 - val_loss: 0.1835 - val_accuracy: 0.9649
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6742255e-05
 -3.9471192e-06 -2.5228610e-06]
Sparsity at: 0.6261870773854245
Epoch 319/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1254 - accuracy: 0.9808 - val_loss: 0.1833 - val_accuracy: 0.9660
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1417631e-03
 -6.8913342e-04 -1.1971162e-03]
Sparsity at: 0.6261870773854245
Epoch 320/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1244 - accuracy: 0.9813 - val_loss: 0.1773 - val_accuracy: 0.9676
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.7696171e-06
  3.5594916e-04 -4.7122332e-04]
Sparsity at: 0.6261870773854245
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9818 - val_loss: 0.2454 - val_accuracy: 0.9486
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.4351084e-05
  3.4836259e-05 -5.7605641e-05]
Sparsity at: 0.6261870773854245
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9807 - val_loss: 0.1810 - val_accuracy: 0.9657
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8291770e-07
  4.4326944e-07 -3.3495209e-07]
Sparsity at: 0.6261870773854245
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9801 - val_loss: 0.2066 - val_accuracy: 0.9598
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.8536533e-10
  2.4985121e-09 -1.5308512e-09]
Sparsity at: 0.6261870773854245
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9816 - val_loss: 0.2076 - val_accuracy: 0.9577
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.9931994e-13
 -9.4603332e-11  8.7120450e-11]
Sparsity at: 0.6261870773854245
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9822 - val_loss: 0.2154 - val_accuracy: 0.9569
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  1.08697414e-13
  3.74635707e-13 -2.58976461e-13]
Sparsity at: 0.6261870773854245
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9815 - val_loss: 0.1798 - val_accuracy: 0.9653
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.8934929e-15
  2.5194495e-13  2.5717210e-13]
Sparsity at: 0.6261870773854245
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1270 - accuracy: 0.9804 - val_loss: 0.1709 - val_accuracy: 0.9686
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.6717186e-13
  1.3715066e-12 -3.9171945e-13]
Sparsity at: 0.6261870773854245
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9812 - val_loss: 0.2190 - val_accuracy: 0.9571
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.0156342e-11
  3.4194644e-11  3.1782414e-11]
Sparsity at: 0.6261870773854245
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9812 - val_loss: 0.1857 - val_accuracy: 0.9641
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.5535848e-11
 -3.6869674e-11  7.9269855e-12]
Sparsity at: 0.6261870773854245
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9814 - val_loss: 0.1725 - val_accuracy: 0.9685
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.2052292e-10
  1.1079103e-09  2.6166798e-09]
Sparsity at: 0.6261870773854245
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1171 - accuracy: 0.9828 - val_loss: 0.2078 - val_accuracy: 0.9574
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.1187367e-07
  1.6515718e-07  6.1671290e-07]
Sparsity at: 0.6261870773854245
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9816 - val_loss: 0.1882 - val_accuracy: 0.9628
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.1856587e-04
 -1.5323196e-05  1.3223820e-04]
Sparsity at: 0.6261870773854245
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9821 - val_loss: 0.1959 - val_accuracy: 0.9634
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.4338312e-04
 -3.4625272e-04 -1.6054850e-03]
Sparsity at: 0.6261870773854245
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9813 - val_loss: 0.1919 - val_accuracy: 0.9603
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.7541933e-04
  6.4889708e-04 -3.8442525e-04]
Sparsity at: 0.6261870773854245
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9821 - val_loss: 0.1898 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.5082167e-06
 -8.2695269e-06  6.1761648e-06]
Sparsity at: 0.6261870773854245
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9821 - val_loss: 0.1922 - val_accuracy: 0.9616
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.8460225e-08
  3.2468051e-08 -5.9829389e-08]
Sparsity at: 0.6261870773854245
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9818 - val_loss: 0.2019 - val_accuracy: 0.9614
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9019851e-08
 -2.5484098e-07  2.4295900e-07]
Sparsity at: 0.6261870773854245
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9814 - val_loss: 0.2220 - val_accuracy: 0.9523
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.1292232e-08
 -3.5855411e-08  2.5434628e-09]
Sparsity at: 0.6261870773854245
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9826 - val_loss: 0.2040 - val_accuracy: 0.9581
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.9230287e-08
 -2.5214524e-07  1.5871755e-07]
Sparsity at: 0.6261870773854245
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9819 - val_loss: 0.1960 - val_accuracy: 0.9630
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.9071865e-06
 -6.4912333e-06  5.8411615e-08]
Sparsity at: 0.6261870773854245
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9811 - val_loss: 0.1954 - val_accuracy: 0.9626
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.5610355e-04
 -1.8952641e-03  9.0429024e-04]
Sparsity at: 0.6261870773854245
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9816 - val_loss: 0.1748 - val_accuracy: 0.9687
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9881116e-04
  3.2671183e-04 -9.9432786e-05]
Sparsity at: 0.6261870773854245
Epoch 343/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1195 - accuracy: 0.9822 - val_loss: 0.1826 - val_accuracy: 0.9668
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.3091468e-07
 -1.6314901e-06  4.5388042e-06]
Sparsity at: 0.6261870773854245
Epoch 344/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1203 - accuracy: 0.9816 - val_loss: 0.1923 - val_accuracy: 0.9632
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.4757637e-08
  6.1931885e-08 -1.8621314e-08]
Sparsity at: 0.6261870773854245
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9816 - val_loss: 0.2053 - val_accuracy: 0.9627
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9891685e-08
  2.3257110e-08  9.2476640e-09]
Sparsity at: 0.6261870773854245
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9820 - val_loss: 0.1973 - val_accuracy: 0.9633
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.0301445e-08
 -9.2350859e-08  1.1424606e-07]
Sparsity at: 0.6261870773854245
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9824 - val_loss: 0.1960 - val_accuracy: 0.9614
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.9290087e-07
 -1.5072590e-06 -8.1361600e-07]
Sparsity at: 0.6261870773854245
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9809 - val_loss: 0.1741 - val_accuracy: 0.9674
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.6777303e-06
 -3.0742922e-06  2.2224619e-06]
Sparsity at: 0.6261870773854245
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9825 - val_loss: 0.1826 - val_accuracy: 0.9646
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.8931043e-05
 -4.2349572e-04  6.4892709e-05]
Sparsity at: 0.6261870773854245
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9805 - val_loss: 0.1970 - val_accuracy: 0.9631
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.6876666e-03
 -1.1000779e-02  3.8385950e-03]
Sparsity at: 0.6261870773854245
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.016510036329997257
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.032463779063679254
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.49263334
tf.Tensor(
[[1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.08349714231515382
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 241s 12ms/step - loss: 0.1226 - accuracy: 0.9812 - val_loss: 0.2095 - val_accuracy: 0.9587
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.9531977e-04
  5.3317996e-04 -3.2300997e-04]
Sparsity at: 0.6261870773854245
Epoch 352/500
235/235 [==============================] - 3s 12ms/step - loss: 0.1209 - accuracy: 0.9819 - val_loss: 0.1772 - val_accuracy: 0.9678
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.3880455e-07
  1.1467820e-06 -5.6270508e-08]
Sparsity at: 0.6261870773854245
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9808 - val_loss: 0.1756 - val_accuracy: 0.9697
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.0081194e-08
 -6.2615612e-08  3.6974178e-08]
Sparsity at: 0.6261870773854245
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9823 - val_loss: 0.1855 - val_accuracy: 0.9662
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.7609330e-09
  6.8751644e-09 -5.3175215e-09]
Sparsity at: 0.6261870773854245
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9819 - val_loss: 0.1957 - val_accuracy: 0.9606
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4816058e-10
 -1.1987249e-09  5.2496668e-10]
Sparsity at: 0.6261870773854245
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9813 - val_loss: 0.1974 - val_accuracy: 0.9618
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1932011e-10
 -2.5540496e-09 -4.0782278e-10]
Sparsity at: 0.6261870773854245
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9824 - val_loss: 0.1844 - val_accuracy: 0.9660
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3855547e-08
 -3.5656178e-08  7.5758670e-09]
Sparsity at: 0.6261870773854245
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9823 - val_loss: 0.1996 - val_accuracy: 0.9593
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3251099e-07
 -8.6960206e-08  1.3410200e-07]
Sparsity at: 0.6261870773854245
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9811 - val_loss: 0.2020 - val_accuracy: 0.9597
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.0295259e-06
 -2.7657692e-05  1.8722087e-05]
Sparsity at: 0.6261870773854245
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9813 - val_loss: 0.1955 - val_accuracy: 0.9627
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6231709e-03
 -4.3094899e-03  2.4324767e-03]
Sparsity at: 0.6261870773854245
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9815 - val_loss: 0.1947 - val_accuracy: 0.9636
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.5039894e-05
 -8.6653876e-05 -3.9276289e-05]
Sparsity at: 0.6261870773854245
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9816 - val_loss: 0.1873 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.3990757e-06
 -2.6400239e-05  8.3225477e-06]
Sparsity at: 0.6261870773854245
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9819 - val_loss: 0.1920 - val_accuracy: 0.9631
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.3987320e-07
 -1.0562367e-06  2.5708218e-06]
Sparsity at: 0.6261870773854245
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9825 - val_loss: 0.2058 - val_accuracy: 0.9574
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.4820805e-08
 -1.2265305e-07  6.9001878e-08]
Sparsity at: 0.6261870773854245
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9811 - val_loss: 0.1809 - val_accuracy: 0.9666
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.1365485e-08
 -5.8898596e-08  4.0429018e-08]
Sparsity at: 0.6261870773854245
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9829 - val_loss: 0.1827 - val_accuracy: 0.9651
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.5186194e-09
 -7.3361397e-09  1.5544590e-08]
Sparsity at: 0.6261870773854245
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9825 - val_loss: 0.1795 - val_accuracy: 0.9662
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.1708516e-07
 -2.6395290e-07  5.5364168e-08]
Sparsity at: 0.6261870773854245
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9815 - val_loss: 0.1993 - val_accuracy: 0.9609
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.3384391e-07
  1.6957705e-07  2.0388266e-07]
Sparsity at: 0.6261870773854245
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9818 - val_loss: 0.2149 - val_accuracy: 0.9578
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.0046033e-05
 -1.1024142e-05  2.1142159e-06]
Sparsity at: 0.6261870773854245
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9819 - val_loss: 0.1875 - val_accuracy: 0.9642
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.5015073e-03
 -9.2926165e-03  3.6455139e-03]
Sparsity at: 0.6261870773854245
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9817 - val_loss: 0.2097 - val_accuracy: 0.9577
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9895568e-05
  8.5564607e-05  2.4726311e-05]
Sparsity at: 0.6261870773854245
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9821 - val_loss: 0.2126 - val_accuracy: 0.9561
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.4960627e-07
 -9.0040328e-07  7.1273462e-07]
Sparsity at: 0.6261870773854245
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9812 - val_loss: 0.1928 - val_accuracy: 0.9658
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.5436972e-09
  9.1328197e-08 -3.2596944e-08]
Sparsity at: 0.6261870773854245
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9812 - val_loss: 0.2106 - val_accuracy: 0.9583
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.7077399e-10
  4.0826223e-10  1.3260228e-09]
Sparsity at: 0.6261870773854245
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9806 - val_loss: 0.1961 - val_accuracy: 0.9640
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.7743569e-11
  8.0098629e-11 -2.5052777e-10]
Sparsity at: 0.6261870773854245
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9831 - val_loss: 0.2043 - val_accuracy: 0.9585
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.9130689e-11
  1.9376868e-11 -2.1291011e-11]
Sparsity at: 0.6261870773854245
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9807 - val_loss: 0.1999 - val_accuracy: 0.9617
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.4929843e-12
 -1.4658622e-10  8.0232994e-11]
Sparsity at: 0.6261870773854245
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9819 - val_loss: 0.1957 - val_accuracy: 0.9643
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.0127725e-09
 -3.4216001e-09  1.5104137e-09]
Sparsity at: 0.6261870773854245
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9825 - val_loss: 0.1907 - val_accuracy: 0.9632
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.6618918e-08
 -6.5728564e-08 -7.4243488e-08]
Sparsity at: 0.6261870773854245
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9819 - val_loss: 0.1924 - val_accuracy: 0.9612
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.4558652e-08
 -4.2482547e-08  2.1463874e-08]
Sparsity at: 0.6261870773854245
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9819 - val_loss: 0.1856 - val_accuracy: 0.9666
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.4102834e-04
 -6.7503972e-04  3.8109746e-04]
Sparsity at: 0.6261870773854245
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9799 - val_loss: 0.1855 - val_accuracy: 0.9666
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.6282367e-04
  2.9312423e-04 -7.3925342e-04]
Sparsity at: 0.6261870773854245
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9812 - val_loss: 0.2045 - val_accuracy: 0.9600
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2994341e-04
 -3.6185217e-04  8.5605127e-05]
Sparsity at: 0.6261870773854245
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9818 - val_loss: 0.1726 - val_accuracy: 0.9682
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.0336897e-05
  2.0385464e-04 -1.9895655e-04]
Sparsity at: 0.6261870773854245
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9800 - val_loss: 0.1881 - val_accuracy: 0.9666
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.4276200e-05
 -2.9844925e-04  5.5596058e-05]
Sparsity at: 0.6261870773854245
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9827 - val_loss: 0.1989 - val_accuracy: 0.9621
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.0085348e-04
 -4.0005415e-04  4.6760647e-04]
Sparsity at: 0.6261870773854245
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9825 - val_loss: 0.1938 - val_accuracy: 0.9642
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.4465741e-05
 -7.3872827e-05  3.5320216e-05]
Sparsity at: 0.6261870773854245
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9818 - val_loss: 0.2511 - val_accuracy: 0.9441
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2384003e-04
  1.0775219e-04  2.7143807e-04]
Sparsity at: 0.6261870773854245
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9817 - val_loss: 0.1982 - val_accuracy: 0.9615
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.4222763e-05
 -3.5958691e-04  3.6389907e-04]
Sparsity at: 0.6261870773854245
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9806 - val_loss: 0.1852 - val_accuracy: 0.9644
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.7730907e-05
 -1.7828708e-04  9.0911381e-05]
Sparsity at: 0.6261870773854245
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9828 - val_loss: 0.1822 - val_accuracy: 0.9658
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.0261314e-04
 -3.9816654e-04  2.7230865e-04]
Sparsity at: 0.6261870773854245
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9814 - val_loss: 0.1850 - val_accuracy: 0.9642
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3339235e-05
  1.1734862e-04  2.0584546e-03]
Sparsity at: 0.6261870773854245
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9812 - val_loss: 0.1864 - val_accuracy: 0.9632
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.5511265e-06
 -8.3721188e-06  1.7499680e-05]
Sparsity at: 0.6261870773854245
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9836 - val_loss: 0.1970 - val_accuracy: 0.9610
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.9954527e-09
 -1.4136415e-07  1.3934643e-07]
Sparsity at: 0.6261870773854245
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9807 - val_loss: 0.1876 - val_accuracy: 0.9650
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.0663334e-08
 -1.5606945e-07  8.0859479e-08]
Sparsity at: 0.6261870773854245
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9826 - val_loss: 0.1763 - val_accuracy: 0.9676
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.9010654e-08
 -2.7427653e-08 -5.3518118e-10]
Sparsity at: 0.6261870773854245
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9810 - val_loss: 0.2041 - val_accuracy: 0.9600
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2713302e-07
  1.1743823e-06  1.2525702e-06]
Sparsity at: 0.6261870773854245
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9817 - val_loss: 0.1986 - val_accuracy: 0.9610
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.6987228e-06
 -3.1854102e-05  1.8687613e-05]
Sparsity at: 0.6261870773854245
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9822 - val_loss: 0.2027 - val_accuracy: 0.9611
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.6813243e-04
 -3.5479618e-03  1.7110492e-03]
Sparsity at: 0.6261870773854245
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9817 - val_loss: 0.1980 - val_accuracy: 0.9611
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.0105329e-05
 -3.3844609e-04  6.6571374e-05]
Sparsity at: 0.6261870773854245
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.020474764355928432
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.6458588
tf.Tensor(
[[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 ...
 [0. 1. 1. ... 0. 0. 0.]
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.03751815616762588
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.49263334
tf.Tensor(
[[1. 0. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]
 ...
 [0. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.0868303992841506
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.004
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 235s 12ms/step - loss: 0.1243 - accuracy: 0.9813 - val_loss: 0.1925 - val_accuracy: 0.9646
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3655852e-07
 -1.1837324e-06  6.9311528e-07]
Sparsity at: 0.6261870773854245
Epoch 402/500
235/235 [==============================] - 3s 13ms/step - loss: 0.1209 - accuracy: 0.9821 - val_loss: 0.2176 - val_accuracy: 0.9577
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.0531508e-09
  3.0644582e-09  3.0888985e-09]
Sparsity at: 0.6261870773854245
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9807 - val_loss: 0.1914 - val_accuracy: 0.9630
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.8350095e-11
  1.3681974e-10 -5.1871882e-11]
Sparsity at: 0.6261870773854245
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9824 - val_loss: 0.1891 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3656733e-11
 -8.9092053e-11  1.7147960e-11]
Sparsity at: 0.6261870773854245
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9822 - val_loss: 0.1944 - val_accuracy: 0.9637
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.3320718e-14
  2.9360889e-12 -1.7571143e-12]
Sparsity at: 0.6261870773854245
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9815 - val_loss: 0.1962 - val_accuracy: 0.9622
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.4582606e-12
  3.5340411e-12 -6.0484907e-12]
Sparsity at: 0.6261870773854245
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9815 - val_loss: 0.1683 - val_accuracy: 0.9692
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.4846724e-12
  4.1195992e-12  1.6674201e-12]
Sparsity at: 0.6261870773854245
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9831 - val_loss: 0.2182 - val_accuracy: 0.9580
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8965916e-11
  3.1729796e-11 -1.2170326e-11]
Sparsity at: 0.6261870773854245
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9814 - val_loss: 0.1807 - val_accuracy: 0.9667
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.2945463e-10
  4.5110888e-09  3.5747381e-09]
Sparsity at: 0.6261870773854245
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9824 - val_loss: 0.2036 - val_accuracy: 0.9610
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.1630442e-08
  8.0722259e-08 -6.0818820e-08]
Sparsity at: 0.6261870773854245
Epoch 411/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9804 - val_loss: 0.1998 - val_accuracy: 0.9600
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8610057e-04
  5.0290907e-04 -4.6582933e-05]
Sparsity at: 0.6261870773854245
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9826 - val_loss: 0.1949 - val_accuracy: 0.9629
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8485835e-03
  4.4665267e-03 -6.3190900e-04]
Sparsity at: 0.6261870773854245
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9826 - val_loss: 0.1962 - val_accuracy: 0.9650
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -7.4259791e-05
 -1.2143866e-04  1.2779946e-04]
Sparsity at: 0.6261870773854245
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9822 - val_loss: 0.2031 - val_accuracy: 0.9602
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.7616647e-05
  1.8563025e-05 -2.4891418e-05]
Sparsity at: 0.6261870773854245
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9811 - val_loss: 0.2153 - val_accuracy: 0.9553
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.5368364e-07
 -1.6331296e-06  2.8643328e-07]
Sparsity at: 0.6261870773854245
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9826 - val_loss: 0.1788 - val_accuracy: 0.9688
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.4618365e-07
  3.0221217e-06 -1.2266655e-06]
Sparsity at: 0.6261870773854245
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9819 - val_loss: 0.1960 - val_accuracy: 0.9617
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.1497204e-08
 -1.0743433e-06  3.1983700e-07]
Sparsity at: 0.6261870773854245
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9812 - val_loss: 0.2122 - val_accuracy: 0.9587
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.1215657e-07
  5.8115938e-07 -1.3543797e-09]
Sparsity at: 0.6261870773854245
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9812 - val_loss: 0.2161 - val_accuracy: 0.9568
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.1358805e-06
 -8.3415325e-05  1.3039102e-05]
Sparsity at: 0.6261870773854245
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9813 - val_loss: 0.2255 - val_accuracy: 0.9534
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.6890911e-04
 -2.0496054e-03  7.9449115e-04]
Sparsity at: 0.6261870773854245
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9810 - val_loss: 0.2052 - val_accuracy: 0.9589
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.9004872e-04
 -3.3907319e-04  3.8326718e-04]
Sparsity at: 0.6261870773854245
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9824 - val_loss: 0.1842 - val_accuracy: 0.9642
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.1958482e-06
 -3.4224431e-05  1.6701235e-06]
Sparsity at: 0.6261870773854245
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1175 - accuracy: 0.9826 - val_loss: 0.1986 - val_accuracy: 0.9618
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.5034656e-05
 -1.3027662e-04  1.4647128e-04]
Sparsity at: 0.6261870773854245
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9807 - val_loss: 0.1913 - val_accuracy: 0.9660
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.8158928e-04
 -9.0000255e-04 -8.2751818e-04]
Sparsity at: 0.6261870773854245
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9817 - val_loss: 0.1932 - val_accuracy: 0.9631
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.6023814e-03
  3.0240847e-03 -1.6541183e-03]
Sparsity at: 0.6261870773854245
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9796 - val_loss: 0.1932 - val_accuracy: 0.9638
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.9944021e-05
 -1.0959603e-04  6.0733058e-05]
Sparsity at: 0.6261870773854245
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9826 - val_loss: 0.1974 - val_accuracy: 0.9630
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.6153046e-07
 -3.5220025e-06  1.3992014e-06]
Sparsity at: 0.6261870773854245
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9815 - val_loss: 0.1881 - val_accuracy: 0.9645
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.1976826e-08
 -6.4389283e-08 -4.6575477e-09]
Sparsity at: 0.6261870773854245
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9816 - val_loss: 0.1902 - val_accuracy: 0.9629
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.8809073e-09
  2.9563136e-08 -2.8069392e-08]
Sparsity at: 0.6261870773854245
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9819 - val_loss: 0.1915 - val_accuracy: 0.9644
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.0323533e-08
 -3.5640181e-08  2.7647818e-08]
Sparsity at: 0.6261870773854245
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9819 - val_loss: 0.2474 - val_accuracy: 0.9507
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3004569e-07
 -2.5500796e-08  2.2905246e-07]
Sparsity at: 0.6261870773854245
Epoch 432/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1231 - accuracy: 0.9811 - val_loss: 0.2023 - val_accuracy: 0.9606
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ... -1.06880634e-07
 -3.53378340e-07 -4.57105841e-07]
Sparsity at: 0.6261870773854245
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9829 - val_loss: 0.2089 - val_accuracy: 0.9581
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.6122502e-05
 -2.5339850e-05  1.4253271e-05]
Sparsity at: 0.6261870773854245
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9822 - val_loss: 0.2101 - val_accuracy: 0.9605
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.8770240e-04
  1.2434192e-04  1.1543169e-03]
Sparsity at: 0.6261870773854245
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9804 - val_loss: 0.2040 - val_accuracy: 0.9603
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.6606188e-04
  1.4322897e-02 -1.4225943e-02]
Sparsity at: 0.6261870773854245
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9819 - val_loss: 0.1798 - val_accuracy: 0.9664
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.7763979e-04
  9.0713607e-04 -9.4500756e-05]
Sparsity at: 0.6261870773854245
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9811 - val_loss: 0.2078 - val_accuracy: 0.9600
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.1781408e-05
  5.8634032e-05 -2.7287941e-05]
Sparsity at: 0.6261870773854245
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9819 - val_loss: 0.2033 - val_accuracy: 0.9617
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.6274080e-07
 -2.5273908e-07  4.9004385e-08]
Sparsity at: 0.6261870773854245
Epoch 439/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1209 - accuracy: 0.9823 - val_loss: 0.2109 - val_accuracy: 0.9561
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1140786e-09
  2.1387829e-09  1.8099465e-10]
Sparsity at: 0.6261870773854245
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9821 - val_loss: 0.2195 - val_accuracy: 0.9566
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.7802224e-10
 -2.4276109e-10  3.9868029e-11]
Sparsity at: 0.6261870773854245
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9816 - val_loss: 0.1966 - val_accuracy: 0.9620
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.7571757e-12
 -8.7592059e-13 -2.9176485e-12]
Sparsity at: 0.6261870773854245
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9812 - val_loss: 0.2242 - val_accuracy: 0.9546
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.1258848e-13
  5.1406069e-13 -8.2724609e-13]
Sparsity at: 0.6261870773854245
Epoch 443/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1230 - accuracy: 0.9812 - val_loss: 0.2372 - val_accuracy: 0.9500
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.1517095e-13
 -3.2449702e-13  4.5773091e-13]
Sparsity at: 0.6261870773854245
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9826 - val_loss: 0.2119 - val_accuracy: 0.9581
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.5527575e-13
 -2.4795072e-13  2.4899074e-13]
Sparsity at: 0.6261870773854245
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9818 - val_loss: 0.1848 - val_accuracy: 0.9660
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.2018537e-14
 -2.5366848e-13 -1.4930739e-13]
Sparsity at: 0.6261870773854245
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9814 - val_loss: 0.2099 - val_accuracy: 0.9605
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.4189448e-12
 -4.7730353e-13  5.3890638e-12]
Sparsity at: 0.6261870773854245
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9814 - val_loss: 0.1914 - val_accuracy: 0.9624
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.5428935e-10
 -8.1295304e-10  3.5037806e-10]
Sparsity at: 0.6261870773854245
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9809 - val_loss: 0.1963 - val_accuracy: 0.9632
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.3212157e-08
 -3.2464115e-08  7.0028420e-08]
Sparsity at: 0.6261870773854245
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9821 - val_loss: 0.1773 - val_accuracy: 0.9678
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1695812e-04
 -2.9856834e-04  9.5314455e-05]
Sparsity at: 0.6261870773854245
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9811 - val_loss: 0.2233 - val_accuracy: 0.9562
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.6348332e-04
 -3.3171699e-04  4.9628050e-04]
Sparsity at: 0.6261870773854245
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9811 - val_loss: 0.1846 - val_accuracy: 0.9655
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ... -7.74102518e-05
 -2.59405322e-04  1.10905414e-04]
Sparsity at: 0.6261870773854245
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9819 - val_loss: 0.1846 - val_accuracy: 0.9657
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.8358597e-05
 -9.7059037e-06  4.9182665e-05]
Sparsity at: 0.6261870773854245
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9818 - val_loss: 0.2096 - val_accuracy: 0.9577
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.6698266e-03
 -4.9069775e-03  4.5817667e-03]
Sparsity at: 0.6261870773854245
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9803 - val_loss: 0.1874 - val_accuracy: 0.9646
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.1737018e-04
  7.3223858e-04 -3.2825355e-04]
Sparsity at: 0.6261870773854245
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9816 - val_loss: 0.1916 - val_accuracy: 0.9615
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.7965823e-05
  5.6329271e-05 -6.5956730e-05]
Sparsity at: 0.6261870773854245
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9819 - val_loss: 0.1917 - val_accuracy: 0.9643
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.7323512e-06
 -3.8164759e-05  1.0086858e-05]
Sparsity at: 0.6261870773854245
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9801 - val_loss: 0.1852 - val_accuracy: 0.9669
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.9808927e-05
  3.7349731e-05 -3.5346897e-05]
Sparsity at: 0.6261870773854245
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9816 - val_loss: 0.1872 - val_accuracy: 0.9625
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.6425590e-05
 -4.4926524e-05  4.6440087e-05]
Sparsity at: 0.6261870773854245
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9811 - val_loss: 0.2054 - val_accuracy: 0.9594
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.0927357e-03
 -6.2026009e-03  5.4529822e-03]
Sparsity at: 0.6261870773854245
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9817 - val_loss: 0.1866 - val_accuracy: 0.9658
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.9960027e-04
 -3.3034716e-04 -7.3041134e-05]
Sparsity at: 0.6261870773854245
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9824 - val_loss: 0.1936 - val_accuracy: 0.9627
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.4566958e-06
  1.5686150e-05 -6.8722593e-06]
Sparsity at: 0.6261870773854245
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9821 - val_loss: 0.1927 - val_accuracy: 0.9637
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  1.14674075e-08
  5.52240706e-08 -1.84081905e-08]
Sparsity at: 0.6261870773854245
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9819 - val_loss: 0.1928 - val_accuracy: 0.9628
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ... -1.47609125e-08
 -1.38054261e-08  3.24301723e-08]
Sparsity at: 0.6261870773854245
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9822 - val_loss: 0.1960 - val_accuracy: 0.9614
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  6.7346173e-09
  2.3804260e-08 -2.5602670e-08]
Sparsity at: 0.6261870773854245
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9801 - val_loss: 0.1924 - val_accuracy: 0.9632
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -5.7261254e-09
 -2.4134216e-08  2.2805558e-08]
Sparsity at: 0.6261870773854245
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9805 - val_loss: 0.1949 - val_accuracy: 0.9627
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.5028095e-08
 -4.2201656e-08  2.1726066e-07]
Sparsity at: 0.6261870773854245
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9824 - val_loss: 0.1882 - val_accuracy: 0.9635
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.5888820e-07
  8.3858038e-07 -5.3474452e-08]
Sparsity at: 0.6261870773854245
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9815 - val_loss: 0.1767 - val_accuracy: 0.9688
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -6.5361692e-06
  1.0307149e-05 -8.6042819e-06]
Sparsity at: 0.6261870773854245
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9813 - val_loss: 0.1909 - val_accuracy: 0.9631
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.7489677e-04
  8.2126469e-05  8.9422778e-05]
Sparsity at: 0.6261870773854245
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9809 - val_loss: 0.1926 - val_accuracy: 0.9630
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.7484671e-04
  6.3625691e-03 -2.7935002e-03]
Sparsity at: 0.6261870773854245
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9825 - val_loss: 0.2120 - val_accuracy: 0.9571
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -8.7932922e-06
  5.7491590e-05 -4.0772579e-06]
Sparsity at: 0.6261870773854245
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9809 - val_loss: 0.1984 - val_accuracy: 0.9600
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3095249e-07
 -6.3485606e-07  2.7989262e-07]
Sparsity at: 0.6261870773854245
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9813 - val_loss: 0.1938 - val_accuracy: 0.9635
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.5383223e-11
 -1.5748814e-09 -3.1654665e-10]
Sparsity at: 0.6261870773854245
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9815 - val_loss: 0.1750 - val_accuracy: 0.9686
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.4670797e-11
 -4.6456564e-11  1.4698331e-11]
Sparsity at: 0.6261870773854245
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9827 - val_loss: 0.1793 - val_accuracy: 0.9648
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.6470230e-14
  9.3751829e-13 -5.1819406e-14]
Sparsity at: 0.6261870773854245
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9821 - val_loss: 0.1835 - val_accuracy: 0.9637
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1831476e-14
 -4.1584093e-14  3.9630764e-14]
Sparsity at: 0.6261870773854245
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9811 - val_loss: 0.1901 - val_accuracy: 0.9630
[ 4.77403704e-34 -3.33338055e-34  3.05236187e-34 ...  1.04044835e-16
 -3.12714294e-16 -1.01114865e-15]
Sparsity at: 0.6261870773854245
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9813 - val_loss: 0.2042 - val_accuracy: 0.9580
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  1.0857882e-17
  1.6774048e-17 -1.7847906e-17]
Sparsity at: 0.6261870773854245
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9820 - val_loss: 0.2019 - val_accuracy: 0.9583
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1867780e-18
 -2.5999674e-18  2.2984882e-18]
Sparsity at: 0.6261870773854245
Epoch 480/500
235/235 [==============================] - 3s 12ms/step - loss: 0.1225 - accuracy: 0.9811 - val_loss: 0.1971 - val_accuracy: 0.9606
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.0571067e-17
 -2.5372296e-17  1.0218783e-17]
Sparsity at: 0.6261870773854245
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9811 - val_loss: 0.1813 - val_accuracy: 0.9669
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.1598795e-17
  1.2874081e-15  1.3142595e-15]
Sparsity at: 0.6261870773854245
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9819 - val_loss: 0.1940 - val_accuracy: 0.9644
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.0109051e-13
 -1.7352532e-13  5.7039910e-14]
Sparsity at: 0.6261870773854245
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9816 - val_loss: 0.1824 - val_accuracy: 0.9674
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.1375887e-12
 -1.3811724e-11  7.6892234e-12]
Sparsity at: 0.6261870773854245
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9814 - val_loss: 0.1922 - val_accuracy: 0.9652
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.2137538e-07
 -5.2418752e-08  9.4872050e-08]
Sparsity at: 0.6261870773854245
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9825 - val_loss: 0.1916 - val_accuracy: 0.9625
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.3055704e-05
  1.0003652e-04 -2.2111813e-05]
Sparsity at: 0.6261870773854245
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9808 - val_loss: 0.1880 - val_accuracy: 0.9646
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -4.9724868e-03
 -9.1233030e-03  6.6645974e-03]
Sparsity at: 0.6261870773854245
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1270 - accuracy: 0.9804 - val_loss: 0.1936 - val_accuracy: 0.9639
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  2.1798498e-05
  4.4860793e-04  5.2090283e-05]
Sparsity at: 0.6261870773854245
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9821 - val_loss: 0.1955 - val_accuracy: 0.9606
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  4.7741697e-08
  7.0689929e-08 -4.6588973e-08]
Sparsity at: 0.6261870773854245
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9819 - val_loss: 0.1827 - val_accuracy: 0.9660
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.0700405e-10
 -3.2731223e-10 -1.2186370e-10]
Sparsity at: 0.6261870773854245
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9821 - val_loss: 0.1870 - val_accuracy: 0.9646
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.3028921e-12
 -5.9507980e-12  4.5671158e-12]
Sparsity at: 0.6261870773854245
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9822 - val_loss: 0.1963 - val_accuracy: 0.9610
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  7.2621440e-14
  3.6937890e-13 -3.2164638e-13]
Sparsity at: 0.6261870773854245
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9802 - val_loss: 0.1830 - val_accuracy: 0.9682
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -2.3568306e-14
 -3.4070670e-14  3.0928551e-14]
Sparsity at: 0.6261870773854245
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9811 - val_loss: 0.1920 - val_accuracy: 0.9647
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.1635828e-16
  9.1994247e-16 -3.5308238e-16]
Sparsity at: 0.6261870773854245
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9808 - val_loss: 0.1833 - val_accuracy: 0.9678
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  9.5777635e-17
  9.6112074e-17 -1.2069399e-16]
Sparsity at: 0.6261870773854245
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9817 - val_loss: 0.1945 - val_accuracy: 0.9616
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -1.0933997e-16
 -1.3684917e-16  1.0053309e-16]
Sparsity at: 0.6261870773854245
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9804 - val_loss: 0.1829 - val_accuracy: 0.9662
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -3.6710675e-15
 -3.3810262e-14  1.7806262e-14]
Sparsity at: 0.6261870773854245
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9815 - val_loss: 0.1760 - val_accuracy: 0.9681
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  8.0980692e-13
 -4.3334577e-13 -7.8565653e-13]
Sparsity at: 0.6261870773854245
Epoch 498/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9820 - val_loss: 0.1861 - val_accuracy: 0.9650
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  3.7641068e-10
  1.0870197e-09 -2.9902397e-10]
Sparsity at: 0.6261870773854245
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9809 - val_loss: 0.1853 - val_accuracy: 0.9662
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ...  5.6010452e-10
  5.5396737e-10 -6.4949551e-10]
Sparsity at: 0.6261870773854245
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9802 - val_loss: 0.1920 - val_accuracy: 0.9635
[ 4.7740370e-34 -3.3333806e-34  3.0523619e-34 ... -9.4685001e-06
  2.6315218e-05 -4.6686155e-06]
Sparsity at: 0.6261870773854245
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.03716909512877464
Thresholhold 0.01880677044391632
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.061059337109327316
Thresholhold 0.049408040940761566
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.12204661220312119
Thresholhold -0.13929115235805511
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
  5/235 [..............................] - ETA: 3s - loss: 2.0911 - accuracy: 0.3438     WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0133s vs `on_train_batch_begin` time: 11.2313s). Check your callbacks.
235/235 [==============================] - 71s 12ms/step - loss: 0.2822 - accuracy: 0.9165 - val_loss: 0.2323 - val_accuracy: 0.9565
[ 0.01880677  0.          0.03845737 ...  0.23230197 -0.12916717
  0.17103295]
Sparsity at: 0.2700864012021037
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0972 - accuracy: 0.9724 - val_loss: 0.1042 - val_accuracy: 0.9701
[ 0.01880677  0.          0.03845737 ...  0.25154665 -0.1502766
  0.17470135]
Sparsity at: 0.2700864012021037
Epoch 3/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0568 - accuracy: 0.9842 - val_loss: 0.0897 - val_accuracy: 0.9719
[ 0.01880677  0.          0.03845737 ...  0.26642194 -0.16245458
  0.17647526]
Sparsity at: 0.2700864012021037
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0342 - accuracy: 0.9912 - val_loss: 0.0872 - val_accuracy: 0.9730
[ 0.01880677  0.          0.03845737 ...  0.2833718  -0.17415844
  0.17902444]
Sparsity at: 0.2700864012021037
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0212 - accuracy: 0.9956 - val_loss: 0.0861 - val_accuracy: 0.9740
[ 0.01880677  0.          0.03845737 ...  0.2940272  -0.18124813
  0.18278676]
Sparsity at: 0.2700864012021037
Epoch 6/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0123 - accuracy: 0.9980 - val_loss: 0.0830 - val_accuracy: 0.9762
[ 0.01880677  0.          0.03845737 ...  0.3043624  -0.18816443
  0.19039059]
Sparsity at: 0.2700864012021037
Epoch 7/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0097 - accuracy: 0.9981 - val_loss: 0.0868 - val_accuracy: 0.9753
[ 0.01880677  0.          0.03845737 ...  0.3132718  -0.19224674
  0.19410321]
Sparsity at: 0.2700864012021037
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0084 - accuracy: 0.9986 - val_loss: 0.0925 - val_accuracy: 0.9760
[ 0.01880677  0.          0.03845737 ...  0.32388616 -0.19788942
  0.20015681]
Sparsity at: 0.2700864012021037
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9981 - val_loss: 0.1065 - val_accuracy: 0.9707
[ 0.01880677  0.          0.03845737 ...  0.32894272 -0.19394982
  0.20317557]
Sparsity at: 0.2700864012021037
Epoch 10/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0125 - accuracy: 0.9963 - val_loss: 0.1081 - val_accuracy: 0.9706
[ 0.01880677  0.          0.03845737 ...  0.3307135  -0.20972607
  0.20700717]
Sparsity at: 0.2700864012021037
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0137 - accuracy: 0.9959 - val_loss: 0.0955 - val_accuracy: 0.9742
[ 0.01880677  0.          0.03845737 ...  0.3430643  -0.2280049
  0.20173776]
Sparsity at: 0.2700864012021037
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0104 - accuracy: 0.9968 - val_loss: 0.0976 - val_accuracy: 0.9766
[ 0.01880677  0.          0.03845737 ...  0.34481898 -0.24098162
  0.20202838]
Sparsity at: 0.2700864012021037
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.0830 - val_accuracy: 0.9785
[ 0.01880677  0.          0.03845737 ...  0.33959004 -0.24173242
  0.21162002]
Sparsity at: 0.2700864012021037
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.0930 - val_accuracy: 0.9782
[ 0.01880677  0.          0.03845737 ...  0.3554757  -0.24013168
  0.21411751]
Sparsity at: 0.2700864012021037
Epoch 15/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9989 - val_loss: 0.1005 - val_accuracy: 0.9748
[ 0.01880677  0.          0.03845737 ...  0.3582253  -0.23785211
  0.21446487]
Sparsity at: 0.2700864012021037
Epoch 16/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.1081 - val_accuracy: 0.9739
[ 0.01880677  0.          0.03845737 ...  0.35811278 -0.23401527
  0.21904224]
Sparsity at: 0.2700864012021037
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9995 - val_loss: 0.0896 - val_accuracy: 0.9801
[ 0.01880677  0.          0.03845737 ...  0.36331508 -0.24353647
  0.21864751]
Sparsity at: 0.2700864012021037
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.0949 - val_accuracy: 0.9790
[ 0.01880677  0.          0.03845737 ...  0.3648974  -0.24765861
  0.22203295]
Sparsity at: 0.2700864012021037
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9979 - val_loss: 0.1297 - val_accuracy: 0.9683
[ 0.01880677  0.          0.03845737 ...  0.37055087 -0.23265326
  0.22086513]
Sparsity at: 0.2700864012021037
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0185 - accuracy: 0.9937 - val_loss: 0.1121 - val_accuracy: 0.9734
[ 0.01880677  0.          0.03845737 ...  0.36024556 -0.23035558
  0.21201189]
Sparsity at: 0.2700864012021037
Epoch 21/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0105 - accuracy: 0.9965 - val_loss: 0.1040 - val_accuracy: 0.9759
[ 0.01880677  0.          0.03845737 ...  0.35330984 -0.24664202
  0.23289207]
Sparsity at: 0.2700864012021037
Epoch 22/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0049 - accuracy: 0.9987 - val_loss: 0.0848 - val_accuracy: 0.9805
[ 0.01880677  0.          0.03845737 ...  0.35600254 -0.24224123
  0.23009336]
Sparsity at: 0.2700864012021037
Epoch 23/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 0.0832 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.36016792 -0.24645394
  0.22845107]
Sparsity at: 0.2700864012021037
Epoch 24/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0813 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.36635008 -0.25365627
  0.22901575]
Sparsity at: 0.2700864012021037
Epoch 25/500
235/235 [==============================] - 3s 13ms/step - loss: 3.9546e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9837
[ 0.01880677  0.          0.03845737 ...  0.36794826 -0.25475106
  0.22977498]
Sparsity at: 0.2700864012021037
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0581e-04 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9840
[ 0.01880677  0.          0.03845737 ...  0.3690376  -0.2554894
  0.23062307]
Sparsity at: 0.2700864012021037
Epoch 27/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6062e-04 - accuracy: 1.0000 - val_loss: 0.0773 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.36995986 -0.25572395
  0.23165694]
Sparsity at: 0.2700864012021037
Epoch 28/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3167e-04 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9841
[ 0.01880677  0.          0.03845737 ...  0.37123787 -0.2565263
  0.23230918]
Sparsity at: 0.2700864012021037
Epoch 29/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7690e-04 - accuracy: 0.9999 - val_loss: 0.0931 - val_accuracy: 0.9805
[ 0.01880677  0.          0.03845737 ...  0.38548762 -0.25822222
  0.22202328]
Sparsity at: 0.2700864012021037
Epoch 30/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0087 - accuracy: 0.9974 - val_loss: 0.1964 - val_accuracy: 0.9596
[ 0.01880677  0.          0.03845737 ...  0.38521686 -0.25009054
  0.21948905]
Sparsity at: 0.2700864012021037
Epoch 31/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0260 - accuracy: 0.9911 - val_loss: 0.1129 - val_accuracy: 0.9739
[ 0.01880677  0.          0.03845737 ...  0.35372147 -0.23223694
  0.21364167]
Sparsity at: 0.2700864012021037
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9976 - val_loss: 0.0824 - val_accuracy: 0.9804
[ 0.01880677  0.          0.03845737 ...  0.35754386 -0.24363075
  0.22736974]
Sparsity at: 0.2700864012021037
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.0790 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.35523343 -0.25388137
  0.22773895]
Sparsity at: 0.2700864012021037
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6699e-04 - accuracy: 0.9999 - val_loss: 0.0762 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.35688597 -0.25698718
  0.22564442]
Sparsity at: 0.2700864012021037
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5886e-04 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.35901588 -0.25960016
  0.22549549]
Sparsity at: 0.2700864012021037
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2731e-04 - accuracy: 1.0000 - val_loss: 0.0736 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.3602011  -0.26038894
  0.22640407]
Sparsity at: 0.2700864012021037
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0105e-04 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.3611924  -0.26196063
  0.22725156]
Sparsity at: 0.2700864012021037
Epoch 38/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6604e-04 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.36253938 -0.26318452
  0.22772466]
Sparsity at: 0.2700864012021037
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2356e-04 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.36385235 -0.26307192
  0.22799993]
Sparsity at: 0.2700864012021037
Epoch 40/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1706e-04 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.36574307 -0.2628978
  0.22846548]
Sparsity at: 0.2700864012021037
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3216e-05 - accuracy: 1.0000 - val_loss: 0.0742 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.366388   -0.26367915
  0.22872297]
Sparsity at: 0.2700864012021037
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8997e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.36807352 -0.26403603
  0.22917792]
Sparsity at: 0.2700864012021037
Epoch 43/500
235/235 [==============================] - 3s 13ms/step - loss: 8.7593e-05 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.3646239  -0.2634478
  0.23352297]
Sparsity at: 0.2700864012021037
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7897e-05 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9840
[ 0.01880677  0.          0.03845737 ...  0.36961648 -0.26444662
  0.23028725]
Sparsity at: 0.2700864012021037
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2560e-05 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9846
[ 0.01880677  0.          0.03845737 ...  0.37098816 -0.2641888
  0.2308286 ]
Sparsity at: 0.2700864012021037
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2305e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9845
[ 0.01880677  0.          0.03845737 ...  0.37239194 -0.26480266
  0.23175713]
Sparsity at: 0.2700864012021037
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6073e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.373855   -0.26585743
  0.23230873]
Sparsity at: 0.2700864012021037
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8456e-05 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.37559715 -0.26602846
  0.2321943 ]
Sparsity at: 0.2700864012021037
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0379 - accuracy: 0.9889 - val_loss: 0.1178 - val_accuracy: 0.9728
[ 0.01880677  0.          0.03845737 ...  0.34024954 -0.31169555
  0.2523747 ]
Sparsity at: 0.2700864012021037
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0145 - accuracy: 0.9951 - val_loss: 0.0805 - val_accuracy: 0.9802
[ 0.01880677  0.          0.03845737 ...  0.33796552 -0.2952199
  0.24179254]
Sparsity at: 0.2700864012021037
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.06611566487076992
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.07814751184028879
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.29586061393585084
Thresholhold -0.3402203619480133
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 212s 12ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.0732 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.34649986 -0.30593488
  0.24776769]
Sparsity at: 0.2700864012021037
Epoch 52/500
235/235 [==============================] - 3s 12ms/step - loss: 9.4482e-04 - accuracy: 0.9999 - val_loss: 0.0708 - val_accuracy: 0.9843
[ 0.01880677  0.          0.03845737 ...  0.35014412 -0.30574608
  0.24929197]
Sparsity at: 0.2700864012021037
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0215e-04 - accuracy: 1.0000 - val_loss: 0.0723 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.34939146 -0.3053437
  0.24822623]
Sparsity at: 0.2700864012021037
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3638e-04 - accuracy: 1.0000 - val_loss: 0.0739 - val_accuracy: 0.9846
[ 0.01880677  0.          0.03845737 ...  0.35179278 -0.30652002
  0.24977842]
Sparsity at: 0.2700864012021037
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3570e-04 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.35326567 -0.30825225
  0.24982753]
Sparsity at: 0.2700864012021037
Epoch 56/500
235/235 [==============================] - 3s 13ms/step - loss: 2.2293e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9840
[ 0.01880677  0.          0.03845737 ...  0.35434288 -0.310057
  0.25146276]
Sparsity at: 0.2700864012021037
Epoch 57/500
235/235 [==============================] - 3s 13ms/step - loss: 2.6789e-04 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 0.9843
[ 0.01880677  0.          0.03845737 ...  0.35687312 -0.31093258
  0.24972314]
Sparsity at: 0.2700864012021037
Epoch 58/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5531e-04 - accuracy: 1.0000 - val_loss: 0.0757 - val_accuracy: 0.9843
[ 0.01880677  0.          0.03845737 ...  0.3587526  -0.31249523
  0.25078946]
Sparsity at: 0.2700864012021037
Epoch 59/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2801e-04 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.35987198 -0.31298646
  0.25120676]
Sparsity at: 0.2700864012021037
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0518e-04 - accuracy: 1.0000 - val_loss: 0.0758 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.36058617 -0.3141509
  0.2525968 ]
Sparsity at: 0.2700864012021037
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 9.2929e-05 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.36126772 -0.31457937
  0.25347766]
Sparsity at: 0.2700864012021037
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7364e-04 - accuracy: 1.0000 - val_loss: 0.0833 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.36554542 -0.32059965
  0.25254095]
Sparsity at: 0.2700864012021037
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0141 - accuracy: 0.9953 - val_loss: 0.1353 - val_accuracy: 0.9736
[ 0.01880677  0.          0.03845737 ...  0.37673658 -0.30792266
  0.25823566]
Sparsity at: 0.2700864012021037
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0165 - accuracy: 0.9943 - val_loss: 0.0898 - val_accuracy: 0.9803
[ 0.01880677  0.          0.03845737 ...  0.35412034 -0.30369896
  0.23872036]
Sparsity at: 0.2700864012021037
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0831 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.355375   -0.3069879
  0.24859676]
Sparsity at: 0.2700864012021037
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0825 - val_accuracy: 0.9815
[ 0.01880677  0.          0.03845737 ...  0.35351956 -0.30806434
  0.2519983 ]
Sparsity at: 0.2700864012021037
Epoch 67/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7384e-04 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.35471302 -0.30594563
  0.25232986]
Sparsity at: 0.2700864012021037
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2957e-04 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.35748875 -0.3065587
  0.24906768]
Sparsity at: 0.2700864012021037
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7658e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.35796073 -0.30671787
  0.25018468]
Sparsity at: 0.2700864012021037
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4306e-04 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.3580548  -0.30622974
  0.25115842]
Sparsity at: 0.2700864012021037
Epoch 71/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1660e-04 - accuracy: 1.0000 - val_loss: 0.0786 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.35857388 -0.30798468
  0.25219536]
Sparsity at: 0.2700864012021037
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 9.8954e-05 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.35941797 -0.30842283
  0.25261715]
Sparsity at: 0.2700864012021037
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6900e-04 - accuracy: 0.9999 - val_loss: 0.0796 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.35994643 -0.30888423
  0.25311223]
Sparsity at: 0.2700864012021037
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1509e-04 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.36516017 -0.3101393
  0.25442067]
Sparsity at: 0.2700864012021037
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1917e-05 - accuracy: 1.0000 - val_loss: 0.0797 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.36551282 -0.31127137
  0.25638562]
Sparsity at: 0.2700864012021037
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4454e-05 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.3660598  -0.31175742
  0.2564181 ]
Sparsity at: 0.2700864012021037
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6115e-05 - accuracy: 1.0000 - val_loss: 0.0802 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.36705652 -0.31253
  0.2576863 ]
Sparsity at: 0.2700864012021037
Epoch 78/500
235/235 [==============================] - 3s 13ms/step - loss: 4.3647e-05 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.36782357 -0.31288725
  0.25788078]
Sparsity at: 0.2700864012021037
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9160e-05 - accuracy: 1.0000 - val_loss: 0.0810 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.36902595 -0.31310007
  0.25819367]
Sparsity at: 0.2700864012021037
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6321e-05 - accuracy: 1.0000 - val_loss: 0.0816 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.36952093 -0.31437287
  0.25937843]
Sparsity at: 0.2700864012021037
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1893e-05 - accuracy: 1.0000 - val_loss: 0.0822 - val_accuracy: 0.9838
[ 0.01880677  0.          0.03845737 ...  0.37053114 -0.3150092
  0.25942394]
Sparsity at: 0.2700864012021037
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4929e-05 - accuracy: 1.0000 - val_loss: 0.0831 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.36897573 -0.31608197
  0.262067  ]
Sparsity at: 0.2700864012021037
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0179 - accuracy: 0.9947 - val_loss: 0.1481 - val_accuracy: 0.9695
[ 0.01880677  0.          0.03845737 ...  0.35340166 -0.31162125
  0.26489103]
Sparsity at: 0.2700864012021037
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0142 - accuracy: 0.9951 - val_loss: 0.1011 - val_accuracy: 0.9781
[ 0.01880677  0.          0.03845737 ...  0.34771538 -0.30399865
  0.26014343]
Sparsity at: 0.2700864012021037
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.0833 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.33571327 -0.31862366
  0.25775436]
Sparsity at: 0.2700864012021037
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0813 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.33758464 -0.32563385
  0.25469264]
Sparsity at: 0.2700864012021037
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0645e-04 - accuracy: 1.0000 - val_loss: 0.0818 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.33737102 -0.32579282
  0.2565311 ]
Sparsity at: 0.2700864012021037
Epoch 88/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1240e-04 - accuracy: 1.0000 - val_loss: 0.0818 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.33733696 -0.32757583
  0.25629613]
Sparsity at: 0.2700864012021037
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4687e-04 - accuracy: 1.0000 - val_loss: 0.0824 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.33717707 -0.3288919
  0.25795746]
Sparsity at: 0.2700864012021037
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1503e-04 - accuracy: 1.0000 - val_loss: 0.0828 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.33816466 -0.33012235
  0.257459  ]
Sparsity at: 0.2700864012021037
Epoch 91/500
235/235 [==============================] - 3s 13ms/step - loss: 9.5318e-05 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9837
[ 0.01880677  0.          0.03845737 ...  0.33882195 -0.33077592
  0.25776714]
Sparsity at: 0.2700864012021037
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 9.4048e-05 - accuracy: 1.0000 - val_loss: 0.0840 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.33912402 -0.3321402
  0.2589783 ]
Sparsity at: 0.2700864012021037
Epoch 93/500
235/235 [==============================] - 3s 13ms/step - loss: 7.8743e-05 - accuracy: 1.0000 - val_loss: 0.0842 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.33953968 -0.33359125
  0.26039138]
Sparsity at: 0.2700864012021037
Epoch 94/500
235/235 [==============================] - 3s 13ms/step - loss: 8.5555e-05 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.33987436 -0.3338481
  0.2613152 ]
Sparsity at: 0.2700864012021037
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3672e-05 - accuracy: 1.0000 - val_loss: 0.0847 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.3400828  -0.3348275
  0.2617422 ]
Sparsity at: 0.2700864012021037
Epoch 96/500
235/235 [==============================] - 3s 13ms/step - loss: 5.4499e-05 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9841
[ 0.01880677  0.          0.03845737 ...  0.34050107 -0.33645937
  0.2617213 ]
Sparsity at: 0.2700864012021037
Epoch 97/500
235/235 [==============================] - 3s 13ms/step - loss: 6.3806e-05 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.34189215 -0.3368822
  0.261293  ]
Sparsity at: 0.2700864012021037
Epoch 98/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7061e-05 - accuracy: 1.0000 - val_loss: 0.0867 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.34146202 -0.33804733
  0.26295632]
Sparsity at: 0.2700864012021037
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0421e-05 - accuracy: 1.0000 - val_loss: 0.0874 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.34213364 -0.33804637
  0.26357004]
Sparsity at: 0.2700864012021037
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3490e-05 - accuracy: 1.0000 - val_loss: 0.0873 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.34299138 -0.33868858
  0.26447505]
Sparsity at: 0.2700864012021037
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.11399688154693344
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.13121018478452484
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.4002787557079337
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 208s 11ms/step - loss: 3.3624e-05 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.34355098 -0.33980036
  0.26527673]
Sparsity at: 0.2700864012021037
Epoch 102/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7624e-05 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9840
[ 0.01880677  0.          0.03845737 ...  0.34335235 -0.34064272
  0.2672803 ]
Sparsity at: 0.2700864012021037
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0451e-05 - accuracy: 1.0000 - val_loss: 0.0880 - val_accuracy: 0.9843
[ 0.01880677  0.          0.03845737 ...  0.34468177 -0.3413692
  0.26692942]
Sparsity at: 0.2700864012021037
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 0.2332 - val_accuracy: 0.9630
[ 0.01880677  0.          0.03845737 ...  0.36040077 -0.3347753
  0.28032964]
Sparsity at: 0.2700864012021037
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0200 - accuracy: 0.9936 - val_loss: 0.1075 - val_accuracy: 0.9794
[ 0.01880677  0.          0.03845737 ...  0.32841897 -0.34730953
  0.24894111]
Sparsity at: 0.2700864012021037
Epoch 106/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0908 - val_accuracy: 0.9811
[ 0.01880677  0.          0.03845737 ...  0.32772934 -0.35196272
  0.25824478]
Sparsity at: 0.2700864012021037
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.0885 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.32978287 -0.37093228
  0.25648287]
Sparsity at: 0.2700864012021037
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3949e-04 - accuracy: 1.0000 - val_loss: 0.0861 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.32799506 -0.3661913
  0.25405964]
Sparsity at: 0.2700864012021037
Epoch 109/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6075e-04 - accuracy: 1.0000 - val_loss: 0.0853 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.3300315  -0.36777917
  0.2537363 ]
Sparsity at: 0.2700864012021037
Epoch 110/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4888e-04 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.33057898 -0.36922738
  0.25497517]
Sparsity at: 0.2700864012021037
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2932e-04 - accuracy: 1.0000 - val_loss: 0.0838 - val_accuracy: 0.9838
[ 0.01880677  0.          0.03845737 ...  0.33290505 -0.3710852
  0.2563007 ]
Sparsity at: 0.2700864012021037
Epoch 112/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5849e-04 - accuracy: 1.0000 - val_loss: 0.0852 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.33359864 -0.3725762
  0.25865397]
Sparsity at: 0.2700864012021037
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 8.3597e-05 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.33404073 -0.37506932
  0.25921378]
Sparsity at: 0.2700864012021037
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1107e-05 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.33477712 -0.37590307
  0.25919968]
Sparsity at: 0.2700864012021037
Epoch 115/500
235/235 [==============================] - 3s 13ms/step - loss: 6.0728e-05 - accuracy: 1.0000 - val_loss: 0.0866 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.3355483  -0.3761024
  0.26005617]
Sparsity at: 0.2700864012021037
Epoch 116/500
235/235 [==============================] - 3s 13ms/step - loss: 5.1633e-05 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.336646   -0.37635362
  0.26030508]
Sparsity at: 0.2700864012021037
Epoch 117/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7865e-05 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9838
[ 0.01880677  0.          0.03845737 ...  0.3382502  -0.3771674
  0.2609766 ]
Sparsity at: 0.2700864012021037
Epoch 118/500
235/235 [==============================] - 3s 13ms/step - loss: 4.5381e-05 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.33886024 -0.37758505
  0.26143485]
Sparsity at: 0.2700864012021037
Epoch 119/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0442e-04 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.3404003  -0.37835962
  0.26037258]
Sparsity at: 0.2700864012021037
Epoch 120/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 0.1375 - val_accuracy: 0.9756
[ 0.01880677  0.          0.03845737 ...  0.3375445  -0.3762832
  0.28690943]
Sparsity at: 0.2700864012021037
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9976 - val_loss: 0.1007 - val_accuracy: 0.9809
[ 0.01880677  0.          0.03845737 ...  0.3395634  -0.39015815
  0.27647647]
Sparsity at: 0.2700864012021037
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0950 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.3348401  -0.40696812
  0.28352305]
Sparsity at: 0.2700864012021037
Epoch 123/500
235/235 [==============================] - 3s 13ms/step - loss: 3.8965e-04 - accuracy: 0.9999 - val_loss: 0.0938 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.3371955  -0.4054046
  0.28151917]
Sparsity at: 0.2700864012021037
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1092e-04 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.34081003 -0.40776658
  0.2796952 ]
Sparsity at: 0.2700864012021037
Epoch 125/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1504e-04 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.3406034  -0.40692627
  0.2807455 ]
Sparsity at: 0.2700864012021037
Epoch 126/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0141e-04 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.34120196 -0.40680203
  0.28074947]
Sparsity at: 0.2700864012021037
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 8.7916e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.3409172  -0.40760893
  0.28079218]
Sparsity at: 0.2700864012021037
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5763e-05 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.34316167 -0.40584117
  0.28331783]
Sparsity at: 0.2700864012021037
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5958e-04 - accuracy: 0.9999 - val_loss: 0.0935 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.34004548 -0.40655473
  0.28512457]
Sparsity at: 0.2700864012021037
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 8.6982e-05 - accuracy: 1.0000 - val_loss: 0.0900 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.3461929  -0.40691686
  0.28500563]
Sparsity at: 0.2700864012021037
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6271e-05 - accuracy: 1.0000 - val_loss: 0.0922 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.34648305 -0.40848494
  0.2842884 ]
Sparsity at: 0.2700864012021037
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6481e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.3457973  -0.40725157
  0.2840913 ]
Sparsity at: 0.2700864012021037
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4592e-05 - accuracy: 1.0000 - val_loss: 0.0900 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.3462609  -0.40795615
  0.28438127]
Sparsity at: 0.2700864012021037
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5627e-04 - accuracy: 0.9999 - val_loss: 0.1054 - val_accuracy: 0.9815
[ 0.01880677  0.          0.03845737 ...  0.35479066 -0.40877277
  0.2864238 ]
Sparsity at: 0.2700864012021037
Epoch 135/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0060 - accuracy: 0.9984 - val_loss: 0.1422 - val_accuracy: 0.9778
[ 0.01880677  0.          0.03845737 ...  0.36897647 -0.44003403
  0.28236172]
Sparsity at: 0.2700864012021037
Epoch 136/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0064 - accuracy: 0.9977 - val_loss: 0.0959 - val_accuracy: 0.9812
[ 0.01880677  0.          0.03845737 ...  0.36256036 -0.4249798
  0.28098148]
Sparsity at: 0.2700864012021037
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0979 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.36129194 -0.4155263
  0.27498567]
Sparsity at: 0.2700864012021037
Epoch 138/500
235/235 [==============================] - 3s 13ms/step - loss: 6.7090e-04 - accuracy: 0.9998 - val_loss: 0.0978 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.36150488 -0.4277539
  0.27248868]
Sparsity at: 0.2700864012021037
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5918e-04 - accuracy: 1.0000 - val_loss: 0.0945 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.35935318 -0.4291232
  0.2744506 ]
Sparsity at: 0.2700864012021037
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 9.4314e-05 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.3617491  -0.43119362
  0.2706803 ]
Sparsity at: 0.2700864012021037
Epoch 141/500
235/235 [==============================] - 3s 13ms/step - loss: 7.5312e-05 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.36000967 -0.43136913
  0.27359766]
Sparsity at: 0.2700864012021037
Epoch 142/500
235/235 [==============================] - 3s 13ms/step - loss: 5.3974e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.36042088 -0.43318978
  0.27439418]
Sparsity at: 0.2700864012021037
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3083e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.3607577  -0.4337899
  0.2744164 ]
Sparsity at: 0.2700864012021037
Epoch 144/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7338e-04 - accuracy: 0.9999 - val_loss: 0.0951 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.37011084 -0.4350443
  0.27406392]
Sparsity at: 0.2700864012021037
Epoch 145/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5814e-04 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.37632015 -0.4368607
  0.27520758]
Sparsity at: 0.2700864012021037
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3552e-05 - accuracy: 1.0000 - val_loss: 0.0981 - val_accuracy: 0.9848
[ 0.01880677  0.          0.03845737 ...  0.37311575 -0.437021
  0.27650645]
Sparsity at: 0.2700864012021037
Epoch 147/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6147e-04 - accuracy: 0.9999 - val_loss: 0.1055 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.37655255 -0.43914235
  0.27770877]
Sparsity at: 0.2700864012021037
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6875e-04 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.37839386 -0.43755397
  0.281912  ]
Sparsity at: 0.2700864012021037
Epoch 149/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1201 - val_accuracy: 0.9808
[ 0.01880677  0.          0.03845737 ...  0.35995096 -0.4258499
  0.2926219 ]
Sparsity at: 0.2700864012021037
Epoch 150/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0059 - accuracy: 0.9984 - val_loss: 0.1128 - val_accuracy: 0.9807
[ 0.01880677  0.          0.03845737 ...  0.34868708 -0.4287324
  0.28939417]
Sparsity at: 0.2700864012021037
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.18812474255891143
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.206513359169878
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.491107206467543
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 208s 11ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1037 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.3601177  -0.42047694
  0.277014  ]
Sparsity at: 0.2700864012021037
Epoch 152/500
235/235 [==============================] - 3s 12ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1092 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.3643385  -0.43071586
  0.2699396 ]
Sparsity at: 0.2700864012021037
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 6.8272e-04 - accuracy: 0.9998 - val_loss: 0.1103 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.36669075 -0.42281324
  0.27123058]
Sparsity at: 0.2700864012021037
Epoch 154/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1772e-04 - accuracy: 0.9998 - val_loss: 0.1016 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.36908308 -0.42386812
  0.2698287 ]
Sparsity at: 0.2700864012021037
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9805e-04 - accuracy: 0.9999 - val_loss: 0.1058 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.36343887 -0.43117967
  0.2665424 ]
Sparsity at: 0.2700864012021037
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1713e-04 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.36169255 -0.44398272
  0.27015308]
Sparsity at: 0.2700864012021037
Epoch 157/500
235/235 [==============================] - 3s 13ms/step - loss: 5.9860e-04 - accuracy: 0.9998 - val_loss: 0.1112 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.36126116 -0.44341728
  0.27187636]
Sparsity at: 0.2700864012021037
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3008e-04 - accuracy: 0.9998 - val_loss: 0.1085 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.36051553 -0.451966
  0.272434  ]
Sparsity at: 0.2700864012021037
Epoch 159/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3101e-04 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.35927668 -0.4410087
  0.2706667 ]
Sparsity at: 0.2700864012021037
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 5.1731e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.36012492 -0.4401841
  0.2708258 ]
Sparsity at: 0.2700864012021037
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9982e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.36151013 -0.44185114
  0.27136832]
Sparsity at: 0.2700864012021037
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5551e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.36244118 -0.4421511
  0.2711029 ]
Sparsity at: 0.2700864012021037
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8482e-04 - accuracy: 0.9999 - val_loss: 0.1064 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.3640065  -0.44545192
  0.274835  ]
Sparsity at: 0.2700864012021037
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0920e-04 - accuracy: 0.9999 - val_loss: 0.1189 - val_accuracy: 0.9813
[ 0.01880677  0.          0.03845737 ...  0.35981256 -0.42686298
  0.2642127 ]
Sparsity at: 0.2700864012021037
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0071 - accuracy: 0.9980 - val_loss: 0.1256 - val_accuracy: 0.9782
[ 0.01880677  0.          0.03845737 ...  0.33363706 -0.44629386
  0.29015514]
Sparsity at: 0.2700864012021037
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1069 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.34243256 -0.44975555
  0.29448718]
Sparsity at: 0.2700864012021037
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1093 - val_accuracy: 0.9811
[ 0.01880677  0.          0.03845737 ...  0.33557335 -0.43532175
  0.2882321 ]
Sparsity at: 0.2700864012021037
Epoch 168/500
235/235 [==============================] - 3s 13ms/step - loss: 9.8776e-04 - accuracy: 0.9997 - val_loss: 0.1052 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.32133368 -0.44937596
  0.3104836 ]
Sparsity at: 0.2700864012021037
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7773e-04 - accuracy: 0.9999 - val_loss: 0.1008 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.32649454 -0.45285904
  0.305842  ]
Sparsity at: 0.2700864012021037
Epoch 170/500
235/235 [==============================] - 3s 13ms/step - loss: 7.2340e-05 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9837
[ 0.01880677  0.          0.03845737 ...  0.32736513 -0.45328802
  0.3058304 ]
Sparsity at: 0.2700864012021037
Epoch 171/500
235/235 [==============================] - 3s 13ms/step - loss: 5.1660e-05 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9837
[ 0.01880677  0.          0.03845737 ...  0.3276849  -0.45351404
  0.30513856]
Sparsity at: 0.2700864012021037
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5364e-04 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.3285319  -0.455314
  0.30560303]
Sparsity at: 0.2700864012021037
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5150e-05 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.3289708  -0.45579734
  0.30627987]
Sparsity at: 0.2700864012021037
Epoch 174/500
235/235 [==============================] - 3s 13ms/step - loss: 2.9283e-05 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9838
[ 0.01880677  0.          0.03845737 ...  0.3295714  -0.45555884
  0.30615643]
Sparsity at: 0.2700864012021037
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3191e-05 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.3298288  -0.45581204
  0.30725217]
Sparsity at: 0.2700864012021037
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7074e-05 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9837
[ 0.01880677  0.          0.03845737 ...  0.33048102 -0.45574743
  0.3074661 ]
Sparsity at: 0.2700864012021037
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0661e-05 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9838
[ 0.01880677  0.          0.03845737 ...  0.33148414 -0.45745006
  0.30826765]
Sparsity at: 0.2700864012021037
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9745e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.3335649  -0.45762914
  0.30664203]
Sparsity at: 0.2700864012021037
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7588e-05 - accuracy: 1.0000 - val_loss: 0.0950 - val_accuracy: 0.9848
[ 0.01880677  0.          0.03845737 ...  0.33367676 -0.45804408
  0.30824685]
Sparsity at: 0.2700864012021037
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7507e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9846
[ 0.01880677  0.          0.03845737 ...  0.33417374 -0.45997372
  0.3081841 ]
Sparsity at: 0.2700864012021037
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4932e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9847
[ 0.01880677  0.          0.03845737 ...  0.33482525 -0.46087486
  0.3086759 ]
Sparsity at: 0.2700864012021037
Epoch 182/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3685e-05 - accuracy: 1.0000 - val_loss: 0.0950 - val_accuracy: 0.9845
[ 0.01880677  0.          0.03845737 ...  0.3344229  -0.46051115
  0.30941352]
Sparsity at: 0.2700864012021037
Epoch 183/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1003e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9848
[ 0.01880677  0.          0.03845737 ...  0.33476108 -0.46100658
  0.3097497 ]
Sparsity at: 0.2700864012021037
Epoch 184/500
235/235 [==============================] - 3s 13ms/step - loss: 9.8042e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9846
[ 0.01880677  0.          0.03845737 ...  0.3348471  -0.462014
  0.31109118]
Sparsity at: 0.2700864012021037
Epoch 185/500
235/235 [==============================] - 3s 13ms/step - loss: 7.8493e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9845
[ 0.01880677  0.          0.03845737 ...  0.3358044  -0.46281505
  0.31119043]
Sparsity at: 0.2700864012021037
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1004e-06 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9845
[ 0.01880677  0.          0.03845737 ...  0.33712786 -0.4634458
  0.31126857]
Sparsity at: 0.2700864012021037
Epoch 187/500
235/235 [==============================] - 3s 13ms/step - loss: 6.8091e-06 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9849
[ 0.01880677  0.          0.03845737 ...  0.33780485 -0.46447983
  0.31138745]
Sparsity at: 0.2700864012021037
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5666e-06 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9847
[ 0.01880677  0.          0.03845737 ...  0.3385703  -0.4649353
  0.31126598]
Sparsity at: 0.2700864012021037
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8479e-06 - accuracy: 1.0000 - val_loss: 0.0978 - val_accuracy: 0.9846
[ 0.01880677  0.          0.03845737 ...  0.3395042  -0.46442014
  0.31081635]
Sparsity at: 0.2700864012021037
Epoch 190/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7895e-06 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9852
[ 0.01880677  0.          0.03845737 ...  0.33979148 -0.46555427
  0.3122346 ]
Sparsity at: 0.2700864012021037
Epoch 191/500
235/235 [==============================] - 3s 13ms/step - loss: 4.3655e-06 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9847
[ 0.01880677  0.          0.03845737 ...  0.34040755 -0.46597618
  0.3123224 ]
Sparsity at: 0.2700864012021037
Epoch 192/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7726e-06 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9848
[ 0.01880677  0.          0.03845737 ...  0.34118575 -0.46675992
  0.3128848 ]
Sparsity at: 0.2700864012021037
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6890e-06 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9848
[ 0.01880677  0.          0.03845737 ...  0.3418691  -0.46705475
  0.31337795]
Sparsity at: 0.2700864012021037
Epoch 194/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0388e-06 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9849
[ 0.01880677  0.          0.03845737 ...  0.34258795 -0.46789283
  0.3135271 ]
Sparsity at: 0.2700864012021037
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9256e-06 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9844
[ 0.01880677  0.          0.03845737 ...  0.34350485 -0.46867213
  0.3140597 ]
Sparsity at: 0.2700864012021037
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8383e-06 - accuracy: 1.0000 - val_loss: 0.0994 - val_accuracy: 0.9848
[ 0.01880677  0.          0.03845737 ...  0.34262195 -0.46986195
  0.31702903]
Sparsity at: 0.2700864012021037
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6226e-06 - accuracy: 1.0000 - val_loss: 0.0998 - val_accuracy: 0.9848
[ 0.01880677  0.          0.03845737 ...  0.34476092 -0.47078562
  0.31589088]
Sparsity at: 0.2700864012021037
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0970e-06 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9852
[ 0.01880677  0.          0.03845737 ...  0.3455278  -0.47136638
  0.31602263]
Sparsity at: 0.2700864012021037
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1485e-06 - accuracy: 1.0000 - val_loss: 0.1004 - val_accuracy: 0.9850
[ 0.01880677  0.          0.03845737 ...  0.3460891  -0.47320247
  0.3166219 ]
Sparsity at: 0.2700864012021037
Epoch 200/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9395e-06 - accuracy: 1.0000 - val_loss: 0.1007 - val_accuracy: 0.9848
[ 0.01880677  0.          0.03845737 ...  0.3473236  -0.4727571
  0.31633136]
Sparsity at: 0.2700864012021037
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.25391200827743177
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.2734804470574659
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.584526818800029
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 209s 11ms/step - loss: 1.9554e-06 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9850
[ 0.01880677  0.          0.03845737 ...  0.34716374 -0.4705087
  0.31762323]
Sparsity at: 0.2700864012021037
Epoch 202/500
235/235 [==============================] - 3s 12ms/step - loss: 2.4538e-06 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9853
[ 0.01880677  0.          0.03845737 ...  0.3507001  -0.4713123
  0.31804335]
Sparsity at: 0.2700864012021037
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9883e-06 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9854
[ 0.01880677  0.          0.03845737 ...  0.35301724 -0.4728621
  0.31954953]
Sparsity at: 0.2700864012021037
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3723e-06 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9852
[ 0.01880677  0.          0.03845737 ...  0.3544895  -0.47318903
  0.32081956]
Sparsity at: 0.2700864012021037
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2705e-06 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9849
[ 0.01880677  0.          0.03845737 ...  0.3551191  -0.47363254
  0.32090122]
Sparsity at: 0.2700864012021037
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2754e-06 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9854
[ 0.01880677  0.          0.03845737 ...  0.35557815 -0.47504097
  0.3197729 ]
Sparsity at: 0.2700864012021037
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1830e-06 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9852
[ 0.01880677  0.          0.03845737 ...  0.35496363 -0.47466144
  0.3235351 ]
Sparsity at: 0.2700864012021037
Epoch 208/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1301e-06 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9847
[ 0.01880677  0.          0.03845737 ...  0.35510162 -0.47458428
  0.32540953]
Sparsity at: 0.2700864012021037
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6354e-07 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9851
[ 0.01880677  0.          0.03845737 ...  0.35627982 -0.4757639
  0.32569066]
Sparsity at: 0.2700864012021037
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1455e-07 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9852
[ 0.01880677  0.          0.03845737 ...  0.3577389  -0.4764981
  0.32580936]
Sparsity at: 0.2700864012021037
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5487e-07 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9850
[ 0.01880677  0.          0.03845737 ...  0.35756075 -0.47778642
  0.32649672]
Sparsity at: 0.2700864012021037
Epoch 212/500
235/235 [==============================] - 3s 13ms/step - loss: 6.0484e-07 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9855
[ 0.01880677  0.          0.03845737 ...  0.35865164 -0.47870785
  0.32698348]
Sparsity at: 0.2700864012021037
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9977 - val_loss: 0.2105 - val_accuracy: 0.9693
[ 0.01880677  0.          0.03845737 ...  0.39237145 -0.49093488
  0.3377547 ]
Sparsity at: 0.2700864012021037
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0122 - accuracy: 0.9965 - val_loss: 0.1333 - val_accuracy: 0.9796
[ 0.01880677  0.          0.03845737 ...  0.42461854 -0.48783022
  0.30389294]
Sparsity at: 0.2700864012021037
Epoch 215/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1121 - val_accuracy: 0.9812
[ 0.01880677  0.          0.03845737 ...  0.42036825 -0.4888907
  0.30109268]
Sparsity at: 0.2700864012021037
Epoch 216/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7490e-04 - accuracy: 0.9999 - val_loss: 0.1149 - val_accuracy: 0.9809
[ 0.01880677  0.          0.03845737 ...  0.41582876 -0.4907547
  0.30110124]
Sparsity at: 0.2700864012021037
Epoch 217/500
235/235 [==============================] - 3s 15ms/step - loss: 1.5328e-04 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.41897428 -0.4913908
  0.30070364]
Sparsity at: 0.2700864012021037
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2944e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.4183183  -0.49323383
  0.3007402 ]
Sparsity at: 0.2700864012021037
Epoch 219/500
235/235 [==============================] - 3s 13ms/step - loss: 8.3078e-05 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.4179737  -0.49358097
  0.30209407]
Sparsity at: 0.2700864012021037
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7978e-05 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.41765046 -0.4942667
  0.30295324]
Sparsity at: 0.2700864012021037
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9476e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.4176864  -0.49442405
  0.3027076 ]
Sparsity at: 0.2700864012021037
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8024e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.41773552 -0.49574912
  0.3027521 ]
Sparsity at: 0.2700864012021037
Epoch 223/500
235/235 [==============================] - 3s 15ms/step - loss: 3.0302e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.41809425 -0.49592334
  0.30306578]
Sparsity at: 0.2700864012021037
Epoch 224/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0294e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.41829067 -0.49560916
  0.30217493]
Sparsity at: 0.2700864012021037
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0096e-05 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.4182166  -0.49559775
  0.3022078 ]
Sparsity at: 0.2700864012021037
Epoch 226/500
235/235 [==============================] - 3s 13ms/step - loss: 2.0924e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.41829705 -0.4960251
  0.30145115]
Sparsity at: 0.2700864012021037
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5397e-05 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.41814268 -0.4950594
  0.30178007]
Sparsity at: 0.2700864012021037
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8972e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.41764712 -0.49246183
  0.3023807 ]
Sparsity at: 0.2700864012021037
Epoch 229/500
235/235 [==============================] - 3s 13ms/step - loss: 3.0595e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.42408592 -0.4939985
  0.29702964]
Sparsity at: 0.2700864012021037
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 0.1797 - val_accuracy: 0.9733
[ 0.01880677  0.          0.03845737 ...  0.38841423 -0.48762226
  0.3102435 ]
Sparsity at: 0.2700864012021037
Epoch 231/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0062 - accuracy: 0.9979 - val_loss: 0.1504 - val_accuracy: 0.9772
[ 0.01880677  0.          0.03845737 ...  0.3962979  -0.4863689
  0.28819868]
Sparsity at: 0.2700864012021037
Epoch 232/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1200 - val_accuracy: 0.9815
[ 0.01880677  0.          0.03845737 ...  0.38960353 -0.5037552
  0.3101871 ]
Sparsity at: 0.2700864012021037
Epoch 233/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6499e-04 - accuracy: 0.9998 - val_loss: 0.1169 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.39122912 -0.50801945
  0.3068121 ]
Sparsity at: 0.2700864012021037
Epoch 234/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3443e-04 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.3966092  -0.5081724
  0.30134556]
Sparsity at: 0.2700864012021037
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0725e-04 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.39615136 -0.5080645
  0.3009617 ]
Sparsity at: 0.2700864012021037
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2857e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.39488232 -0.5084414
  0.300379  ]
Sparsity at: 0.2700864012021037
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0070e-05 - accuracy: 1.0000 - val_loss: 0.1110 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.3950429  -0.50430375
  0.30109242]
Sparsity at: 0.2700864012021037
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6719e-05 - accuracy: 1.0000 - val_loss: 0.1117 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.39512762 -0.5052368
  0.30190766]
Sparsity at: 0.2700864012021037
Epoch 239/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4429e-05 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.39563128 -0.5036845
  0.302569  ]
Sparsity at: 0.2700864012021037
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0874e-05 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.39612657 -0.5045081
  0.3019057 ]
Sparsity at: 0.2700864012021037
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7883e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.39527878 -0.50371796
  0.30286515]
Sparsity at: 0.2700864012021037
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4259e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.39500716 -0.50418353
  0.30306926]
Sparsity at: 0.2700864012021037
Epoch 243/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3056e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.39501286 -0.50402915
  0.30357143]
Sparsity at: 0.2700864012021037
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4530e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.39493927 -0.5045228
  0.3038868 ]
Sparsity at: 0.2700864012021037
Epoch 245/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.1372 - val_accuracy: 0.9799
[ 0.01880677  0.          0.03845737 ...  0.39564136 -0.5123502
  0.30292124]
Sparsity at: 0.2700864012021037
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.1471 - val_accuracy: 0.9793
[ 0.01880677  0.          0.03845737 ...  0.4302787  -0.51585627
  0.33107707]
Sparsity at: 0.2700864012021037
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.1289 - val_accuracy: 0.9799
[ 0.01880677  0.          0.03845737 ...  0.43270093 -0.5038761
  0.32290173]
Sparsity at: 0.2700864012021037
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1196 - val_accuracy: 0.9815
[ 0.01880677  0.          0.03845737 ...  0.42104304 -0.5219948
  0.3384797 ]
Sparsity at: 0.2700864012021037
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9831e-04 - accuracy: 0.9999 - val_loss: 0.1184 - val_accuracy: 0.9812
[ 0.01880677  0.          0.03845737 ...  0.415998   -0.52314967
  0.329935  ]
Sparsity at: 0.2700864012021037
Epoch 250/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2644e-04 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.4171712  -0.5249213
  0.3339978 ]
Sparsity at: 0.2700864012021037
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.3525012334928377
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.3701170785072101
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.6656737416860992
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 188s 11ms/step - loss: 9.8400e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.4153415  -0.5251948
  0.33510813]
Sparsity at: 0.2700864012021037
Epoch 252/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0117e-04 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.4205243  -0.52923036
  0.33135483]
Sparsity at: 0.2700864012021037
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6433e-04 - accuracy: 0.9999 - val_loss: 0.1190 - val_accuracy: 0.9814
[ 0.01880677  0.          0.03845737 ...  0.41517037 -0.52724665
  0.33376193]
Sparsity at: 0.2700864012021037
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8912e-04 - accuracy: 0.9999 - val_loss: 0.1197 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.41745842 -0.52228445
  0.3346743 ]
Sparsity at: 0.2700864012021037
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3901e-04 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.41376457 -0.53081435
  0.33818257]
Sparsity at: 0.2700864012021037
Epoch 256/500
235/235 [==============================] - 3s 13ms/step - loss: 3.5619e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.4151042  -0.5313632
  0.3392456 ]
Sparsity at: 0.2700864012021037
Epoch 257/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4986e-04 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.41385606 -0.53089726
  0.3399099 ]
Sparsity at: 0.2700864012021037
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7431e-05 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.40875438 -0.5307653
  0.3448017 ]
Sparsity at: 0.2700864012021037
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8695e-04 - accuracy: 0.9999 - val_loss: 0.1275 - val_accuracy: 0.9807
[ 0.01880677  0.          0.03845737 ...  0.4073385  -0.5323199
  0.34833315]
Sparsity at: 0.2700864012021037
Epoch 260/500
235/235 [==============================] - 3s 13ms/step - loss: 4.5138e-04 - accuracy: 0.9999 - val_loss: 0.1256 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.40468368 -0.539095
  0.3535958 ]
Sparsity at: 0.2700864012021037
Epoch 261/500
235/235 [==============================] - 3s 13ms/step - loss: 8.5688e-05 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.4081946  -0.53055644
  0.3521436 ]
Sparsity at: 0.2700864012021037
Epoch 262/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3778e-05 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.40689647 -0.5327723
  0.35564968]
Sparsity at: 0.2700864012021037
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7068e-05 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.40389222 -0.53630584
  0.35580838]
Sparsity at: 0.2700864012021037
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2575e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.4028894  -0.5357232
  0.35670733]
Sparsity at: 0.2700864012021037
Epoch 265/500
235/235 [==============================] - 3s 13ms/step - loss: 1.1395e-05 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.40474254 -0.536253
  0.35634285]
Sparsity at: 0.2700864012021037
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9075e-05 - accuracy: 1.0000 - val_loss: 0.1196 - val_accuracy: 0.9837
[ 0.01880677  0.          0.03845737 ...  0.40504634 -0.535391
  0.35517022]
Sparsity at: 0.2700864012021037
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.1489 - val_accuracy: 0.9775
[ 0.01880677  0.          0.03845737 ...  0.39812222 -0.545516
  0.36634803]
Sparsity at: 0.2700864012021037
Epoch 268/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9988 - val_loss: 0.1299 - val_accuracy: 0.9803
[ 0.01880677  0.          0.03845737 ...  0.3965682  -0.55635047
  0.3821793 ]
Sparsity at: 0.2700864012021037
Epoch 269/500
235/235 [==============================] - 3s 13ms/step - loss: 6.9510e-04 - accuracy: 0.9998 - val_loss: 0.1191 - val_accuracy: 0.9809
[ 0.01880677  0.          0.03845737 ...  0.39817712 -0.55722547
  0.37561053]
Sparsity at: 0.2700864012021037
Epoch 270/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7852e-04 - accuracy: 0.9999 - val_loss: 0.1209 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.40058008 -0.551672
  0.37366408]
Sparsity at: 0.2700864012021037
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4779e-05 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.40039518 -0.55060464
  0.37323916]
Sparsity at: 0.2700864012021037
Epoch 272/500
235/235 [==============================] - 3s 13ms/step - loss: 7.0072e-05 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.39827678 -0.552044
  0.37506703]
Sparsity at: 0.2700864012021037
Epoch 273/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5641e-05 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.3988323  -0.5526311
  0.37640378]
Sparsity at: 0.2700864012021037
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8866e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.3990114  -0.55301535
  0.37673283]
Sparsity at: 0.2700864012021037
Epoch 275/500
235/235 [==============================] - 3s 13ms/step - loss: 1.0358e-04 - accuracy: 1.0000 - val_loss: 0.1143 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.39578936 -0.55523306
  0.3687744 ]
Sparsity at: 0.2700864012021037
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2937e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.39896113 -0.5541853
  0.3726779 ]
Sparsity at: 0.2700864012021037
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5709e-04 - accuracy: 0.9999 - val_loss: 0.1186 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.39995697 -0.5614169
  0.3724748 ]
Sparsity at: 0.2700864012021037
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1227e-05 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.40198377 -0.56815046
  0.37304357]
Sparsity at: 0.2700864012021037
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8917e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.40365246 -0.5685243
  0.37307006]
Sparsity at: 0.2700864012021037
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4405e-05 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.40452942 -0.5679126
  0.37245178]
Sparsity at: 0.2700864012021037
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1274e-05 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.4048419  -0.5687718
  0.37242457]
Sparsity at: 0.2700864012021037
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7092e-05 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.40500468 -0.56931275
  0.3740916 ]
Sparsity at: 0.2700864012021037
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0768e-05 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.4030841  -0.56994265
  0.37501928]
Sparsity at: 0.2700864012021037
Epoch 284/500
235/235 [==============================] - 3s 13ms/step - loss: 8.3432e-06 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.40471503 -0.57026035
  0.37497312]
Sparsity at: 0.2700864012021037
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8467e-06 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.4045088  -0.56993586
  0.3774917 ]
Sparsity at: 0.2700864012021037
Epoch 286/500
235/235 [==============================] - 3s 13ms/step - loss: 5.8827e-06 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.40437478 -0.569443
  0.37802136]
Sparsity at: 0.2700864012021037
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 7.8581e-06 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9815
[ 0.01880677  0.          0.03845737 ...  0.40446097 -0.5660051
  0.37882906]
Sparsity at: 0.2700864012021037
Epoch 288/500
235/235 [==============================] - 3s 13ms/step - loss: 9.7530e-06 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.4051946  -0.5693887
  0.38016033]
Sparsity at: 0.2700864012021037
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2845e-06 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.40208623 -0.5697308
  0.3808913 ]
Sparsity at: 0.2700864012021037
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 5.9980e-06 - accuracy: 1.0000 - val_loss: 0.1176 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.40573612 -0.5696897
  0.38126838]
Sparsity at: 0.2700864012021037
Epoch 291/500
235/235 [==============================] - 3s 13ms/step - loss: 7.2127e-06 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.40287754 -0.57210827
  0.38060874]
Sparsity at: 0.2700864012021037
Epoch 292/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0083 - accuracy: 0.9977 - val_loss: 0.1469 - val_accuracy: 0.9766
[ 0.01880677  0.          0.03845737 ...  0.4109524  -0.6072722
  0.36886907]
Sparsity at: 0.2700864012021037
Epoch 293/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9987 - val_loss: 0.1129 - val_accuracy: 0.9808
[ 0.01880677  0.          0.03845737 ...  0.41276094 -0.61180115
  0.3721094 ]
Sparsity at: 0.2700864012021037
Epoch 294/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1165 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.41705182 -0.6110271
  0.37227574]
Sparsity at: 0.2700864012021037
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5655e-04 - accuracy: 0.9999 - val_loss: 0.1122 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.41582498 -0.6111558
  0.374399  ]
Sparsity at: 0.2700864012021037
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0012e-04 - accuracy: 0.9999 - val_loss: 0.1150 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.4160955  -0.6085833
  0.37427104]
Sparsity at: 0.2700864012021037
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2332e-04 - accuracy: 0.9999 - val_loss: 0.1134 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.41404036 -0.607109
  0.3792852 ]
Sparsity at: 0.2700864012021037
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4397e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.41415608 -0.6082279
  0.3801659 ]
Sparsity at: 0.2700864012021037
Epoch 299/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7002e-05 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.41369513 -0.6079416
  0.380806  ]
Sparsity at: 0.2700864012021037
Epoch 300/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7661e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.4144415  -0.60806644
  0.37954375]
Sparsity at: 0.2700864012021037
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.455179675822027
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.46342552438913387
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.7265608601560913
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 196s 12ms/step - loss: 3.0829e-05 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.41459385 -0.6087974
  0.38024697]
Sparsity at: 0.2700864012021037
Epoch 302/500
235/235 [==============================] - 3s 13ms/step - loss: 4.7216e-05 - accuracy: 1.0000 - val_loss: 0.1112 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.41463572 -0.6081316
  0.37883145]
Sparsity at: 0.2700864012021037
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2472e-04 - accuracy: 0.9998 - val_loss: 0.1191 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.4214491  -0.6075456
  0.3654542 ]
Sparsity at: 0.2700864012021037
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 9.0894e-04 - accuracy: 0.9997 - val_loss: 0.1268 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.41715908 -0.5988605
  0.36602002]
Sparsity at: 0.2700864012021037
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.1211 - val_accuracy: 0.9801
[ 0.01880677  0.          0.03845737 ...  0.42403612 -0.58957195
  0.3784302 ]
Sparsity at: 0.2700864012021037
Epoch 306/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1065 - val_accuracy: 0.9840
[ 0.01880677  0.          0.03845737 ...  0.43092728 -0.57015735
  0.38037157]
Sparsity at: 0.2700864012021037
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3765e-04 - accuracy: 0.9998 - val_loss: 0.1138 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.437109   -0.5724672
  0.37934366]
Sparsity at: 0.2700864012021037
Epoch 308/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7738e-04 - accuracy: 0.9999 - val_loss: 0.1153 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.4284016  -0.5722831
  0.3850294 ]
Sparsity at: 0.2700864012021037
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 9.6084e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.42836928 -0.57336134
  0.3846535 ]
Sparsity at: 0.2700864012021037
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4341e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.42907724 -0.57186526
  0.3895162 ]
Sparsity at: 0.2700864012021037
Epoch 311/500
235/235 [==============================] - 3s 13ms/step - loss: 1.6215e-04 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.43583128 -0.57241714
  0.38954934]
Sparsity at: 0.2700864012021037
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1114e-04 - accuracy: 0.9999 - val_loss: 0.1264 - val_accuracy: 0.9812
[ 0.01880677  0.          0.03845737 ...  0.42379013 -0.57757056
  0.395545  ]
Sparsity at: 0.2700864012021037
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 7.9186e-05 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.41842535 -0.5782206
  0.39567617]
Sparsity at: 0.2700864012021037
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9300e-04 - accuracy: 0.9999 - val_loss: 0.1223 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.41781586 -0.5808469
  0.39791888]
Sparsity at: 0.2700864012021037
Epoch 315/500
235/235 [==============================] - 3s 13ms/step - loss: 3.1029e-04 - accuracy: 0.9999 - val_loss: 0.1268 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.41379616 -0.57899874
  0.39824682]
Sparsity at: 0.2700864012021037
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0156e-04 - accuracy: 0.9999 - val_loss: 0.1175 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.4355057  -0.5857341
  0.39691567]
Sparsity at: 0.2700864012021037
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7518e-04 - accuracy: 0.9999 - val_loss: 0.1190 - val_accuracy: 0.9808
[ 0.01880677  0.          0.03845737 ...  0.42032993 -0.59410816
  0.41350687]
Sparsity at: 0.2700864012021037
Epoch 318/500
235/235 [==============================] - 3s 13ms/step - loss: 5.9927e-04 - accuracy: 0.9998 - val_loss: 0.1221 - val_accuracy: 0.9812
[ 0.01880677  0.          0.03845737 ...  0.4316157  -0.5942907
  0.4007133 ]
Sparsity at: 0.2700864012021037
Epoch 319/500
235/235 [==============================] - 3s 13ms/step - loss: 6.2144e-04 - accuracy: 0.9999 - val_loss: 0.1265 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.42972535 -0.5975518
  0.40797153]
Sparsity at: 0.2700864012021037
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5714e-05 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.43510303 -0.5928668
  0.39725143]
Sparsity at: 0.2700864012021037
Epoch 321/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4296e-04 - accuracy: 0.9999 - val_loss: 0.1188 - val_accuracy: 0.9814
[ 0.01880677  0.          0.03845737 ...  0.43313786 -0.58579254
  0.38436073]
Sparsity at: 0.2700864012021037
Epoch 322/500
235/235 [==============================] - 3s 13ms/step - loss: 4.9333e-04 - accuracy: 0.9999 - val_loss: 0.1233 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.42589295 -0.6013268
  0.39300346]
Sparsity at: 0.2700864012021037
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5335e-04 - accuracy: 0.9999 - val_loss: 0.1250 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.42929596 -0.5873119
  0.41284811]
Sparsity at: 0.2700864012021037
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5569e-04 - accuracy: 0.9999 - val_loss: 0.1256 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.41426408 -0.57459337
  0.4235511 ]
Sparsity at: 0.2700864012021037
Epoch 325/500
235/235 [==============================] - 3s 13ms/step - loss: 4.3872e-04 - accuracy: 0.9998 - val_loss: 0.1293 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.422124   -0.58939534
  0.41267574]
Sparsity at: 0.2700864012021037
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1553 - val_accuracy: 0.9787
[ 0.01880677  0.          0.03845737 ...  0.42896804 -0.5908288
  0.4117715 ]
Sparsity at: 0.2700864012021037
Epoch 327/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4508e-04 - accuracy: 0.9998 - val_loss: 0.1311 - val_accuracy: 0.9813
[ 0.01880677  0.          0.03845737 ...  0.42904016 -0.59808546
  0.41232345]
Sparsity at: 0.2700864012021037
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0160e-04 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9807
[ 0.01880677  0.          0.03845737 ...  0.42890304 -0.60567963
  0.40989468]
Sparsity at: 0.2700864012021037
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2469e-04 - accuracy: 1.0000 - val_loss: 0.1419 - val_accuracy: 0.9813
[ 0.01880677  0.          0.03845737 ...  0.4193758  -0.63648313
  0.42645466]
Sparsity at: 0.2700864012021037
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0493e-04 - accuracy: 0.9998 - val_loss: 0.1458 - val_accuracy: 0.9795
[ 0.01880677  0.          0.03845737 ...  0.42119902 -0.61875105
  0.41385153]
Sparsity at: 0.2700864012021037
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4694e-04 - accuracy: 0.9998 - val_loss: 0.1353 - val_accuracy: 0.9802
[ 0.01880677  0.          0.03845737 ...  0.42231616 -0.62632924
  0.41865134]
Sparsity at: 0.2700864012021037
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 8.6802e-04 - accuracy: 0.9997 - val_loss: 0.1416 - val_accuracy: 0.9801
[ 0.01880677  0.          0.03845737 ...  0.4231663  -0.61787933
  0.41199622]
Sparsity at: 0.2700864012021037
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 2.9512e-04 - accuracy: 0.9999 - val_loss: 0.1363 - val_accuracy: 0.9815
[ 0.01880677  0.          0.03845737 ...  0.42298537 -0.61879206
  0.41606992]
Sparsity at: 0.2700864012021037
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1669e-05 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.4234758  -0.61653227
  0.4089424 ]
Sparsity at: 0.2700864012021037
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2091e-05 - accuracy: 1.0000 - val_loss: 0.1323 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.4228547  -0.6168121
  0.4101582 ]
Sparsity at: 0.2700864012021037
Epoch 336/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3946e-05 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.4233637  -0.61846846
  0.4107243 ]
Sparsity at: 0.2700864012021037
Epoch 337/500
235/235 [==============================] - 3s 13ms/step - loss: 4.6527e-05 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.42081243 -0.61313814
  0.40760744]
Sparsity at: 0.2700864012021037
Epoch 338/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1333 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.41643348 -0.6186158
  0.4007658 ]
Sparsity at: 0.2700864012021037
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.1574 - val_accuracy: 0.9806
[ 0.01880677  0.          0.03845737 ...  0.43208918 -0.6476024
  0.4021684 ]
Sparsity at: 0.2700864012021037
Epoch 340/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1398 - val_accuracy: 0.9801
[ 0.01880677  0.          0.03845737 ...  0.4421755  -0.64383507
  0.40396574]
Sparsity at: 0.2700864012021037
Epoch 341/500
235/235 [==============================] - 3s 13ms/step - loss: 5.4276e-04 - accuracy: 0.9998 - val_loss: 0.1397 - val_accuracy: 0.9810
[ 0.01880677  0.          0.03845737 ...  0.44869712 -0.63456476
  0.40345833]
Sparsity at: 0.2700864012021037
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9778e-04 - accuracy: 0.9999 - val_loss: 0.1340 - val_accuracy: 0.9812
[ 0.01880677  0.          0.03845737 ...  0.4479818  -0.63540125
  0.4027738 ]
Sparsity at: 0.2700864012021037
Epoch 343/500
235/235 [==============================] - 3s 13ms/step - loss: 7.0454e-05 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9815
[ 0.01880677  0.          0.03845737 ...  0.44842836 -0.63507396
  0.4027824 ]
Sparsity at: 0.2700864012021037
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2659e-05 - accuracy: 1.0000 - val_loss: 0.1332 - val_accuracy: 0.9814
[ 0.01880677  0.          0.03845737 ...  0.44942498 -0.63560736
  0.4025435 ]
Sparsity at: 0.2700864012021037
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2014e-05 - accuracy: 1.0000 - val_loss: 0.1312 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.44750255 -0.63570803
  0.4047935 ]
Sparsity at: 0.2700864012021037
Epoch 346/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4643e-05 - accuracy: 1.0000 - val_loss: 0.1356 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.44930214 -0.63584536
  0.4038901 ]
Sparsity at: 0.2700864012021037
Epoch 347/500
235/235 [==============================] - 3s 13ms/step - loss: 3.8809e-05 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.44775516 -0.6362284
  0.4068559 ]
Sparsity at: 0.2700864012021037
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6870e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9814
[ 0.01880677  0.          0.03845737 ...  0.44722083 -0.6346979
  0.4073854 ]
Sparsity at: 0.2700864012021037
Epoch 349/500
235/235 [==============================] - 3s 13ms/step - loss: 1.5981e-05 - accuracy: 1.0000 - val_loss: 0.1294 - val_accuracy: 0.9815
[ 0.01880677  0.          0.03845737 ...  0.44566643 -0.63472396
  0.40778485]
Sparsity at: 0.2700864012021037
Epoch 350/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9011e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.4450856  -0.6330681
  0.40925416]
Sparsity at: 0.2700864012021037
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.5644655648506856
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.5600278243738117
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.8163510335207533
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 197s 11ms/step - loss: 1.0384e-05 - accuracy: 1.0000 - val_loss: 0.1291 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.4451666  -0.633469
  0.40874115]
Sparsity at: 0.2700864012021037
Epoch 352/500
235/235 [==============================] - 3s 13ms/step - loss: 8.6306e-06 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9813
[ 0.01880677  0.          0.03845737 ...  0.44577155 -0.63642395
  0.40825918]
Sparsity at: 0.2700864012021037
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2373e-06 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9812
[ 0.01880677  0.          0.03845737 ...  0.44691172 -0.6360387
  0.4089757 ]
Sparsity at: 0.2700864012021037
Epoch 354/500
235/235 [==============================] - 3s 13ms/step - loss: 6.0818e-06 - accuracy: 1.0000 - val_loss: 0.1290 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.44685388 -0.6367842
  0.40975323]
Sparsity at: 0.2700864012021037
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2330e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9814
[ 0.01880677  0.          0.03845737 ...  0.4484082  -0.63781106
  0.4159827 ]
Sparsity at: 0.2700864012021037
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1555 - val_accuracy: 0.9786
[ 0.01880677  0.          0.03845737 ...  0.46490914 -0.61572325
  0.4567466 ]
Sparsity at: 0.2700864012021037
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.1555 - val_accuracy: 0.9782
[ 0.01880677  0.          0.03845737 ...  0.43958744 -0.60434747
  0.43700954]
Sparsity at: 0.2700864012021037
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1367 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.44159612 -0.6271154
  0.43310794]
Sparsity at: 0.2700864012021037
Epoch 359/500
235/235 [==============================] - 3s 13ms/step - loss: 6.1194e-04 - accuracy: 0.9999 - val_loss: 0.1316 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.44574755 -0.6251823
  0.42895037]
Sparsity at: 0.2700864012021037
Epoch 360/500
235/235 [==============================] - 3s 13ms/step - loss: 6.2409e-05 - accuracy: 1.0000 - val_loss: 0.1322 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.44672886 -0.62360114
  0.428358  ]
Sparsity at: 0.2700864012021037
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6384e-04 - accuracy: 0.9999 - val_loss: 0.1328 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.44695884 -0.6236495
  0.42847192]
Sparsity at: 0.2700864012021037
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2636e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.44764778 -0.62459743
  0.42766157]
Sparsity at: 0.2700864012021037
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0133e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.4477151  -0.6235162
  0.4275271 ]
Sparsity at: 0.2700864012021037
Epoch 364/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4958e-05 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.44712654 -0.6234714
  0.42802516]
Sparsity at: 0.2700864012021037
Epoch 365/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3007e-05 - accuracy: 1.0000 - val_loss: 0.1305 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.44765165 -0.6232419
  0.4275212 ]
Sparsity at: 0.2700864012021037
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6517e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.44750014 -0.6245654
  0.4277241 ]
Sparsity at: 0.2700864012021037
Epoch 367/500
235/235 [==============================] - 3s 13ms/step - loss: 4.0779e-05 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.44877905 -0.6284608
  0.42834488]
Sparsity at: 0.2700864012021037
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5644e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.4506028  -0.62678146
  0.42664927]
Sparsity at: 0.2700864012021037
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4897e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.45197487 -0.62553996
  0.42520854]
Sparsity at: 0.2700864012021037
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0477e-05 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.45133063 -0.62686414
  0.42566523]
Sparsity at: 0.2700864012021037
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8011e-04 - accuracy: 0.9999 - val_loss: 0.1409 - val_accuracy: 0.9801
[ 0.01880677  0.          0.03845737 ...  0.4507499  -0.6270846
  0.418127  ]
Sparsity at: 0.2700864012021037
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 8.3508e-04 - accuracy: 0.9998 - val_loss: 0.1442 - val_accuracy: 0.9802
[ 0.01880677  0.          0.03845737 ...  0.44715053 -0.6429228
  0.43147108]
Sparsity at: 0.2700864012021037
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.1411 - val_accuracy: 0.9800
[ 0.01880677  0.          0.03845737 ...  0.43295485 -0.6394391
  0.41250584]
Sparsity at: 0.2700864012021037
Epoch 374/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1417 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.44535258 -0.62315625
  0.39839727]
Sparsity at: 0.2700864012021037
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5793e-04 - accuracy: 0.9999 - val_loss: 0.1343 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.4467874  -0.6337322
  0.3953345 ]
Sparsity at: 0.2700864012021037
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9581e-04 - accuracy: 0.9999 - val_loss: 0.1335 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.44663742 -0.63570327
  0.39670038]
Sparsity at: 0.2700864012021037
Epoch 377/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7601e-04 - accuracy: 1.0000 - val_loss: 0.1330 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.44553074 -0.6353643
  0.3965377 ]
Sparsity at: 0.2700864012021037
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0936e-05 - accuracy: 1.0000 - val_loss: 0.1325 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.44526246 -0.6364899
  0.39647025]
Sparsity at: 0.2700864012021037
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5624e-05 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.44557664 -0.637094
  0.39603493]
Sparsity at: 0.2700864012021037
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6198e-05 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.45825878 -0.63764393
  0.39865378]
Sparsity at: 0.2700864012021037
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7758e-04 - accuracy: 0.9999 - val_loss: 0.1307 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.4549328  -0.6383177
  0.39185613]
Sparsity at: 0.2700864012021037
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5026e-04 - accuracy: 0.9998 - val_loss: 0.1344 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.4535509  -0.63780546
  0.39373192]
Sparsity at: 0.2700864012021037
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6417e-04 - accuracy: 0.9999 - val_loss: 0.1314 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.44011182 -0.6319519
  0.4048404 ]
Sparsity at: 0.2700864012021037
Epoch 384/500
235/235 [==============================] - 3s 13ms/step - loss: 5.0730e-04 - accuracy: 0.9998 - val_loss: 0.1335 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.46084502 -0.64427954
  0.38993615]
Sparsity at: 0.2700864012021037
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8395e-04 - accuracy: 0.9999 - val_loss: 0.1381 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.45769942 -0.64657545
  0.3885706 ]
Sparsity at: 0.2700864012021037
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1189e-04 - accuracy: 0.9999 - val_loss: 0.1475 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.45612776 -0.64769167
  0.39585263]
Sparsity at: 0.2700864012021037
Epoch 387/500
235/235 [==============================] - 3s 13ms/step - loss: 6.9048e-04 - accuracy: 0.9998 - val_loss: 0.1431 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.4588586  -0.6370141
  0.40369546]
Sparsity at: 0.2700864012021037
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7104e-04 - accuracy: 0.9998 - val_loss: 0.1424 - val_accuracy: 0.9814
[ 0.01880677  0.          0.03845737 ...  0.45682585 -0.6313816
  0.40460625]
Sparsity at: 0.2700864012021037
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6342e-04 - accuracy: 0.9999 - val_loss: 0.1369 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.4650648  -0.6638783
  0.40846455]
Sparsity at: 0.2700864012021037
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4489e-05 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.46627247 -0.65998715
  0.40710875]
Sparsity at: 0.2700864012021037
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8615e-05 - accuracy: 1.0000 - val_loss: 0.1349 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.46676907 -0.6613538
  0.40647185]
Sparsity at: 0.2700864012021037
Epoch 392/500
235/235 [==============================] - 3s 13ms/step - loss: 9.0562e-06 - accuracy: 1.0000 - val_loss: 0.1344 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.4667732  -0.66145897
  0.40652075]
Sparsity at: 0.2700864012021037
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5482e-06 - accuracy: 1.0000 - val_loss: 0.1346 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.46673197 -0.66031224
  0.40658796]
Sparsity at: 0.2700864012021037
Epoch 394/500
235/235 [==============================] - 4s 15ms/step - loss: 9.7914e-05 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.46717578 -0.66075
  0.4064628 ]
Sparsity at: 0.2700864012021037
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7885e-04 - accuracy: 0.9998 - val_loss: 0.1548 - val_accuracy: 0.9802
[ 0.01880677  0.          0.03845737 ...  0.46122706 -0.64909434
  0.41022262]
Sparsity at: 0.2700864012021037
Epoch 396/500
235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.1685 - val_accuracy: 0.9775
[ 0.01880677  0.          0.03845737 ...  0.4667297  -0.63820547
  0.4229265 ]
Sparsity at: 0.2700864012021037
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1427 - val_accuracy: 0.9804
[ 0.01880677  0.          0.03845737 ...  0.47011888 -0.6622189
  0.40988645]
Sparsity at: 0.2700864012021037
Epoch 398/500
235/235 [==============================] - 3s 13ms/step - loss: 8.1614e-04 - accuracy: 0.9998 - val_loss: 0.1446 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.46479094 -0.6591441
  0.42159024]
Sparsity at: 0.2700864012021037
Epoch 399/500
235/235 [==============================] - 3s 13ms/step - loss: 1.2917e-04 - accuracy: 1.0000 - val_loss: 0.1448 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.46502146 -0.6647157
  0.4207599 ]
Sparsity at: 0.2700864012021037
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2641e-05 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.46341294 -0.666411
  0.42230028]
Sparsity at: 0.2700864012021037
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.6572577147876544
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25390732
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [0. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 0.]
 [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.6440891058018465
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.40593332
tf.Tensor(
[[1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [1. 0. 1. ... 1. 1. 0.]
 ...
 [1. 1. 0. ... 1. 0. 1.]
 [0. 0. 1. ... 1. 1. 0.]
 [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.8803838511902526
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32)
235/235 [==============================] - 197s 11ms/step - loss: 2.0607e-05 - accuracy: 1.0000 - val_loss: 0.1446 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.46633345 -0.66804427
  0.41892812]
Sparsity at: 0.2700864012021037
Epoch 402/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9554e-05 - accuracy: 1.0000 - val_loss: 0.1437 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.46652657 -0.668023
  0.41829896]
Sparsity at: 0.2700864012021037
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0368e-05 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.46724027 -0.66870284
  0.4183195 ]
Sparsity at: 0.2700864012021037
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 9.5840e-06 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.4663525  -0.668626
  0.4193142 ]
Sparsity at: 0.2700864012021037
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6389e-06 - accuracy: 1.0000 - val_loss: 0.1422 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.4665143  -0.66899633
  0.41944754]
Sparsity at: 0.2700864012021037
Epoch 406/500
235/235 [==============================] - 3s 13ms/step - loss: 9.0062e-06 - accuracy: 1.0000 - val_loss: 0.1424 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.46860546 -0.6689246
  0.4184126 ]
Sparsity at: 0.2700864012021037
Epoch 407/500
235/235 [==============================] - 3s 13ms/step - loss: 8.1056e-06 - accuracy: 1.0000 - val_loss: 0.1426 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.46873325 -0.6685833
  0.4183288 ]
Sparsity at: 0.2700864012021037
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4066e-06 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.46880314 -0.6681805
  0.41848987]
Sparsity at: 0.2700864012021037
Epoch 409/500
235/235 [==============================] - 3s 13ms/step - loss: 3.7950e-04 - accuracy: 0.9999 - val_loss: 0.1561 - val_accuracy: 0.9810
[ 0.01880677  0.          0.03845737 ...  0.45084572 -0.6686814
  0.43604264]
Sparsity at: 0.2700864012021037
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9755e-04 - accuracy: 0.9998 - val_loss: 0.1550 - val_accuracy: 0.9816
[ 0.01880677  0.          0.03845737 ...  0.4656313  -0.6752855
  0.40138668]
Sparsity at: 0.2700864012021037
Epoch 411/500
235/235 [==============================] - 3s 13ms/step - loss: 6.2412e-04 - accuracy: 0.9998 - val_loss: 0.1466 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.4507388  -0.6743006
  0.41448167]
Sparsity at: 0.2700864012021037
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 8.8058e-04 - accuracy: 0.9997 - val_loss: 0.1463 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.45489672 -0.69769186
  0.40985057]
Sparsity at: 0.2700864012021037
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1358 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.45985702 -0.68309146
  0.41062984]
Sparsity at: 0.2700864012021037
Epoch 414/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9374e-04 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.45656508 -0.69363266
  0.41802064]
Sparsity at: 0.2700864012021037
Epoch 415/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3520e-04 - accuracy: 0.9999 - val_loss: 0.1323 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.47364724 -0.69193727
  0.3999162 ]
Sparsity at: 0.2700864012021037
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3129e-04 - accuracy: 0.9999 - val_loss: 0.1365 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.4739784  -0.68859583
  0.3998263 ]
Sparsity at: 0.2700864012021037
Epoch 417/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3219e-04 - accuracy: 0.9999 - val_loss: 0.1390 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.47405094 -0.6805354
  0.40050626]
Sparsity at: 0.2700864012021037
Epoch 418/500
235/235 [==============================] - 3s 13ms/step - loss: 1.7869e-04 - accuracy: 0.9999 - val_loss: 0.1406 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.46566617 -0.6805592
  0.40232405]
Sparsity at: 0.2700864012021037
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6626e-05 - accuracy: 1.0000 - val_loss: 0.1407 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.46778002 -0.68003017
  0.40077406]
Sparsity at: 0.2700864012021037
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4312e-05 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.46637502 -0.6790302
  0.4022436 ]
Sparsity at: 0.2700864012021037
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6845e-05 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.4661699  -0.68192846
  0.40519503]
Sparsity at: 0.2700864012021037
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3993e-04 - accuracy: 0.9999 - val_loss: 0.1360 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.46617717 -0.6790349
  0.40467882]
Sparsity at: 0.2700864012021037
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4790e-04 - accuracy: 0.9999 - val_loss: 0.1387 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.46748582 -0.67802894
  0.40360326]
Sparsity at: 0.2700864012021037
Epoch 424/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9054e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.46735564 -0.67733115
  0.40376613]
Sparsity at: 0.2700864012021037
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1974e-05 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.4680344  -0.67662156
  0.40240416]
Sparsity at: 0.2700864012021037
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7721e-06 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.4682882  -0.6771541
  0.4021718 ]
Sparsity at: 0.2700864012021037
Epoch 427/500
235/235 [==============================] - 3s 13ms/step - loss: 9.5601e-06 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.4677433  -0.6776685
  0.40427023]
Sparsity at: 0.2700864012021037
Epoch 428/500
235/235 [==============================] - 3s 13ms/step - loss: 5.7319e-06 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.46762228 -0.67821455
  0.40410846]
Sparsity at: 0.2700864012021037
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3334e-06 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.467608   -0.6782383
  0.40366098]
Sparsity at: 0.2700864012021037
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8042e-06 - accuracy: 1.0000 - val_loss: 0.1353 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.46754295 -0.67817485
  0.40394324]
Sparsity at: 0.2700864012021037
Epoch 431/500
235/235 [==============================] - 3s 13ms/step - loss: 2.7058e-06 - accuracy: 1.0000 - val_loss: 0.1349 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.46759093 -0.6779699
  0.4040676 ]
Sparsity at: 0.2700864012021037
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8740e-04 - accuracy: 0.9999 - val_loss: 0.1466 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.46744943 -0.66644967
  0.40588087]
Sparsity at: 0.2700864012021037
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1524 - val_accuracy: 0.9813
[ 0.01880677  0.          0.03845737 ...  0.46097827 -0.62711006
  0.42067784]
Sparsity at: 0.2700864012021037
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1414 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.45657557 -0.6273581
  0.4250402 ]
Sparsity at: 0.2700864012021037
Epoch 435/500
235/235 [==============================] - 3s 13ms/step - loss: 3.4893e-04 - accuracy: 0.9999 - val_loss: 0.1398 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.45802897 -0.6266265
  0.42477885]
Sparsity at: 0.2700864012021037
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2441e-04 - accuracy: 0.9999 - val_loss: 0.1402 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.45604822 -0.62752724
  0.43428692]
Sparsity at: 0.2700864012021037
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 6.8691e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9840
[ 0.01880677  0.          0.03845737 ...  0.45472613 -0.6288736
  0.4348841 ]
Sparsity at: 0.2700864012021037
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8456e-05 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9837
[ 0.01880677  0.          0.03845737 ...  0.45594355 -0.62880594
  0.43710533]
Sparsity at: 0.2700864012021037
Epoch 439/500
235/235 [==============================] - 3s 15ms/step - loss: 9.6716e-05 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.45650694 -0.62691766
  0.43600905]
Sparsity at: 0.2700864012021037
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4640e-04 - accuracy: 0.9999 - val_loss: 0.1408 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.45526773 -0.63491094
  0.4365719 ]
Sparsity at: 0.2700864012021037
Epoch 441/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4208e-04 - accuracy: 0.9999 - val_loss: 0.1438 - val_accuracy: 0.9838
[ 0.01880677  0.          0.03845737 ...  0.45614707 -0.6313941
  0.4328075 ]
Sparsity at: 0.2700864012021037
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7247e-05 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9842
[ 0.01880677  0.          0.03845737 ...  0.45721295 -0.6321107
  0.43130693]
Sparsity at: 0.2700864012021037
Epoch 443/500
235/235 [==============================] - 3s 13ms/step - loss: 1.3336e-05 - accuracy: 1.0000 - val_loss: 0.1438 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.45744625 -0.6324652
  0.4301266 ]
Sparsity at: 0.2700864012021037
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 9.2265e-06 - accuracy: 1.0000 - val_loss: 0.1434 - val_accuracy: 0.9839
[ 0.01880677  0.          0.03845737 ...  0.4575293  -0.63369817
  0.43058583]
Sparsity at: 0.2700864012021037
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4377e-04 - accuracy: 0.9999 - val_loss: 0.1509 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.4552686  -0.65081006
  0.43637168]
Sparsity at: 0.2700864012021037
Epoch 446/500
235/235 [==============================] - 3s 13ms/step - loss: 1.9970e-04 - accuracy: 0.9999 - val_loss: 0.1427 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.45342895 -0.6588442
  0.43469203]
Sparsity at: 0.2700864012021037
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7455e-04 - accuracy: 0.9999 - val_loss: 0.1465 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.45570192 -0.6619532
  0.43477646]
Sparsity at: 0.2700864012021037
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1448 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.47522023 -0.6305926
  0.42582753]
Sparsity at: 0.2700864012021037
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1343 - val_accuracy: 0.9835
[ 0.01880677  0.          0.03845737 ...  0.4954192  -0.6285819
  0.4669497 ]
Sparsity at: 0.2700864012021037
Epoch 450/500
235/235 [==============================] - 3s 13ms/step - loss: 8.5876e-04 - accuracy: 0.9997 - val_loss: 0.1420 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.50079274 -0.6303393
  0.4709089 ]
Sparsity at: 0.2700864012021037
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1441e-04 - accuracy: 0.9998 - val_loss: 0.1381 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.49741262 -0.62992424
  0.47417143]
Sparsity at: 0.2700864012021037
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3354e-04 - accuracy: 0.9999 - val_loss: 0.1448 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.49561664 -0.62809485
  0.4776332 ]
Sparsity at: 0.2700864012021037
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6338e-04 - accuracy: 0.9999 - val_loss: 0.1417 - val_accuracy: 0.9826
[ 0.01880677  0.          0.03845737 ...  0.49396494 -0.6300975
  0.48053837]
Sparsity at: 0.2700864012021037
Epoch 454/500
235/235 [==============================] - 3s 13ms/step - loss: 2.3473e-04 - accuracy: 0.9999 - val_loss: 0.1367 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.49057928 -0.6315608
  0.4860551 ]
Sparsity at: 0.2700864012021037
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6786e-05 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.49089608 -0.63261527
  0.48420486]
Sparsity at: 0.2700864012021037
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5470e-05 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.4890302  -0.6310676
  0.48348197]
Sparsity at: 0.2700864012021037
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2836e-05 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9830
[ 0.01880677  0.          0.03845737 ...  0.4887562  -0.6263034
  0.48337683]
Sparsity at: 0.2700864012021037
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6432e-04 - accuracy: 0.9999 - val_loss: 0.1344 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.48778415 -0.6281333
  0.4847315 ]
Sparsity at: 0.2700864012021037
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6053e-05 - accuracy: 1.0000 - val_loss: 0.1337 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.48816496 -0.6256419
  0.48476303]
Sparsity at: 0.2700864012021037
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 8.3425e-06 - accuracy: 1.0000 - val_loss: 0.1323 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.4886472  -0.6258442
  0.4841313 ]
Sparsity at: 0.2700864012021037
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1966e-06 - accuracy: 1.0000 - val_loss: 0.1319 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.48854548 -0.62605673
  0.48446417]
Sparsity at: 0.2700864012021037
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0274e-06 - accuracy: 1.0000 - val_loss: 0.1315 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.48861292 -0.6263668
  0.48482335]
Sparsity at: 0.2700864012021037
Epoch 463/500
235/235 [==============================] - 3s 14ms/step - loss: 6.7343e-06 - accuracy: 1.0000 - val_loss: 0.1314 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.48862886 -0.6252509
  0.48466864]
Sparsity at: 0.2700864012021037
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4802e-06 - accuracy: 1.0000 - val_loss: 0.1306 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.48871908 -0.62547314
  0.48476958]
Sparsity at: 0.2700864012021037
Epoch 465/500
235/235 [==============================] - 3s 13ms/step - loss: 3.8455e-06 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.48870808 -0.6255478
  0.4848354 ]
Sparsity at: 0.2700864012021037
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 3.3227e-06 - accuracy: 1.0000 - val_loss: 0.1303 - val_accuracy: 0.9833
[ 0.01880677  0.          0.03845737 ...  0.48873988 -0.6258117
  0.48489904]
Sparsity at: 0.2700864012021037
Epoch 467/500
235/235 [==============================] - 3s 13ms/step - loss: 3.3459e-06 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9834
[ 0.01880677  0.          0.03845737 ...  0.4889535  -0.6255376
  0.4848146 ]
Sparsity at: 0.2700864012021037
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2425e-06 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.4885305  -0.62568027
  0.4854094 ]
Sparsity at: 0.2700864012021037
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7768e-06 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9832
[ 0.01880677  0.          0.03845737 ...  0.4881956  -0.62559664
  0.4857285 ]
Sparsity at: 0.2700864012021037
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3299e-06 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9837
[ 0.01880677  0.          0.03845737 ...  0.48870555 -0.6255467
  0.4857981 ]
Sparsity at: 0.2700864012021037
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3181e-06 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.4888284  -0.6260802
  0.48551163]
Sparsity at: 0.2700864012021037
Epoch 472/500
235/235 [==============================] - 4s 16ms/step - loss: 3.5649e-06 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9841
[ 0.01880677  0.          0.03845737 ...  0.48941073 -0.6271921
  0.48552984]
Sparsity at: 0.2700864012021037
Epoch 473/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6748e-06 - accuracy: 1.0000 - val_loss: 0.1303 - val_accuracy: 0.9836
[ 0.01880677  0.          0.03845737 ...  0.48932803 -0.6267637
  0.48558357]
Sparsity at: 0.2700864012021037
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3095e-04 - accuracy: 0.9998 - val_loss: 0.1749 - val_accuracy: 0.9794
[ 0.01880677  0.          0.03845737 ...  0.4655318  -0.628347
  0.46190992]
Sparsity at: 0.2700864012021037
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0050 - accuracy: 0.9985 - val_loss: 0.1691 - val_accuracy: 0.9798
[ 0.01880677  0.          0.03845737 ...  0.44827434 -0.62281805
  0.5273579 ]
Sparsity at: 0.2700864012021037
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1462 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.44287345 -0.61887336
  0.52848494]
Sparsity at: 0.2700864012021037
Epoch 477/500
235/235 [==============================] - 3s 13ms/step - loss: 2.5482e-04 - accuracy: 0.9999 - val_loss: 0.1387 - val_accuracy: 0.9828
[ 0.01880677  0.          0.03845737 ...  0.44191244 -0.6289943
  0.52140516]
Sparsity at: 0.2700864012021037
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 6.6499e-05 - accuracy: 1.0000 - val_loss: 0.1401 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.44295457 -0.62999314
  0.52239364]
Sparsity at: 0.2700864012021037
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4158e-05 - accuracy: 1.0000 - val_loss: 0.1402 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.44192117 -0.6276317
  0.5260622 ]
Sparsity at: 0.2700864012021037
Epoch 480/500
235/235 [==============================] - 3s 13ms/step - loss: 2.1252e-05 - accuracy: 1.0000 - val_loss: 0.1412 - val_accuracy: 0.9823
[ 0.01880677  0.          0.03845737 ...  0.44181022 -0.62668484
  0.52317286]
Sparsity at: 0.2700864012021037
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3044e-05 - accuracy: 1.0000 - val_loss: 0.1410 - val_accuracy: 0.9827
[ 0.01880677  0.          0.03845737 ...  0.44262424 -0.6269376
  0.52284724]
Sparsity at: 0.2700864012021037
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3125e-05 - accuracy: 1.0000 - val_loss: 0.1411 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.44242978 -0.627432
  0.5216963 ]
Sparsity at: 0.2700864012021037
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4894e-05 - accuracy: 1.0000 - val_loss: 0.1416 - val_accuracy: 0.9825
[ 0.01880677  0.          0.03845737 ...  0.4431408  -0.6280628
  0.52082515]
Sparsity at: 0.2700864012021037
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 4.4950e-05 - accuracy: 1.0000 - val_loss: 0.1415 - val_accuracy: 0.9821
[ 0.01880677  0.          0.03845737 ...  0.43784878 -0.6195985
  0.52076095]
Sparsity at: 0.2700864012021037
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8787e-05 - accuracy: 1.0000 - val_loss: 0.1430 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.43878213 -0.6349239
  0.5280176 ]
Sparsity at: 0.2700864012021037
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7190e-05 - accuracy: 1.0000 - val_loss: 0.1411 - val_accuracy: 0.9829
[ 0.01880677  0.          0.03845737 ...  0.43720216 -0.6323217
  0.5261143 ]
Sparsity at: 0.2700864012021037
Epoch 487/500
235/235 [==============================] - 3s 13ms/step - loss: 8.4527e-06 - accuracy: 1.0000 - val_loss: 0.1404 - val_accuracy: 0.9831
[ 0.01880677  0.          0.03845737 ...  0.4384575  -0.63301456
  0.5239528 ]
Sparsity at: 0.2700864012021037
Epoch 488/500
235/235 [==============================] - 3s 13ms/step - loss: 4.4184e-04 - accuracy: 0.9999 - val_loss: 0.1496 - val_accuracy: 0.9819
[ 0.01880677  0.          0.03845737 ...  0.44965163 -0.63156843
  0.50300837]
Sparsity at: 0.2700864012021037
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1647 - val_accuracy: 0.9796
[ 0.01880677  0.          0.03845737 ...  0.43609804 -0.65148854
  0.50748736]
Sparsity at: 0.2700864012021037
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1490 - val_accuracy: 0.9808
[ 0.01880677  0.          0.03845737 ...  0.44622424 -0.65381765
  0.5002455 ]
Sparsity at: 0.2700864012021037
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3510e-04 - accuracy: 0.9999 - val_loss: 0.1443 - val_accuracy: 0.9806
[ 0.01880677  0.          0.03845737 ...  0.44121972 -0.6252421
  0.49803033]
Sparsity at: 0.2700864012021037
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7316e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.44149444 -0.62498707
  0.49824798]
Sparsity at: 0.2700864012021037
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3295e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9817
[ 0.01880677  0.          0.03845737 ...  0.4419989  -0.6275225
  0.49887785]
Sparsity at: 0.2700864012021037
Epoch 494/500
235/235 [==============================] - 3s 13ms/step - loss: 1.4205e-04 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9814
[ 0.01880677  0.          0.03845737 ...  0.44184676 -0.6266536
  0.50754625]
Sparsity at: 0.2700864012021037
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5516e-05 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.44270357 -0.6250824
  0.5069428 ]
Sparsity at: 0.2700864012021037
Epoch 496/500
235/235 [==============================] - 3s 13ms/step - loss: 9.3779e-06 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9818
[ 0.01880677  0.          0.03845737 ...  0.4431729  -0.62458086
  0.50672305]
Sparsity at: 0.2700864012021037
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6809e-06 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.44329783 -0.6244296
  0.50652224]
Sparsity at: 0.2700864012021037
Epoch 498/500
235/235 [==============================] - 3s 13ms/step - loss: 4.5708e-06 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9820
[ 0.01880677  0.          0.03845737 ...  0.44338268 -0.62531406
  0.5064558 ]
Sparsity at: 0.2700864012021037
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6651e-06 - accuracy: 1.0000 - val_loss: 0.1421 - val_accuracy: 0.9822
[ 0.01880677  0.          0.03845737 ...  0.44348463 -0.62528926
  0.5063269 ]
Sparsity at: 0.2700864012021037
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1423e-06 - accuracy: 1.0000 - val_loss: 0.1427 - val_accuracy: 0.9824
[ 0.01880677  0.          0.03845737 ...  0.44315782 -0.62628734
  0.5062329 ]
Sparsity at: 0.2700864012021037
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.0419277586042881
Thresholhold 0.02126331627368927
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.08924054354429245
Thresholhold 0.07131436467170715
Using suggest threshold.
Applying new mask
Percentage zeros 0.40124512
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10948323458433151
Thresholhold -0.12435988336801529
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 59:45 - loss: 4.5111 - accuracy: 0.1133WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0054s vs `on_train_batch_begin` time: 2.4816s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 1.6686 - accuracy: 0.8455 - val_loss: 1.0195 - val_accuracy: 0.8994
[-8.9834053e-08  0.0000000e+00  2.7637500e-07 ...  9.2959017e-02
 -2.2156824e-01  1.0482026e-01]
Sparsity at: 0.2684582886266094
Epoch 2/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9634 - accuracy: 0.8940 - val_loss: 0.9065 - val_accuracy: 0.8988
[-3.0853472e-13  0.0000000e+00  2.2770566e-13 ...  8.2338721e-02
 -2.3112498e-01  4.4672038e-02]
Sparsity at: 0.2684582886266094
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9122 - accuracy: 0.8944 - val_loss: 0.8852 - val_accuracy: 0.8966
[-1.7760507e-18  0.0000000e+00  8.2230149e-19 ...  7.8650504e-02
 -2.2880547e-01  7.9721259e-03]
Sparsity at: 0.2684582886266094
Epoch 4/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8972 - accuracy: 0.8944 - val_loss: 0.8747 - val_accuracy: 0.8967
[-9.8042710e-24  0.0000000e+00 -1.8378601e-23 ...  7.7959441e-02
 -2.2215100e-01 -1.5425724e-02]
Sparsity at: 0.2684582886266094
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8888 - accuracy: 0.8942 - val_loss: 0.8683 - val_accuracy: 0.8967
[-3.9132513e-29  0.0000000e+00  1.3306845e-28 ...  7.8732900e-02
 -2.1589056e-01 -3.1906340e-02]
Sparsity at: 0.2684582886266094
Epoch 6/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8837 - accuracy: 0.8942 - val_loss: 0.8639 - val_accuracy: 0.8967
[-5.4838758e-34  0.0000000e+00  7.2596075e-34 ...  7.9592131e-02
 -2.1179301e-01 -4.4010080e-02]
Sparsity at: 0.2684582886266094
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8798 - accuracy: 0.8944 - val_loss: 0.8598 - val_accuracy: 0.8969
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  8.0336444e-02
 -2.0890974e-01 -5.2624092e-02]
Sparsity at: 0.2684582886266094
Epoch 8/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8768 - accuracy: 0.8945 - val_loss: 0.8564 - val_accuracy: 0.8984
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  8.0848403e-02
 -2.0676634e-01 -5.8968142e-02]
Sparsity at: 0.2684582886266094
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8743 - accuracy: 0.8950 - val_loss: 0.8543 - val_accuracy: 0.8990
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  8.1109725e-02
 -2.0475410e-01 -6.3569792e-02]
Sparsity at: 0.2684582886266094
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8724 - accuracy: 0.8952 - val_loss: 0.8520 - val_accuracy: 0.8991
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  8.1877403e-02
 -2.0312461e-01 -6.7121670e-02]
Sparsity at: 0.2684582886266094
Epoch 11/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8707 - accuracy: 0.8955 - val_loss: 0.8504 - val_accuracy: 0.8998
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  8.2827128e-02
 -2.0135188e-01 -7.0030734e-02]
Sparsity at: 0.2684582886266094
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8696 - accuracy: 0.8955 - val_loss: 0.8496 - val_accuracy: 0.8998
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  8.3823629e-02
 -1.9927554e-01 -7.2568446e-02]
Sparsity at: 0.2684582886266094
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8685 - accuracy: 0.8957 - val_loss: 0.8482 - val_accuracy: 0.8992
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  8.5966997e-02
 -1.9705325e-01 -7.4851796e-02]
Sparsity at: 0.2684582886266094
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8676 - accuracy: 0.8962 - val_loss: 0.8474 - val_accuracy: 0.8999
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  8.7693244e-02
 -1.9499266e-01 -7.6929368e-02]
Sparsity at: 0.2684582886266094
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8667 - accuracy: 0.8964 - val_loss: 0.8465 - val_accuracy: 0.9001
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  9.0563200e-02
 -1.9279966e-01 -7.9493463e-02]
Sparsity at: 0.2684582886266094
Epoch 16/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8661 - accuracy: 0.8961 - val_loss: 0.8459 - val_accuracy: 0.9006
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  9.3673728e-02
 -1.9049768e-01 -8.0761567e-02]
Sparsity at: 0.2684582886266094
Epoch 17/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8652 - accuracy: 0.8964 - val_loss: 0.8455 - val_accuracy: 0.9001
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  9.7277142e-02
 -1.8852895e-01 -8.2394883e-02]
Sparsity at: 0.2684582886266094
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8653 - accuracy: 0.8963 - val_loss: 0.8449 - val_accuracy: 0.9008
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.0137876e-01
 -1.8637373e-01 -8.3329752e-02]
Sparsity at: 0.2684582886266094
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8645 - accuracy: 0.8965 - val_loss: 0.8449 - val_accuracy: 0.9009
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.0583045e-01
 -1.8402047e-01 -8.3982341e-02]
Sparsity at: 0.2684582886266094
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8643 - accuracy: 0.8963 - val_loss: 0.8445 - val_accuracy: 0.9006
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.1039498e-01
 -1.8128894e-01 -8.4096767e-02]
Sparsity at: 0.2684582886266094
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8641 - accuracy: 0.8964 - val_loss: 0.8443 - val_accuracy: 0.9006
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.1552964e-01
 -1.7875883e-01 -8.4102675e-02]
Sparsity at: 0.2684582886266094
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8637 - accuracy: 0.8967 - val_loss: 0.8440 - val_accuracy: 0.9006
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2074105e-01
 -1.7593881e-01 -8.3336130e-02]
Sparsity at: 0.2684582886266094
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8632 - accuracy: 0.8967 - val_loss: 0.8438 - val_accuracy: 0.9008
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2579715e-01
 -1.7305377e-01 -8.2584061e-02]
Sparsity at: 0.2684582886266094
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8632 - accuracy: 0.8968 - val_loss: 0.8433 - val_accuracy: 0.9009
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3128863e-01
 -1.7029977e-01 -8.1688486e-02]
Sparsity at: 0.2684582886266094
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8630 - accuracy: 0.8970 - val_loss: 0.8431 - val_accuracy: 0.9010
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3630304e-01
 -1.6688475e-01 -8.0261007e-02]
Sparsity at: 0.2684582886266094
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8627 - accuracy: 0.8967 - val_loss: 0.8433 - val_accuracy: 0.9011
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.4104909e-01
 -1.6343622e-01 -7.8545250e-02]
Sparsity at: 0.2684582886266094
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8625 - accuracy: 0.8969 - val_loss: 0.8430 - val_accuracy: 0.9013
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.4602126e-01
 -1.6011302e-01 -7.6961733e-02]
Sparsity at: 0.2684582886266094
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8625 - accuracy: 0.8970 - val_loss: 0.8426 - val_accuracy: 0.9011
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5008494e-01
 -1.5658461e-01 -7.5381815e-02]
Sparsity at: 0.2684582886266094
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8621 - accuracy: 0.8968 - val_loss: 0.8422 - val_accuracy: 0.9012
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5344375e-01
 -1.5296414e-01 -7.3594399e-02]
Sparsity at: 0.2684582886266094
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8974 - val_loss: 0.8421 - val_accuracy: 0.9009
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5623417e-01
 -1.4959928e-01 -7.2117716e-02]
Sparsity at: 0.2684582886266094
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8971 - val_loss: 0.8416 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5851764e-01
 -1.4622952e-01 -7.0940711e-02]
Sparsity at: 0.2684582886266094
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8972 - val_loss: 0.8413 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5999149e-01
 -1.4289366e-01 -6.9045879e-02]
Sparsity at: 0.2684582886266094
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8612 - accuracy: 0.8972 - val_loss: 0.8414 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.6096839e-01
 -1.3972487e-01 -6.7543022e-02]
Sparsity at: 0.2684582886266094
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8612 - accuracy: 0.8970 - val_loss: 0.8416 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.6125178e-01
 -1.3688794e-01 -6.6141121e-02]
Sparsity at: 0.2684582886266094
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8612 - accuracy: 0.8971 - val_loss: 0.8410 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.6116372e-01
 -1.3457474e-01 -6.4589985e-02]
Sparsity at: 0.2684582886266094
Epoch 36/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8608 - accuracy: 0.8974 - val_loss: 0.8415 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.6038267e-01
 -1.3253972e-01 -6.2733725e-02]
Sparsity at: 0.2684582886266094
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.8973 - val_loss: 0.8411 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5946534e-01
 -1.3117670e-01 -6.0914136e-02]
Sparsity at: 0.2684582886266094
Epoch 38/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8606 - accuracy: 0.8972 - val_loss: 0.8412 - val_accuracy: 0.9012
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5804388e-01
 -1.3005283e-01 -5.8641482e-02]
Sparsity at: 0.2684582886266094
Epoch 39/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8605 - accuracy: 0.8972 - val_loss: 0.8409 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5634952e-01
 -1.2936707e-01 -5.6192875e-02]
Sparsity at: 0.2684582886266094
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8604 - accuracy: 0.8974 - val_loss: 0.8409 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5422808e-01
 -1.2921877e-01 -5.3835955e-02]
Sparsity at: 0.2684582886266094
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8603 - accuracy: 0.8976 - val_loss: 0.8406 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.5216966e-01
 -1.3010690e-01 -5.1113464e-02]
Sparsity at: 0.2684582886266094
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8600 - accuracy: 0.8974 - val_loss: 0.8406 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.4935975e-01
 -1.3109179e-01 -4.8321776e-02]
Sparsity at: 0.2684582886266094
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8601 - accuracy: 0.8972 - val_loss: 0.8406 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.4664097e-01
 -1.3282675e-01 -4.5363359e-02]
Sparsity at: 0.2684582886266094
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8598 - accuracy: 0.8974 - val_loss: 0.8403 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.4355481e-01
 -1.3472095e-01 -4.2140409e-02]
Sparsity at: 0.2684582886266094
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8600 - accuracy: 0.8975 - val_loss: 0.8397 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.4051218e-01
 -1.3746266e-01 -3.9190140e-02]
Sparsity at: 0.2684582886266094
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8596 - accuracy: 0.8979 - val_loss: 0.8401 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3740715e-01
 -1.4030695e-01 -3.6161326e-02]
Sparsity at: 0.2684582886266094
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8979 - val_loss: 0.8403 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3381296e-01
 -1.4316659e-01 -3.3144969e-02]
Sparsity at: 0.2684582886266094
Epoch 48/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8595 - accuracy: 0.8976 - val_loss: 0.8398 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3106842e-01
 -1.4616594e-01 -3.0530335e-02]
Sparsity at: 0.2684582886266094
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8594 - accuracy: 0.8975 - val_loss: 0.8398 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2804312e-01
 -1.4871861e-01 -2.7732907e-02]
Sparsity at: 0.2684582886266094
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8595 - accuracy: 0.8974 - val_loss: 0.8394 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2547843e-01
 -1.5147868e-01 -2.5067046e-02]
Sparsity at: 0.2684582886266094
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.0036432837180088717
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.019940239056950748
Thresholhold 0.10994073003530502
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [1. 1. 1. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [1. 1. 0. ... 0. 0. 0.]
 [1. 1. 0. ... 1. 0. 1.]
 [1. 0. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.11557942384274611
Thresholhold -0.10227083414793015
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 48s 7ms/step - loss: 0.8600 - accuracy: 0.8976 - val_loss: 0.8400 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2234274e-01
 -1.5507537e-01 -2.4513166e-02]
Sparsity at: 0.3020553916309013
Epoch 52/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8596 - accuracy: 0.8978 - val_loss: 0.8398 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2294033e-01
 -1.5856490e-01 -2.5542196e-02]
Sparsity at: 0.3020553916309013
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8594 - accuracy: 0.8979 - val_loss: 0.8398 - val_accuracy: 0.9029
[-5.48387584e-34  0.00000000e+00  5.09155001e-34 ...  1.24105774e-01
 -1.61454782e-01 -2.71752495e-02]
Sparsity at: 0.3020553916309013
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8595 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2485599e-01
 -1.6371562e-01 -2.8881321e-02]
Sparsity at: 0.3020553916309013
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8595 - accuracy: 0.8976 - val_loss: 0.8397 - val_accuracy: 0.9030
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2569897e-01
 -1.6562264e-01 -3.0724384e-02]
Sparsity at: 0.3020553916309013
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8595 - accuracy: 0.8978 - val_loss: 0.8393 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2636068e-01
 -1.6714354e-01 -3.2255009e-02]
Sparsity at: 0.3020553916309013
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8976 - val_loss: 0.8396 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2696125e-01
 -1.6858852e-01 -3.3573054e-02]
Sparsity at: 0.3020553916309013
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8976 - val_loss: 0.8399 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2746310e-01
 -1.6980223e-01 -3.4823522e-02]
Sparsity at: 0.3020553916309013
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8594 - accuracy: 0.8977 - val_loss: 0.8396 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2792858e-01
 -1.7073731e-01 -3.5746925e-02]
Sparsity at: 0.3020553916309013
Epoch 60/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8978 - val_loss: 0.8397 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2836723e-01
 -1.7152169e-01 -3.6618531e-02]
Sparsity at: 0.3020553916309013
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8976 - val_loss: 0.8393 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2874204e-01
 -1.7206110e-01 -3.7384976e-02]
Sparsity at: 0.3020553916309013
Epoch 62/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8593 - accuracy: 0.8976 - val_loss: 0.8397 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2877426e-01
 -1.7272711e-01 -3.7892692e-02]
Sparsity at: 0.3020553916309013
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8978 - val_loss: 0.8397 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2884371e-01
 -1.7304236e-01 -3.8338814e-02]
Sparsity at: 0.3020553916309013
Epoch 64/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8593 - accuracy: 0.8975 - val_loss: 0.8396 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2930056e-01
 -1.7367229e-01 -3.8727719e-02]
Sparsity at: 0.3020553916309013
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8977 - val_loss: 0.8395 - val_accuracy: 0.9032
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2944511e-01
 -1.7363392e-01 -3.9231349e-02]
Sparsity at: 0.3020553916309013
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8977 - val_loss: 0.8394 - val_accuracy: 0.9032
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2966843e-01
 -1.7406814e-01 -3.9438769e-02]
Sparsity at: 0.3020553916309013
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8976 - val_loss: 0.8394 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2981075e-01
 -1.7430477e-01 -3.9743174e-02]
Sparsity at: 0.3020553916309013
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8977 - val_loss: 0.8395 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3019060e-01
 -1.7477098e-01 -4.0095720e-02]
Sparsity at: 0.3020553916309013
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8395 - val_accuracy: 0.9033
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3001566e-01
 -1.7477211e-01 -4.0001515e-02]
Sparsity at: 0.3020553916309013
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8979 - val_loss: 0.8394 - val_accuracy: 0.9031
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3010490e-01
 -1.7500022e-01 -4.0189072e-02]
Sparsity at: 0.3020553916309013
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8977 - val_loss: 0.8395 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3003357e-01
 -1.7517424e-01 -4.0266704e-02]
Sparsity at: 0.3020553916309013
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8975 - val_loss: 0.8395 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3006732e-01
 -1.7537594e-01 -4.0378712e-02]
Sparsity at: 0.3020553916309013
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9032
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3020296e-01
 -1.7545526e-01 -4.0520452e-02]
Sparsity at: 0.3020553916309013
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8978 - val_loss: 0.8393 - val_accuracy: 0.9034
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3042489e-01
 -1.7538187e-01 -4.0536780e-02]
Sparsity at: 0.3020553916309013
Epoch 75/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8396 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3049713e-01
 -1.7596348e-01 -4.0737361e-02]
Sparsity at: 0.3020553916309013
Epoch 76/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.8977 - val_loss: 0.8394 - val_accuracy: 0.9034
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3060771e-01
 -1.7591920e-01 -4.0577546e-02]
Sparsity at: 0.3020553916309013
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8978 - val_loss: 0.8393 - val_accuracy: 0.9030
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3076569e-01
 -1.7591909e-01 -4.0690858e-02]
Sparsity at: 0.3020553916309013
Epoch 78/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8395 - val_accuracy: 0.9032
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3076486e-01
 -1.7612094e-01 -4.0782146e-02]
Sparsity at: 0.3020553916309013
Epoch 79/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.8977 - val_loss: 0.8393 - val_accuracy: 0.9031
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3081729e-01
 -1.7613031e-01 -4.0732473e-02]
Sparsity at: 0.3020553916309013
Epoch 80/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8978 - val_loss: 0.8390 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3082041e-01
 -1.7621996e-01 -4.0636394e-02]
Sparsity at: 0.3020553916309013
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8394 - val_accuracy: 0.9033
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3098948e-01
 -1.7644405e-01 -4.0798191e-02]
Sparsity at: 0.3020553916309013
Epoch 82/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8978 - val_loss: 0.8392 - val_accuracy: 0.9031
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3130747e-01
 -1.7655924e-01 -4.0756479e-02]
Sparsity at: 0.3020553916309013
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8976 - val_loss: 0.8393 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3120663e-01
 -1.7677036e-01 -4.0497895e-02]
Sparsity at: 0.3020553916309013
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8979 - val_loss: 0.8395 - val_accuracy: 0.9032
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3121243e-01
 -1.7697068e-01 -4.0852841e-02]
Sparsity at: 0.3020553916309013
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8981 - val_loss: 0.8394 - val_accuracy: 0.9033
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3165174e-01
 -1.7680240e-01 -4.0843982e-02]
Sparsity at: 0.3020553916309013
Epoch 86/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.8980 - val_loss: 0.8398 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3138409e-01
 -1.7695697e-01 -4.0686388e-02]
Sparsity at: 0.3020553916309013
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8979 - val_loss: 0.8394 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3159584e-01
 -1.7705370e-01 -4.0659670e-02]
Sparsity at: 0.3020553916309013
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8975 - val_loss: 0.8394 - val_accuracy: 0.9030
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3142163e-01
 -1.7730904e-01 -4.0503614e-02]
Sparsity at: 0.3020553916309013
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3187335e-01
 -1.7745581e-01 -4.0574327e-02]
Sparsity at: 0.3020553916309013
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8974 - val_loss: 0.8399 - val_accuracy: 0.9031
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3147616e-01
 -1.7752194e-01 -4.0517319e-02]
Sparsity at: 0.3020553916309013
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8979 - val_loss: 0.8393 - val_accuracy: 0.9032
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3184576e-01
 -1.7747264e-01 -4.0603787e-02]
Sparsity at: 0.3020553916309013
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8975 - val_loss: 0.8396 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3190538e-01
 -1.7765263e-01 -4.0478241e-02]
Sparsity at: 0.3020553916309013
Epoch 93/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8589 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9031
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3190360e-01
 -1.7768240e-01 -4.0426746e-02]
Sparsity at: 0.3020553916309013
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8977 - val_loss: 0.8393 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3179721e-01
 -1.7752849e-01 -4.0478081e-02]
Sparsity at: 0.3020553916309013
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8396 - val_accuracy: 0.9033
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3203646e-01
 -1.7793046e-01 -4.0302433e-02]
Sparsity at: 0.3020553916309013
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8593 - accuracy: 0.8974 - val_loss: 0.8397 - val_accuracy: 0.9032
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3222447e-01
 -1.7781563e-01 -4.0426910e-02]
Sparsity at: 0.3020553916309013
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8977 - val_loss: 0.8392 - val_accuracy: 0.9031
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3222960e-01
 -1.7816213e-01 -4.0201418e-02]
Sparsity at: 0.3020553916309013
Epoch 98/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9038
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3217404e-01
 -1.7805555e-01 -4.0291063e-02]
Sparsity at: 0.3020553916309013
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8395 - val_accuracy: 0.9033
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3215074e-01
 -1.7794009e-01 -4.0262833e-02]
Sparsity at: 0.3020553916309013
Epoch 100/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8977 - val_loss: 0.8395 - val_accuracy: 0.9036
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3230026e-01
 -1.7811580e-01 -4.0080838e-02]
Sparsity at: 0.3020553916309013
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.009493211909383348
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.03640017389897343
Thresholhold 0.10112528502941132
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.7593994
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.13820159512233054
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 46s 7ms/step - loss: 0.8635 - accuracy: 0.8981 - val_loss: 0.8433 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2821461e-01
 -1.8314569e-01 -3.8852900e-02]
Sparsity at: 0.31764686158798283
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8625 - accuracy: 0.8977 - val_loss: 0.8432 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2682478e-01
 -1.8493056e-01 -4.0374514e-02]
Sparsity at: 0.31764686158798283
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8623 - accuracy: 0.8976 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2700060e-01
 -1.8594225e-01 -4.1738406e-02]
Sparsity at: 0.31764686158798283
Epoch 104/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8623 - accuracy: 0.8974 - val_loss: 0.8429 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2713227e-01
 -1.8635452e-01 -4.2957414e-02]
Sparsity at: 0.31764686158798283
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8622 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2701347e-01
 -1.8701567e-01 -4.3908153e-02]
Sparsity at: 0.31764686158798283
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8622 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2704466e-01
 -1.8764000e-01 -4.4646252e-02]
Sparsity at: 0.31764686158798283
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2732610e-01
 -1.8808383e-01 -4.5259889e-02]
Sparsity at: 0.31764686158798283
Epoch 108/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2724626e-01
 -1.8865944e-01 -4.5633864e-02]
Sparsity at: 0.31764686158798283
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8621 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2751086e-01
 -1.8911141e-01 -4.6109665e-02]
Sparsity at: 0.31764686158798283
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2767273e-01
 -1.8939529e-01 -4.6456531e-02]
Sparsity at: 0.31764686158798283
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8620 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2795898e-01
 -1.8991999e-01 -4.6820179e-02]
Sparsity at: 0.31764686158798283
Epoch 112/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2815933e-01
 -1.9008371e-01 -4.7333226e-02]
Sparsity at: 0.31764686158798283
Epoch 113/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2815264e-01
 -1.9031869e-01 -4.7475237e-02]
Sparsity at: 0.31764686158798283
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2849016e-01
 -1.9082949e-01 -4.7659192e-02]
Sparsity at: 0.31764686158798283
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2845236e-01
 -1.9101778e-01 -4.7793601e-02]
Sparsity at: 0.31764686158798283
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2865999e-01
 -1.9145867e-01 -4.8006583e-02]
Sparsity at: 0.31764686158798283
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2885745e-01
 -1.9148713e-01 -4.8114952e-02]
Sparsity at: 0.31764686158798283
Epoch 118/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2908526e-01
 -1.9163844e-01 -4.8208833e-02]
Sparsity at: 0.31764686158798283
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8619 - accuracy: 0.8976 - val_loss: 0.8424 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2897655e-01
 -1.9180900e-01 -4.8430927e-02]
Sparsity at: 0.31764686158798283
Epoch 120/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2923451e-01
 -1.9227915e-01 -4.8585393e-02]
Sparsity at: 0.31764686158798283
Epoch 121/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2933771e-01
 -1.9254167e-01 -4.8641730e-02]
Sparsity at: 0.31764686158798283
Epoch 122/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2950709e-01
 -1.9256245e-01 -4.8787158e-02]
Sparsity at: 0.31764686158798283
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2952569e-01
 -1.9285975e-01 -4.8726380e-02]
Sparsity at: 0.31764686158798283
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8619 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2968600e-01
 -1.9273987e-01 -4.8867244e-02]
Sparsity at: 0.31764686158798283
Epoch 125/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2951250e-01
 -1.9285864e-01 -4.8959654e-02]
Sparsity at: 0.31764686158798283
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2962729e-01
 -1.9309379e-01 -4.9072322e-02]
Sparsity at: 0.31764686158798283
Epoch 127/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.2979282e-01
 -1.9322781e-01 -4.8913687e-02]
Sparsity at: 0.31764686158798283
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3003753e-01
 -1.9343826e-01 -4.8896972e-02]
Sparsity at: 0.31764686158798283
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3030161e-01
 -1.9369686e-01 -4.9045946e-02]
Sparsity at: 0.31764686158798283
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3017318e-01
 -1.9368851e-01 -4.8820440e-02]
Sparsity at: 0.31764686158798283
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3038777e-01
 -1.9370711e-01 -4.8804794e-02]
Sparsity at: 0.31764686158798283
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3031024e-01
 -1.9387725e-01 -4.8685513e-02]
Sparsity at: 0.31764686158798283
Epoch 133/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3041861e-01
 -1.9399741e-01 -4.8672013e-02]
Sparsity at: 0.31764686158798283
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8619 - accuracy: 0.8977 - val_loss: 0.8429 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3065943e-01
 -1.9398069e-01 -4.8678558e-02]
Sparsity at: 0.31764686158798283
Epoch 135/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3075496e-01
 -1.9422056e-01 -4.8466679e-02]
Sparsity at: 0.31764686158798283
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3086253e-01
 -1.9430256e-01 -4.8664290e-02]
Sparsity at: 0.31764686158798283
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3089217e-01
 -1.9445390e-01 -4.8425332e-02]
Sparsity at: 0.31764686158798283
Epoch 138/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3113028e-01
 -1.9454832e-01 -4.8509661e-02]
Sparsity at: 0.31764686158798283
Epoch 139/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3124588e-01
 -1.9458926e-01 -4.8391465e-02]
Sparsity at: 0.31764686158798283
Epoch 140/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3110988e-01
 -1.9467093e-01 -4.8345648e-02]
Sparsity at: 0.31764686158798283
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3116015e-01
 -1.9473970e-01 -4.8383031e-02]
Sparsity at: 0.31764686158798283
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8975 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3114993e-01
 -1.9499183e-01 -4.8224635e-02]
Sparsity at: 0.31764686158798283
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8429 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3117220e-01
 -1.9513275e-01 -4.8479598e-02]
Sparsity at: 0.31764686158798283
Epoch 144/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8978 - val_loss: 0.8429 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3135523e-01
 -1.9529510e-01 -4.8400950e-02]
Sparsity at: 0.31764686158798283
Epoch 145/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3129324e-01
 -1.9500449e-01 -4.8331078e-02]
Sparsity at: 0.31764686158798283
Epoch 146/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3116494e-01
 -1.9516598e-01 -4.8521806e-02]
Sparsity at: 0.31764686158798283
Epoch 147/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3150419e-01
 -1.9525765e-01 -4.8552930e-02]
Sparsity at: 0.31764686158798283
Epoch 148/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3143615e-01
 -1.9552180e-01 -4.8496269e-02]
Sparsity at: 0.31764686158798283
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8982 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3129306e-01
 -1.9555634e-01 -4.8402920e-02]
Sparsity at: 0.31764686158798283
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8976 - val_loss: 0.8427 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3145301e-01
 -1.9553630e-01 -4.8637949e-02]
Sparsity at: 0.31764686158798283
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.016370235658226484
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.05085836911255104
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.7593994
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.16201439394987815
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 49s 7ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3134664e-01
 -1.9551423e-01 -4.8543576e-02]
Sparsity at: 0.31764686158798283
Epoch 152/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3151520e-01
 -1.9551814e-01 -4.8416600e-02]
Sparsity at: 0.31764686158798283
Epoch 153/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3142766e-01
 -1.9558457e-01 -4.8482835e-02]
Sparsity at: 0.31764686158798283
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3142197e-01
 -1.9570747e-01 -4.8660621e-02]
Sparsity at: 0.31764686158798283
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3162774e-01
 -1.9576450e-01 -4.8666112e-02]
Sparsity at: 0.31764686158798283
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3142668e-01
 -1.9564982e-01 -4.8768625e-02]
Sparsity at: 0.31764686158798283
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3154729e-01
 -1.9586788e-01 -4.8639391e-02]
Sparsity at: 0.31764686158798283
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8982 - val_loss: 0.8428 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3161272e-01
 -1.9610412e-01 -4.8663873e-02]
Sparsity at: 0.31764686158798283
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3153143e-01
 -1.9595259e-01 -4.8596386e-02]
Sparsity at: 0.31764686158798283
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3161562e-01
 -1.9610885e-01 -4.8720330e-02]
Sparsity at: 0.31764686158798283
Epoch 161/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3173233e-01
 -1.9615996e-01 -4.8737977e-02]
Sparsity at: 0.31764686158798283
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3177030e-01
 -1.9607089e-01 -4.8769373e-02]
Sparsity at: 0.31764686158798283
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3174957e-01
 -1.9621418e-01 -4.8929773e-02]
Sparsity at: 0.31764686158798283
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8429 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3174346e-01
 -1.9631459e-01 -4.8848152e-02]
Sparsity at: 0.31764686158798283
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3175415e-01
 -1.9614850e-01 -4.8962120e-02]
Sparsity at: 0.31764686158798283
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3163190e-01
 -1.9631489e-01 -4.8884977e-02]
Sparsity at: 0.31764686158798283
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3167328e-01
 -1.9637337e-01 -4.8946764e-02]
Sparsity at: 0.31764686158798283
Epoch 168/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3179286e-01
 -1.9639114e-01 -4.8989117e-02]
Sparsity at: 0.31764686158798283
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3153571e-01
 -1.9633736e-01 -4.8856497e-02]
Sparsity at: 0.31764686158798283
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3183625e-01
 -1.9655170e-01 -4.8994616e-02]
Sparsity at: 0.31764686158798283
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3153484e-01
 -1.9667366e-01 -4.8872393e-02]
Sparsity at: 0.31764686158798283
Epoch 172/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9009
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3174793e-01
 -1.9650687e-01 -4.8859589e-02]
Sparsity at: 0.31764686158798283
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3166814e-01
 -1.9660209e-01 -4.9039505e-02]
Sparsity at: 0.31764686158798283
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3182704e-01
 -1.9655381e-01 -4.9036797e-02]
Sparsity at: 0.31764686158798283
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3161987e-01
 -1.9652063e-01 -4.8913248e-02]
Sparsity at: 0.31764686158798283
Epoch 176/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8976 - val_loss: 0.8423 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3184595e-01
 -1.9665773e-01 -4.8893366e-02]
Sparsity at: 0.31764686158798283
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3185468e-01
 -1.9673279e-01 -4.9085606e-02]
Sparsity at: 0.31764686158798283
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3192044e-01
 -1.9671328e-01 -4.9127519e-02]
Sparsity at: 0.31764686158798283
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3185282e-01
 -1.9672896e-01 -4.8897829e-02]
Sparsity at: 0.31764686158798283
Epoch 180/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8429 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3186932e-01
 -1.9679476e-01 -4.9031869e-02]
Sparsity at: 0.31764686158798283
Epoch 181/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3190877e-01
 -1.9668463e-01 -4.9033135e-02]
Sparsity at: 0.31764686158798283
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3218339e-01
 -1.9688039e-01 -4.9073666e-02]
Sparsity at: 0.31764686158798283
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3201900e-01
 -1.9676551e-01 -4.9048178e-02]
Sparsity at: 0.31764686158798283
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3201238e-01
 -1.9678928e-01 -4.9263485e-02]
Sparsity at: 0.31764686158798283
Epoch 185/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8429 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3194270e-01
 -1.9678959e-01 -4.9410645e-02]
Sparsity at: 0.31764686158798283
Epoch 186/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3205931e-01
 -1.9683908e-01 -4.9072742e-02]
Sparsity at: 0.31764686158798283
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3212754e-01
 -1.9685809e-01 -4.9272075e-02]
Sparsity at: 0.31764686158798283
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3204212e-01
 -1.9705476e-01 -4.9155395e-02]
Sparsity at: 0.31764686158798283
Epoch 189/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3212602e-01
 -1.9687542e-01 -4.9248464e-02]
Sparsity at: 0.31764686158798283
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3209842e-01
 -1.9694383e-01 -4.9141664e-02]
Sparsity at: 0.31764686158798283
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3217551e-01
 -1.9706658e-01 -4.9254362e-02]
Sparsity at: 0.31764686158798283
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3203961e-01
 -1.9724111e-01 -4.9024723e-02]
Sparsity at: 0.31764686158798283
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3202311e-01
 -1.9700061e-01 -4.9173471e-02]
Sparsity at: 0.31764686158798283
Epoch 194/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3214070e-01
 -1.9711721e-01 -4.9211733e-02]
Sparsity at: 0.31764686158798283
Epoch 195/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3231492e-01
 -1.9711117e-01 -4.9273893e-02]
Sparsity at: 0.31764686158798283
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.483876e-34  0.000000e+00  5.091550e-34 ...  1.323446e-01 -1.971135e-01
 -4.927989e-02]
Sparsity at: 0.31764686158798283
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9014
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3214421e-01
 -1.9714426e-01 -4.9130607e-02]
Sparsity at: 0.31764686158798283
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8982 - val_loss: 0.8424 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3242665e-01
 -1.9718294e-01 -4.9162071e-02]
Sparsity at: 0.31764686158798283
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3222337e-01
 -1.9710758e-01 -4.8979387e-02]
Sparsity at: 0.31764686158798283
Epoch 200/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3225886e-01
 -1.9716600e-01 -4.8887614e-02]
Sparsity at: 0.31764686158798283
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.02556332363108682
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.08065675018447038
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.7593994
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.187723324082814
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 64s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8422 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3235617e-01
 -1.9716236e-01 -4.8922386e-02]
Sparsity at: 0.31764686158798283
Epoch 202/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3233872e-01
 -1.9742027e-01 -4.8925292e-02]
Sparsity at: 0.31764686158798283
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3249913e-01
 -1.9743334e-01 -4.8945550e-02]
Sparsity at: 0.31764686158798283
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247161e-01
 -1.9742592e-01 -4.8983023e-02]
Sparsity at: 0.31764686158798283
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247986e-01
 -1.9758783e-01 -4.9187984e-02]
Sparsity at: 0.31764686158798283
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9020
[-5.483876e-34  0.000000e+00  5.091550e-34 ...  1.325217e-01 -1.974552e-01
 -4.928371e-02]
Sparsity at: 0.31764686158798283
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3253118e-01
 -1.9760072e-01 -4.9105503e-02]
Sparsity at: 0.31764686158798283
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3233891e-01
 -1.9747142e-01 -4.9214002e-02]
Sparsity at: 0.31764686158798283
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3243431e-01
 -1.9754511e-01 -4.8991591e-02]
Sparsity at: 0.31764686158798283
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3251102e-01
 -1.9762695e-01 -4.9119323e-02]
Sparsity at: 0.31764686158798283
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247992e-01
 -1.9771177e-01 -4.9114339e-02]
Sparsity at: 0.31764686158798283
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9026
[-5.483876e-34  0.000000e+00  5.091550e-34 ...  1.324988e-01 -1.973929e-01
 -4.904706e-02]
Sparsity at: 0.31764686158798283
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3240026e-01
 -1.9747585e-01 -4.9096629e-02]
Sparsity at: 0.31764686158798283
Epoch 214/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254574e-01
 -1.9746101e-01 -4.9201634e-02]
Sparsity at: 0.31764686158798283
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3268796e-01
 -1.9764492e-01 -4.9154725e-02]
Sparsity at: 0.31764686158798283
Epoch 216/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3244972e-01
 -1.9766811e-01 -4.9133420e-02]
Sparsity at: 0.31764686158798283
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8429 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3246946e-01
 -1.9776979e-01 -4.9316298e-02]
Sparsity at: 0.31764686158798283
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250221e-01
 -1.9760148e-01 -4.9325477e-02]
Sparsity at: 0.31764686158798283
Epoch 219/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3251768e-01
 -1.9784541e-01 -4.9305931e-02]
Sparsity at: 0.31764686158798283
Epoch 220/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3245976e-01
 -1.9786319e-01 -4.9312707e-02]
Sparsity at: 0.31764686158798283
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8983 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255097e-01
 -1.9776452e-01 -4.9196705e-02]
Sparsity at: 0.31764686158798283
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3253079e-01
 -1.9779831e-01 -4.9270019e-02]
Sparsity at: 0.31764686158798283
Epoch 223/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258941e-01
 -1.9777648e-01 -4.9184009e-02]
Sparsity at: 0.31764686158798283
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3260470e-01
 -1.9770518e-01 -4.9405504e-02]
Sparsity at: 0.31764686158798283
Epoch 225/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8422 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252379e-01
 -1.9772010e-01 -4.9401361e-02]
Sparsity at: 0.31764686158798283
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8975 - val_loss: 0.8423 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3241419e-01
 -1.9771178e-01 -4.9378376e-02]
Sparsity at: 0.31764686158798283
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8422 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269220e-01
 -1.9773848e-01 -4.9295675e-02]
Sparsity at: 0.31764686158798283
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3248172e-01
 -1.9782944e-01 -4.9281776e-02]
Sparsity at: 0.31764686158798283
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265231e-01
 -1.9786410e-01 -4.9408652e-02]
Sparsity at: 0.31764686158798283
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254000e-01
 -1.9780730e-01 -4.9428076e-02]
Sparsity at: 0.31764686158798283
Epoch 231/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254124e-01
 -1.9786313e-01 -4.9348202e-02]
Sparsity at: 0.31764686158798283
Epoch 232/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8982 - val_loss: 0.8427 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3268690e-01
 -1.9782664e-01 -4.9521375e-02]
Sparsity at: 0.31764686158798283
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3239712e-01
 -1.9786432e-01 -4.9626920e-02]
Sparsity at: 0.31764686158798283
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258721e-01
 -1.9775945e-01 -4.9586155e-02]
Sparsity at: 0.31764686158798283
Epoch 235/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3236269e-01
 -1.9794369e-01 -4.9515229e-02]
Sparsity at: 0.31764686158798283
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256113e-01
 -1.9780158e-01 -4.9612332e-02]
Sparsity at: 0.31764686158798283
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8428 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3253745e-01
 -1.9798046e-01 -4.9473926e-02]
Sparsity at: 0.31764686158798283
Epoch 238/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254429e-01
 -1.9770826e-01 -4.9647328e-02]
Sparsity at: 0.31764686158798283
Epoch 239/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257693e-01
 -1.9768004e-01 -4.9613215e-02]
Sparsity at: 0.31764686158798283
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259612e-01
 -1.9777218e-01 -4.9532041e-02]
Sparsity at: 0.31764686158798283
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3238288e-01
 -1.9778067e-01 -4.9674653e-02]
Sparsity at: 0.31764686158798283
Epoch 242/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3249508e-01
 -1.9765589e-01 -4.9639314e-02]
Sparsity at: 0.31764686158798283
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265821e-01
 -1.9788790e-01 -4.9675032e-02]
Sparsity at: 0.31764686158798283
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3270748e-01
 -1.9804683e-01 -4.9469050e-02]
Sparsity at: 0.31764686158798283
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3251804e-01
 -1.9787075e-01 -4.9816035e-02]
Sparsity at: 0.31764686158798283
Epoch 246/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8976 - val_loss: 0.8423 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3264535e-01
 -1.9795381e-01 -4.9637474e-02]
Sparsity at: 0.31764686158798283
Epoch 247/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8614 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3261926e-01
 -1.9790334e-01 -4.9785260e-02]
Sparsity at: 0.31764686158798283
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3273104e-01
 -1.9783448e-01 -4.9817681e-02]
Sparsity at: 0.31764686158798283
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259253e-01
 -1.9799730e-01 -4.9647469e-02]
Sparsity at: 0.31764686158798283
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3251796e-01
 -1.9777572e-01 -4.9746413e-02]
Sparsity at: 0.31764686158798283
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.03626223626598213
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.11141459069796689
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.7593994
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.21360910653589116
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 54s 7ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257369e-01
 -1.9780327e-01 -4.9655702e-02]
Sparsity at: 0.31764686158798283
Epoch 252/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256332e-01
 -1.9803447e-01 -4.9606662e-02]
Sparsity at: 0.31764686158798283
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247232e-01
 -1.9796558e-01 -4.9524635e-02]
Sparsity at: 0.31764686158798283
Epoch 254/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3240048e-01
 -1.9791499e-01 -4.9514562e-02]
Sparsity at: 0.31764686158798283
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3261023e-01
 -1.9786955e-01 -4.9532466e-02]
Sparsity at: 0.31764686158798283
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3261232e-01
 -1.9786939e-01 -4.9699165e-02]
Sparsity at: 0.31764686158798283
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258256e-01
 -1.9794552e-01 -4.9770962e-02]
Sparsity at: 0.31764686158798283
Epoch 258/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265368e-01
 -1.9798878e-01 -4.9749810e-02]
Sparsity at: 0.31764686158798283
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9013
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250917e-01
 -1.9787462e-01 -4.9729265e-02]
Sparsity at: 0.31764686158798283
Epoch 260/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250373e-01
 -1.9791879e-01 -4.9724240e-02]
Sparsity at: 0.31764686158798283
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258451e-01
 -1.9802029e-01 -4.9740311e-02]
Sparsity at: 0.31764686158798283
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8983 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269940e-01
 -1.9808505e-01 -4.9911484e-02]
Sparsity at: 0.31764686158798283
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3274360e-01
 -1.9800594e-01 -4.9683839e-02]
Sparsity at: 0.31764686158798283
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256845e-01
 -1.9799311e-01 -4.9821161e-02]
Sparsity at: 0.31764686158798283
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3268915e-01
 -1.9769433e-01 -4.9656745e-02]
Sparsity at: 0.31764686158798283
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247445e-01
 -1.9794540e-01 -4.9711458e-02]
Sparsity at: 0.31764686158798283
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3273451e-01
 -1.9806153e-01 -4.9621224e-02]
Sparsity at: 0.31764686158798283
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255194e-01
 -1.9782656e-01 -4.9603172e-02]
Sparsity at: 0.31764686158798283
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250780e-01
 -1.9794939e-01 -4.9568966e-02]
Sparsity at: 0.31764686158798283
Epoch 270/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256982e-01
 -1.9803466e-01 -4.9794700e-02]
Sparsity at: 0.31764686158798283
Epoch 271/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9014
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269734e-01
 -1.9795600e-01 -4.9889129e-02]
Sparsity at: 0.31764686158798283
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269490e-01
 -1.9803166e-01 -4.9882852e-02]
Sparsity at: 0.31764686158798283
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254073e-01
 -1.9798416e-01 -4.9768340e-02]
Sparsity at: 0.31764686158798283
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258573e-01
 -1.9799423e-01 -4.9929056e-02]
Sparsity at: 0.31764686158798283
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3238344e-01
 -1.9812205e-01 -4.9770564e-02]
Sparsity at: 0.31764686158798283
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3249350e-01
 -1.9816700e-01 -4.9689785e-02]
Sparsity at: 0.31764686158798283
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3230267e-01
 -1.9795969e-01 -4.9668603e-02]
Sparsity at: 0.31764686158798283
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259347e-01
 -1.9789292e-01 -4.9734324e-02]
Sparsity at: 0.31764686158798283
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3261445e-01
 -1.9810584e-01 -4.9681913e-02]
Sparsity at: 0.31764686158798283
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256408e-01
 -1.9816026e-01 -4.9610984e-02]
Sparsity at: 0.31764686158798283
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8982 - val_loss: 0.8425 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262101e-01
 -1.9791436e-01 -4.9821641e-02]
Sparsity at: 0.31764686158798283
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254182e-01
 -1.9801253e-01 -4.9684793e-02]
Sparsity at: 0.31764686158798283
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3279141e-01
 -1.9812328e-01 -4.9740724e-02]
Sparsity at: 0.31764686158798283
Epoch 284/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265823e-01
 -1.9802296e-01 -4.9998369e-02]
Sparsity at: 0.31764686158798283
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3283135e-01
 -1.9795346e-01 -5.0070040e-02]
Sparsity at: 0.31764686158798283
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3281339e-01
 -1.9809550e-01 -4.9847193e-02]
Sparsity at: 0.31764686158798283
Epoch 287/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269632e-01
 -1.9811673e-01 -4.9940422e-02]
Sparsity at: 0.31764686158798283
Epoch 288/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267826e-01
 -1.9803974e-01 -4.9987052e-02]
Sparsity at: 0.31764686158798283
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265364e-01
 -1.9791408e-01 -4.9788188e-02]
Sparsity at: 0.31764686158798283
Epoch 290/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3251366e-01
 -1.9786063e-01 -4.9811009e-02]
Sparsity at: 0.31764686158798283
Epoch 291/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262077e-01
 -1.9799949e-01 -4.9862377e-02]
Sparsity at: 0.31764686158798283
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259126e-01
 -1.9800738e-01 -4.9696483e-02]
Sparsity at: 0.31764686158798283
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252182e-01
 -1.9808300e-01 -4.9704488e-02]
Sparsity at: 0.31764686158798283
Epoch 294/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3264091e-01
 -1.9794048e-01 -4.9898226e-02]
Sparsity at: 0.31764686158798283
Epoch 295/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3273513e-01
 -1.9788145e-01 -4.9966913e-02]
Sparsity at: 0.31764686158798283
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252199e-01
 -1.9790380e-01 -4.9957853e-02]
Sparsity at: 0.31764686158798283
Epoch 297/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3242649e-01
 -1.9798808e-01 -4.9913958e-02]
Sparsity at: 0.31764686158798283
Epoch 298/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267371e-01
 -1.9784570e-01 -4.9937833e-02]
Sparsity at: 0.31764686158798283
Epoch 299/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8422 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3268983e-01
 -1.9785890e-01 -4.9913019e-02]
Sparsity at: 0.31764686158798283
Epoch 300/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3266930e-01
 -1.9813108e-01 -5.0035678e-02]
Sparsity at: 0.31764686158798283
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.04788359900957806
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.13539425739688404
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.7593994
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.2351152310006963
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 50s 7ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3278276e-01
 -1.9795179e-01 -4.9800307e-02]
Sparsity at: 0.31764686158798283
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250822e-01
 -1.9795927e-01 -4.9746864e-02]
Sparsity at: 0.31764686158798283
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250214e-01
 -1.9803071e-01 -4.9839821e-02]
Sparsity at: 0.31764686158798283
Epoch 304/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258010e-01
 -1.9806913e-01 -4.9885873e-02]
Sparsity at: 0.31764686158798283
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3266473e-01
 -1.9804579e-01 -4.9949896e-02]
Sparsity at: 0.31764686158798283
Epoch 306/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8429 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3275422e-01
 -1.9803868e-01 -5.0110999e-02]
Sparsity at: 0.31764686158798283
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3272098e-01
 -1.9797283e-01 -5.0021850e-02]
Sparsity at: 0.31764686158798283
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3275847e-01
 -1.9795921e-01 -4.9938034e-02]
Sparsity at: 0.31764686158798283
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269009e-01
 -1.9785550e-01 -4.9979270e-02]
Sparsity at: 0.31764686158798283
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3270719e-01
 -1.9799496e-01 -4.9963541e-02]
Sparsity at: 0.31764686158798283
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3274992e-01
 -1.9787772e-01 -5.0076935e-02]
Sparsity at: 0.31764686158798283
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3275257e-01
 -1.9794948e-01 -4.9907103e-02]
Sparsity at: 0.31764686158798283
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259248e-01
 -1.9797005e-01 -5.0076805e-02]
Sparsity at: 0.31764686158798283
Epoch 314/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3266964e-01
 -1.9787095e-01 -4.9846541e-02]
Sparsity at: 0.31764686158798283
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8421 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262784e-01
 -1.9790511e-01 -4.9979873e-02]
Sparsity at: 0.31764686158798283
Epoch 316/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256787e-01
 -1.9802929e-01 -4.9989499e-02]
Sparsity at: 0.31764686158798283
Epoch 317/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3248563e-01
 -1.9809888e-01 -4.9851183e-02]
Sparsity at: 0.31764686158798283
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257951e-01
 -1.9792271e-01 -5.0012995e-02]
Sparsity at: 0.31764686158798283
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8423 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3253632e-01
 -1.9804838e-01 -5.0026283e-02]
Sparsity at: 0.31764686158798283
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3241871e-01
 -1.9820940e-01 -4.9835708e-02]
Sparsity at: 0.31764686158798283
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3268398e-01
 -1.9788592e-01 -4.9785346e-02]
Sparsity at: 0.31764686158798283
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256554e-01
 -1.9773345e-01 -4.9814887e-02]
Sparsity at: 0.31764686158798283
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3260059e-01
 -1.9801638e-01 -4.9924187e-02]
Sparsity at: 0.31764686158798283
Epoch 324/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250667e-01
 -1.9792512e-01 -4.9900260e-02]
Sparsity at: 0.31764686158798283
Epoch 325/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8428 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247307e-01
 -1.9793144e-01 -4.9950205e-02]
Sparsity at: 0.31764686158798283
Epoch 326/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257244e-01
 -1.9798738e-01 -4.9880147e-02]
Sparsity at: 0.31764686158798283
Epoch 327/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3266009e-01
 -1.9806498e-01 -4.9752679e-02]
Sparsity at: 0.31764686158798283
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3246007e-01
 -1.9803892e-01 -4.9954880e-02]
Sparsity at: 0.31764686158798283
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262451e-01
 -1.9799948e-01 -4.9768053e-02]
Sparsity at: 0.31764686158798283
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3242239e-01
 -1.9808955e-01 -4.9986999e-02]
Sparsity at: 0.31764686158798283
Epoch 331/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3270764e-01
 -1.9804269e-01 -5.0109550e-02]
Sparsity at: 0.31764686158798283
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254161e-01
 -1.9808698e-01 -4.9990784e-02]
Sparsity at: 0.31764686158798283
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254024e-01
 -1.9804102e-01 -5.0128985e-02]
Sparsity at: 0.31764686158798283
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257775e-01
 -1.9807452e-01 -4.9898531e-02]
Sparsity at: 0.31764686158798283
Epoch 335/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3253900e-01
 -1.9809070e-01 -4.9916886e-02]
Sparsity at: 0.31764686158798283
Epoch 336/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256250e-01
 -1.9801477e-01 -5.0011158e-02]
Sparsity at: 0.31764686158798283
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8976 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3237356e-01
 -1.9791655e-01 -4.9787626e-02]
Sparsity at: 0.31764686158798283
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3270876e-01
 -1.9800150e-01 -4.9976308e-02]
Sparsity at: 0.31764686158798283
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263313e-01
 -1.9797869e-01 -4.9882732e-02]
Sparsity at: 0.31764686158798283
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8614 - accuracy: 0.8981 - val_loss: 0.8423 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3278721e-01
 -1.9805530e-01 -4.9979366e-02]
Sparsity at: 0.31764686158798283
Epoch 341/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263096e-01
 -1.9798875e-01 -4.9881052e-02]
Sparsity at: 0.31764686158798283
Epoch 342/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3270178e-01
 -1.9800828e-01 -4.9856909e-02]
Sparsity at: 0.31764686158798283
Epoch 343/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267998e-01
 -1.9799435e-01 -5.0002381e-02]
Sparsity at: 0.31764686158798283
Epoch 344/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3271005e-01
 -1.9807129e-01 -5.0144494e-02]
Sparsity at: 0.31764686158798283
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262835e-01
 -1.9798715e-01 -5.0095156e-02]
Sparsity at: 0.31764686158798283
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3274355e-01
 -1.9808359e-01 -5.0110567e-02]
Sparsity at: 0.31764686158798283
Epoch 347/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3274935e-01
 -1.9809730e-01 -5.0057184e-02]
Sparsity at: 0.31764686158798283
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8422 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250378e-01
 -1.9798309e-01 -4.9946167e-02]
Sparsity at: 0.31764686158798283
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3238361e-01
 -1.9800623e-01 -4.9975187e-02]
Sparsity at: 0.31764686158798283
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3264570e-01
 -1.9787477e-01 -5.0022580e-02]
Sparsity at: 0.31764686158798283
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.05873832177777283
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.15064661322034212
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.7593994
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.2532106425008891
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 51s 7ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265680e-01
 -1.9808239e-01 -4.9933340e-02]
Sparsity at: 0.31764686158798283
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265838e-01
 -1.9794565e-01 -5.0004542e-02]
Sparsity at: 0.31764686158798283
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3272992e-01
 -1.9801296e-01 -5.0048936e-02]
Sparsity at: 0.31764686158798283
Epoch 354/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256115e-01
 -1.9823101e-01 -4.9993545e-02]
Sparsity at: 0.31764686158798283
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3272852e-01
 -1.9798085e-01 -5.0036993e-02]
Sparsity at: 0.31764686158798283
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8976 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3260914e-01
 -1.9804540e-01 -4.9952053e-02]
Sparsity at: 0.31764686158798283
Epoch 357/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3268028e-01
 -1.9809811e-01 -4.9953248e-02]
Sparsity at: 0.31764686158798283
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267979e-01
 -1.9790269e-01 -5.0109535e-02]
Sparsity at: 0.31764686158798283
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269076e-01
 -1.9800615e-01 -4.9854103e-02]
Sparsity at: 0.31764686158798283
Epoch 360/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3268243e-01
 -1.9816689e-01 -5.0042354e-02]
Sparsity at: 0.31764686158798283
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3274419e-01
 -1.9789301e-01 -4.9959507e-02]
Sparsity at: 0.31764686158798283
Epoch 362/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3277349e-01
 -1.9807650e-01 -4.9992546e-02]
Sparsity at: 0.31764686158798283
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8423 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262415e-01
 -1.9814546e-01 -4.9951889e-02]
Sparsity at: 0.31764686158798283
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8422 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259934e-01
 -1.9793811e-01 -4.9808122e-02]
Sparsity at: 0.31764686158798283
Epoch 365/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3244286e-01
 -1.9788468e-01 -4.9903709e-02]
Sparsity at: 0.31764686158798283
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3261224e-01
 -1.9790162e-01 -4.9907438e-02]
Sparsity at: 0.31764686158798283
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247515e-01
 -1.9799717e-01 -4.9878392e-02]
Sparsity at: 0.31764686158798283
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3260734e-01
 -1.9792810e-01 -4.9924057e-02]
Sparsity at: 0.31764686158798283
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247943e-01
 -1.9792208e-01 -4.9863584e-02]
Sparsity at: 0.31764686158798283
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3237037e-01
 -1.9799241e-01 -4.9948093e-02]
Sparsity at: 0.31764686158798283
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267656e-01
 -1.9793609e-01 -4.9962562e-02]
Sparsity at: 0.31764686158798283
Epoch 372/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3288635e-01
 -1.9793965e-01 -4.9978610e-02]
Sparsity at: 0.31764686158798283
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255368e-01
 -1.9814402e-01 -5.0068762e-02]
Sparsity at: 0.31764686158798283
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3249813e-01
 -1.9801405e-01 -5.0055359e-02]
Sparsity at: 0.31764686158798283
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257667e-01
 -1.9787610e-01 -5.0115645e-02]
Sparsity at: 0.31764686158798283
Epoch 376/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3243504e-01
 -1.9782761e-01 -5.0059263e-02]
Sparsity at: 0.31764686158798283
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9029
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269022e-01
 -1.9822682e-01 -5.0120346e-02]
Sparsity at: 0.31764686158798283
Epoch 378/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252436e-01
 -1.9793418e-01 -5.0225124e-02]
Sparsity at: 0.31764686158798283
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3232271e-01
 -1.9817276e-01 -4.9929984e-02]
Sparsity at: 0.31764686158798283
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257635e-01
 -1.9795622e-01 -5.0071165e-02]
Sparsity at: 0.31764686158798283
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250557e-01
 -1.9795847e-01 -4.9893592e-02]
Sparsity at: 0.31764686158798283
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263968e-01
 -1.9806768e-01 -4.9911957e-02]
Sparsity at: 0.31764686158798283
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269655e-01
 -1.9787218e-01 -4.9997874e-02]
Sparsity at: 0.31764686158798283
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267446e-01
 -1.9811675e-01 -4.9882427e-02]
Sparsity at: 0.31764686158798283
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9013
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259782e-01
 -1.9797219e-01 -5.0059944e-02]
Sparsity at: 0.31764686158798283
Epoch 386/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3260244e-01
 -1.9785139e-01 -5.0202347e-02]
Sparsity at: 0.31764686158798283
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3281606e-01
 -1.9803700e-01 -5.0133862e-02]
Sparsity at: 0.31764686158798283
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8428 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3251641e-01
 -1.9800407e-01 -5.0273761e-02]
Sparsity at: 0.31764686158798283
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3280791e-01
 -1.9799767e-01 -5.0290324e-02]
Sparsity at: 0.31764686158798283
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3274169e-01
 -1.9797666e-01 -5.0147194e-02]
Sparsity at: 0.31764686158798283
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267086e-01
 -1.9788232e-01 -4.9989849e-02]
Sparsity at: 0.31764686158798283
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269809e-01
 -1.9788675e-01 -5.0011151e-02]
Sparsity at: 0.31764686158798283
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8975 - val_loss: 0.8427 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3286439e-01
 -1.9792023e-01 -5.0096732e-02]
Sparsity at: 0.31764686158798283
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3266510e-01
 -1.9793274e-01 -5.0019603e-02]
Sparsity at: 0.31764686158798283
Epoch 395/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262866e-01
 -1.9784863e-01 -5.0098855e-02]
Sparsity at: 0.31764686158798283
Epoch 396/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255030e-01
 -1.9783841e-01 -5.0131239e-02]
Sparsity at: 0.31764686158798283
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3251403e-01
 -1.9784488e-01 -5.0109874e-02]
Sparsity at: 0.31764686158798283
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8420 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258386e-01
 -1.9808303e-01 -5.0051775e-02]
Sparsity at: 0.31764686158798283
Epoch 399/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255984e-01
 -1.9824937e-01 -4.9821362e-02]
Sparsity at: 0.31764686158798283
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256598e-01
 -1.9794929e-01 -5.0095707e-02]
Sparsity at: 0.31764686158798283
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.06570947417196216
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.16158135941639884
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.7593994
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [0. 1. 1. ... 0. 0. 0.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.2637035259593681
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 49s 7ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263148e-01
 -1.9777408e-01 -5.0077610e-02]
Sparsity at: 0.31764686158798283
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263366e-01
 -1.9798130e-01 -5.0034296e-02]
Sparsity at: 0.31764686158798283
Epoch 403/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3239899e-01
 -1.9788615e-01 -5.0109621e-02]
Sparsity at: 0.31764686158798283
Epoch 404/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3274319e-01
 -1.9788106e-01 -5.0019369e-02]
Sparsity at: 0.31764686158798283
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3249852e-01
 -1.9787355e-01 -4.9989782e-02]
Sparsity at: 0.31764686158798283
Epoch 406/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8427 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250411e-01
 -1.9818258e-01 -4.9999382e-02]
Sparsity at: 0.31764686158798283
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9014
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250957e-01
 -1.9798924e-01 -5.0187260e-02]
Sparsity at: 0.31764686158798283
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3243374e-01
 -1.9789532e-01 -5.0093584e-02]
Sparsity at: 0.31764686158798283
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3249572e-01
 -1.9804832e-01 -5.0010800e-02]
Sparsity at: 0.31764686158798283
Epoch 410/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257360e-01
 -1.9800691e-01 -4.9868125e-02]
Sparsity at: 0.31764686158798283
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3264650e-01
 -1.9784173e-01 -4.9922738e-02]
Sparsity at: 0.31764686158798283
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3246875e-01
 -1.9800596e-01 -4.9887218e-02]
Sparsity at: 0.31764686158798283
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267536e-01
 -1.9808479e-01 -5.0008949e-02]
Sparsity at: 0.31764686158798283
Epoch 414/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262331e-01
 -1.9801077e-01 -5.0030064e-02]
Sparsity at: 0.31764686158798283
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265188e-01
 -1.9792736e-01 -5.0074395e-02]
Sparsity at: 0.31764686158798283
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3257772e-01
 -1.9794686e-01 -5.0035834e-02]
Sparsity at: 0.31764686158798283
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8421 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256471e-01
 -1.9807048e-01 -5.0043069e-02]
Sparsity at: 0.31764686158798283
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265765e-01
 -1.9790578e-01 -5.0051350e-02]
Sparsity at: 0.31764686158798283
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8422 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3269290e-01
 -1.9794534e-01 -5.0027918e-02]
Sparsity at: 0.31764686158798283
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259532e-01
 -1.9809301e-01 -5.0023921e-02]
Sparsity at: 0.31764686158798283
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3246635e-01
 -1.9785452e-01 -4.9828585e-02]
Sparsity at: 0.31764686158798283
Epoch 422/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8976 - val_loss: 0.8424 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3268240e-01
 -1.9794391e-01 -4.9942352e-02]
Sparsity at: 0.31764686158798283
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3260315e-01
 -1.9805324e-01 -5.0026156e-02]
Sparsity at: 0.31764686158798283
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258880e-01
 -1.9810832e-01 -4.9839612e-02]
Sparsity at: 0.31764686158798283
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256963e-01
 -1.9802730e-01 -4.9957942e-02]
Sparsity at: 0.31764686158798283
Epoch 426/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3240978e-01
 -1.9793838e-01 -4.9901646e-02]
Sparsity at: 0.31764686158798283
Epoch 427/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3248630e-01
 -1.9803531e-01 -4.9987637e-02]
Sparsity at: 0.31764686158798283
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3261342e-01
 -1.9800305e-01 -5.0041020e-02]
Sparsity at: 0.31764686158798283
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256860e-01
 -1.9800164e-01 -5.0026007e-02]
Sparsity at: 0.31764686158798283
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3278963e-01
 -1.9814353e-01 -5.0101567e-02]
Sparsity at: 0.31764686158798283
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259658e-01
 -1.9800606e-01 -5.0203025e-02]
Sparsity at: 0.31764686158798283
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8427 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3245519e-01
 -1.9795997e-01 -5.0144415e-02]
Sparsity at: 0.31764686158798283
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3266058e-01
 -1.9792086e-01 -5.0271228e-02]
Sparsity at: 0.31764686158798283
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3259588e-01
 -1.9829655e-01 -5.0304312e-02]
Sparsity at: 0.31764686158798283
Epoch 435/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3238792e-01
 -1.9814217e-01 -4.9957145e-02]
Sparsity at: 0.31764686158798283
Epoch 436/500
235/235 [==============================] - 2s 10ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252203e-01
 -1.9794235e-01 -4.9957469e-02]
Sparsity at: 0.31764686158798283
Epoch 437/500
235/235 [==============================] - 3s 11ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3249087e-01
 -1.9809633e-01 -4.9902093e-02]
Sparsity at: 0.31764686158798283
Epoch 438/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9027
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256255e-01
 -1.9802758e-01 -5.0011899e-02]
Sparsity at: 0.31764686158798283
Epoch 439/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3235496e-01
 -1.9812007e-01 -5.0001614e-02]
Sparsity at: 0.31764686158798283
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3242367e-01
 -1.9802591e-01 -5.0083179e-02]
Sparsity at: 0.31764686158798283
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3243179e-01
 -1.9807337e-01 -5.0052747e-02]
Sparsity at: 0.31764686158798283
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267496e-01
 -1.9803952e-01 -5.0078813e-02]
Sparsity at: 0.31764686158798283
Epoch 443/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263175e-01
 -1.9824378e-01 -4.9981348e-02]
Sparsity at: 0.31764686158798283
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255067e-01
 -1.9793999e-01 -5.0043646e-02]
Sparsity at: 0.31764686158798283
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8421 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3271338e-01
 -1.9795136e-01 -4.9843110e-02]
Sparsity at: 0.31764686158798283
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8614 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3258998e-01
 -1.9804506e-01 -5.0114356e-02]
Sparsity at: 0.31764686158798283
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3271517e-01
 -1.9807704e-01 -4.9867090e-02]
Sparsity at: 0.31764686158798283
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3273109e-01
 -1.9803922e-01 -4.9987983e-02]
Sparsity at: 0.31764686158798283
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3279690e-01
 -1.9805783e-01 -4.9898762e-02]
Sparsity at: 0.31764686158798283
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262923e-01
 -1.9803488e-01 -4.9989060e-02]
Sparsity at: 0.31764686158798283
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254808e-01
 -1.9794846e-01 -5.0017815e-02]
Sparsity at: 0.31764686158798283
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3271305e-01
 -1.9798163e-01 -5.0043624e-02]
Sparsity at: 0.31764686158798283
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3278776e-01
 -1.9794081e-01 -5.0188821e-02]
Sparsity at: 0.31764686158798283
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256088e-01
 -1.9793466e-01 -5.0146092e-02]
Sparsity at: 0.31764686158798283
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8975 - val_loss: 0.8426 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3248058e-01
 -1.9798517e-01 -5.0227847e-02]
Sparsity at: 0.31764686158798283
Epoch 456/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255036e-01
 -1.9812705e-01 -5.0001271e-02]
Sparsity at: 0.31764686158798283
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3261460e-01
 -1.9794351e-01 -4.9969021e-02]
Sparsity at: 0.31764686158798283
Epoch 458/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3273987e-01
 -1.9806549e-01 -4.9887273e-02]
Sparsity at: 0.31764686158798283
Epoch 459/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267429e-01
 -1.9801582e-01 -5.0127238e-02]
Sparsity at: 0.31764686158798283
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267228e-01
 -1.9791621e-01 -5.0011240e-02]
Sparsity at: 0.31764686158798283
Epoch 461/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8422 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267070e-01
 -1.9809151e-01 -4.9882427e-02]
Sparsity at: 0.31764686158798283
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252528e-01
 -1.9797547e-01 -4.9919579e-02]
Sparsity at: 0.31764686158798283
Epoch 463/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3240734e-01
 -1.9811274e-01 -5.0145332e-02]
Sparsity at: 0.31764686158798283
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3236594e-01
 -1.9795379e-01 -4.9955167e-02]
Sparsity at: 0.31764686158798283
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3246596e-01
 -1.9804743e-01 -4.9982406e-02]
Sparsity at: 0.31764686158798283
Epoch 466/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9012
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3272028e-01
 -1.9798447e-01 -4.9977090e-02]
Sparsity at: 0.31764686158798283
Epoch 467/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252762e-01
 -1.9797373e-01 -5.0039735e-02]
Sparsity at: 0.31764686158798283
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252729e-01
 -1.9786377e-01 -4.9897715e-02]
Sparsity at: 0.31764686158798283
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3262740e-01
 -1.9799607e-01 -5.0096452e-02]
Sparsity at: 0.31764686158798283
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9026
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263333e-01
 -1.9809455e-01 -5.0054040e-02]
Sparsity at: 0.31764686158798283
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8421 - val_accuracy: 0.9023
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263416e-01
 -1.9815184e-01 -4.9906433e-02]
Sparsity at: 0.31764686158798283
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3272633e-01
 -1.9805856e-01 -5.0000936e-02]
Sparsity at: 0.31764686158798283
Epoch 473/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8422 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3273297e-01
 -1.9802850e-01 -5.0038446e-02]
Sparsity at: 0.31764686158798283
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8983 - val_loss: 0.8425 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255207e-01
 -1.9813329e-01 -4.9889475e-02]
Sparsity at: 0.31764686158798283
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8422 - val_accuracy: 0.9022
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255222e-01
 -1.9798040e-01 -4.9811702e-02]
Sparsity at: 0.31764686158798283
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3256466e-01
 -1.9802372e-01 -4.9898021e-02]
Sparsity at: 0.31764686158798283
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3247627e-01
 -1.9791786e-01 -4.9946710e-02]
Sparsity at: 0.31764686158798283
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3254672e-01
 -1.9797236e-01 -4.9786828e-02]
Sparsity at: 0.31764686158798283
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255042e-01
 -1.9817907e-01 -4.9881876e-02]
Sparsity at: 0.31764686158798283
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8975 - val_loss: 0.8426 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3250558e-01
 -1.9806054e-01 -4.9778279e-02]
Sparsity at: 0.31764686158798283
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9017
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3255520e-01
 -1.9807257e-01 -4.9937755e-02]
Sparsity at: 0.31764686158798283
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3241418e-01
 -1.9794680e-01 -4.9906436e-02]
Sparsity at: 0.31764686158798283
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3235725e-01
 -1.9814113e-01 -4.9884971e-02]
Sparsity at: 0.31764686158798283
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8984 - val_loss: 0.8426 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3229862e-01
 -1.9800222e-01 -4.9869563e-02]
Sparsity at: 0.31764686158798283
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3237546e-01
 -1.9806202e-01 -4.9970575e-02]
Sparsity at: 0.31764686158798283
Epoch 486/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3263072e-01
 -1.9810417e-01 -4.9941722e-02]
Sparsity at: 0.31764686158798283
Epoch 487/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8428 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3271482e-01
 -1.9816847e-01 -4.9906813e-02]
Sparsity at: 0.31764686158798283
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3243754e-01
 -1.9806570e-01 -4.9752977e-02]
Sparsity at: 0.31764686158798283
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9022
[-5.483876e-34  0.000000e+00  5.091550e-34 ...  1.326886e-01 -1.981011e-01
 -4.989126e-02]
Sparsity at: 0.31764686158798283
Epoch 490/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8427 - val_accuracy: 0.9020
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3267493e-01
 -1.9803464e-01 -4.9972672e-02]
Sparsity at: 0.31764686158798283
Epoch 491/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9014
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3261253e-01
 -1.9809864e-01 -4.9887691e-02]
Sparsity at: 0.31764686158798283
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3278884e-01
 -1.9795603e-01 -4.9933054e-02]
Sparsity at: 0.31764686158798283
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9019
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3239925e-01
 -1.9804376e-01 -4.9918123e-02]
Sparsity at: 0.31764686158798283
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3253441e-01
 -1.9795430e-01 -4.9909659e-02]
Sparsity at: 0.31764686158798283
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3252130e-01
 -1.9831890e-01 -4.9981963e-02]
Sparsity at: 0.31764686158798283
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9016
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3251908e-01
 -1.9803734e-01 -4.9723741e-02]
Sparsity at: 0.31764686158798283
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9028
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3239101e-01
 -1.9809103e-01 -4.9818065e-02]
Sparsity at: 0.31764686158798283
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9015
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3238424e-01
 -1.9803026e-01 -4.9927667e-02]
Sparsity at: 0.31764686158798283
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9025
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3264406e-01
 -1.9787149e-01 -4.9839906e-02]
Sparsity at: 0.31764686158798283
Epoch 500/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9024
[-5.4838758e-34  0.0000000e+00  5.0915500e-34 ...  1.3265264e-01
 -1.9797458e-01 -4.9811125e-02]
Sparsity at: 0.31764686158798283
Epoch 1/500
Wanted sparsity 0.5
Upper percentile 0.0419277586042881
Thresholhold 0.02126331627368927
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.08924054354429245
Thresholhold 0.07131436467170715
Using suggest threshold.
Applying new mask
Percentage zeros 0.40124512
tf.Tensor(
[[1. 1. 0. ... 0. 1. 0.]
 [1. 1. 1. ... 0. 1. 1.]
 [0. 1. 1. ... 1. 0. 0.]
 ...
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 1. 0. 1.]
 [1. 1. 1. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.5
Upper percentile 0.10948323458433151
Thresholhold -0.12435988336801529
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
  1/235 [..............................] - ETA: 59:11 - loss: 2.3452 - accuracy: 0.1172WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0059s vs `on_train_batch_begin` time: 2.4707s). Check your callbacks.
235/235 [==============================] - 17s 8ms/step - loss: 0.5215 - accuracy: 0.8597 - val_loss: 0.2712 - val_accuracy: 0.9193
[ 0.02126332  0.          0.04348067 ...  0.16702083 -0.26408657
  0.1956686 ]
Sparsity at: 0.2684582886266094
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2477 - accuracy: 0.9294 - val_loss: 0.2109 - val_accuracy: 0.9377
[ 0.02126332  0.          0.04348067 ...  0.18104391 -0.3083002
  0.21626326]
Sparsity at: 0.2684582886266094
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1942 - accuracy: 0.9441 - val_loss: 0.1738 - val_accuracy: 0.9487
[ 0.02126332  0.          0.04348067 ...  0.19138181 -0.34489295
  0.23536518]
Sparsity at: 0.2684582886266094
Epoch 4/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1589 - accuracy: 0.9538 - val_loss: 0.1500 - val_accuracy: 0.9551
[ 0.02126332  0.          0.04348067 ...  0.19762674 -0.372677
  0.25169244]
Sparsity at: 0.2684582886266094
Epoch 5/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1333 - accuracy: 0.9608 - val_loss: 0.1332 - val_accuracy: 0.9597
[ 0.02126332  0.          0.04348067 ...  0.20223808 -0.3937274
  0.26742727]
Sparsity at: 0.2684582886266094
Epoch 6/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1138 - accuracy: 0.9669 - val_loss: 0.1213 - val_accuracy: 0.9629
[ 0.02126332  0.          0.04348067 ...  0.20531437 -0.4105581
  0.28378928]
Sparsity at: 0.2684582886266094
Epoch 7/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0984 - accuracy: 0.9714 - val_loss: 0.1128 - val_accuracy: 0.9644
[ 0.02126332  0.          0.04348067 ...  0.20786038 -0.42422217
  0.29993114]
Sparsity at: 0.2684582886266094
Epoch 8/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0859 - accuracy: 0.9753 - val_loss: 0.1069 - val_accuracy: 0.9662
[ 0.02126332  0.          0.04348067 ...  0.21039784 -0.43551382
  0.31516293]
Sparsity at: 0.2684582886266094
Epoch 9/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0760 - accuracy: 0.9784 - val_loss: 0.1028 - val_accuracy: 0.9678
[ 0.02126332  0.          0.04348067 ...  0.21285456 -0.4457577
  0.3291802 ]
Sparsity at: 0.2684582886266094
Epoch 10/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0674 - accuracy: 0.9812 - val_loss: 0.0997 - val_accuracy: 0.9684
[ 0.02126332  0.          0.04348067 ...  0.21493122 -0.45520988
  0.34343296]
Sparsity at: 0.2684582886266094
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0600 - accuracy: 0.9833 - val_loss: 0.0980 - val_accuracy: 0.9696
[ 0.02126332  0.          0.04348067 ...  0.2168569  -0.4639403
  0.3569665 ]
Sparsity at: 0.2684582886266094
Epoch 12/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0537 - accuracy: 0.9851 - val_loss: 0.0963 - val_accuracy: 0.9700
[ 0.02126332  0.          0.04348067 ...  0.2192016  -0.47206596
  0.37023714]
Sparsity at: 0.2684582886266094
Epoch 13/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0479 - accuracy: 0.9873 - val_loss: 0.0957 - val_accuracy: 0.9706
[ 0.02126332  0.          0.04348067 ...  0.22107998 -0.47938725
  0.38289675]
Sparsity at: 0.2684582886266094
Epoch 14/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0429 - accuracy: 0.9890 - val_loss: 0.0957 - val_accuracy: 0.9707
[ 0.02126332  0.          0.04348067 ...  0.22361805 -0.48631063
  0.3949754 ]
Sparsity at: 0.2684582886266094
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0383 - accuracy: 0.9906 - val_loss: 0.0952 - val_accuracy: 0.9713
[ 0.02126332  0.          0.04348067 ...  0.22632354 -0.49305093
  0.4065653 ]
Sparsity at: 0.2684582886266094
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0341 - accuracy: 0.9917 - val_loss: 0.0956 - val_accuracy: 0.9712
[ 0.02126332  0.          0.04348067 ...  0.2295316  -0.49925712
  0.41724834]
Sparsity at: 0.2684582886266094
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0303 - accuracy: 0.9929 - val_loss: 0.0958 - val_accuracy: 0.9715
[ 0.02126332  0.          0.04348067 ...  0.23396556 -0.5060252
  0.42774615]
Sparsity at: 0.2684582886266094
Epoch 18/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0268 - accuracy: 0.9940 - val_loss: 0.0968 - val_accuracy: 0.9718
[ 0.02126332  0.          0.04348067 ...  0.23803149 -0.51327354
  0.43757227]
Sparsity at: 0.2684582886266094
Epoch 19/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0237 - accuracy: 0.9949 - val_loss: 0.0970 - val_accuracy: 0.9723
[ 0.02126332  0.          0.04348067 ...  0.24232528 -0.5205872
  0.44733778]
Sparsity at: 0.2684582886266094
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0208 - accuracy: 0.9958 - val_loss: 0.0980 - val_accuracy: 0.9724
[ 0.02126332  0.          0.04348067 ...  0.24621749 -0.52817464
  0.45674244]
Sparsity at: 0.2684582886266094
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0182 - accuracy: 0.9967 - val_loss: 0.1000 - val_accuracy: 0.9723
[ 0.02126332  0.          0.04348067 ...  0.25079218 -0.53614646
  0.465871  ]
Sparsity at: 0.2684582886266094
Epoch 22/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0159 - accuracy: 0.9974 - val_loss: 0.1006 - val_accuracy: 0.9738
[ 0.02126332  0.          0.04348067 ...  0.2557964  -0.54538226
  0.47451037]
Sparsity at: 0.2684582886266094
Epoch 23/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0139 - accuracy: 0.9979 - val_loss: 0.1023 - val_accuracy: 0.9733
[ 0.02126332  0.          0.04348067 ...  0.25865233 -0.55384856
  0.48364258]
Sparsity at: 0.2684582886266094
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0122 - accuracy: 0.9984 - val_loss: 0.1034 - val_accuracy: 0.9729
[ 0.02126332  0.          0.04348067 ...  0.26205382 -0.5627565
  0.49245447]
Sparsity at: 0.2684582886266094
Epoch 25/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0107 - accuracy: 0.9988 - val_loss: 0.1040 - val_accuracy: 0.9740
[ 0.02126332  0.          0.04348067 ...  0.26463956 -0.5717029
  0.500163  ]
Sparsity at: 0.2684582886266094
Epoch 26/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0093 - accuracy: 0.9992 - val_loss: 0.1056 - val_accuracy: 0.9739
[ 0.02126332  0.          0.04348067 ...  0.26524624 -0.57905805
  0.5071833 ]
Sparsity at: 0.2684582886266094
Epoch 27/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0081 - accuracy: 0.9993 - val_loss: 0.1063 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.2677221  -0.5871909
  0.5135752 ]
Sparsity at: 0.2684582886266094
Epoch 28/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0072 - accuracy: 0.9994 - val_loss: 0.1099 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.2692408  -0.5945608
  0.5200721 ]
Sparsity at: 0.2684582886266094
Epoch 29/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0065 - accuracy: 0.9994 - val_loss: 0.1164 - val_accuracy: 0.9731
[ 0.02126332  0.          0.04348067 ...  0.27089462 -0.60207236
  0.5238904 ]
Sparsity at: 0.2684582886266094
Epoch 30/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0060 - accuracy: 0.9995 - val_loss: 0.1170 - val_accuracy: 0.9734
[ 0.02126332  0.          0.04348067 ...  0.27399483 -0.60899323
  0.5284181 ]
Sparsity at: 0.2684582886266094
Epoch 31/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0053 - accuracy: 0.9995 - val_loss: 0.1208 - val_accuracy: 0.9728
[ 0.02126332  0.          0.04348067 ...  0.27982938 -0.617689
  0.5311662 ]
Sparsity at: 0.2684582886266094
Epoch 32/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0048 - accuracy: 0.9997 - val_loss: 0.1174 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.281081   -0.62403387
  0.5339233 ]
Sparsity at: 0.2684582886266094
Epoch 33/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0050 - accuracy: 0.9993 - val_loss: 0.1284 - val_accuracy: 0.9715
[ 0.02126332  0.          0.04348067 ...  0.28026482 -0.6339376
  0.5380926 ]
Sparsity at: 0.2684582886266094
Epoch 34/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9991 - val_loss: 0.1309 - val_accuracy: 0.9723
[ 0.02126332  0.          0.04348067 ...  0.27961272 -0.6434787
  0.5465148 ]
Sparsity at: 0.2684582886266094
Epoch 35/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 0.1433 - val_accuracy: 0.9706
[ 0.02126332  0.          0.04348067 ...  0.27895343 -0.63876915
  0.55570805]
Sparsity at: 0.2684582886266094
Epoch 36/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.1257 - val_accuracy: 0.9739
[ 0.02126332  0.          0.04348067 ...  0.28327608 -0.63464594
  0.5659999 ]
Sparsity at: 0.2684582886266094
Epoch 37/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9986 - val_loss: 0.1289 - val_accuracy: 0.9738
[ 0.02126332  0.          0.04348067 ...  0.29228336 -0.6496202
  0.5689408 ]
Sparsity at: 0.2684582886266094
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9991 - val_loss: 0.1467 - val_accuracy: 0.9696
[ 0.02126332  0.          0.04348067 ...  0.287273   -0.6581386
  0.56477755]
Sparsity at: 0.2684582886266094
Epoch 39/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9995 - val_loss: 0.1396 - val_accuracy: 0.9732
[ 0.02126332  0.          0.04348067 ...  0.28866014 -0.66592216
  0.5668326 ]
Sparsity at: 0.2684582886266094
Epoch 40/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1400 - val_accuracy: 0.9726
[ 0.02126332  0.          0.04348067 ...  0.28370982 -0.6668031
  0.5654722 ]
Sparsity at: 0.2684582886266094
Epoch 41/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 0.9996 - val_loss: 0.1353 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.28491423 -0.67179185
  0.5675952 ]
Sparsity at: 0.2684582886266094
Epoch 42/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 0.9994 - val_loss: 0.1443 - val_accuracy: 0.9739
[ 0.02126332  0.          0.04348067 ...  0.28270745 -0.6839155
  0.5858273 ]
Sparsity at: 0.2684582886266094
Epoch 43/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.1443 - val_accuracy: 0.9729
[ 0.02126332  0.          0.04348067 ...  0.2908145  -0.6832616
  0.57646084]
Sparsity at: 0.2684582886266094
Epoch 44/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.1334 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.28910634 -0.67992157
  0.57445294]
Sparsity at: 0.2684582886266094
Epoch 45/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9995 - val_loss: 0.1392 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.28810105 -0.69014704
  0.5703469 ]
Sparsity at: 0.2684582886266094
Epoch 46/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1424 - val_accuracy: 0.9740
[ 0.02126332  0.          0.04348067 ...  0.2841043  -0.69731104
  0.5754799 ]
Sparsity at: 0.2684582886266094
Epoch 47/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1333 - val_accuracy: 0.9760
[ 0.02126332  0.          0.04348067 ...  0.282685   -0.69918203
  0.57337946]
Sparsity at: 0.2684582886266094
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 8.3822e-04 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.28225207 -0.7063152
  0.57344896]
Sparsity at: 0.2684582886266094
Epoch 49/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3327e-04 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9762
[ 0.02126332  0.          0.04348067 ...  0.2876788  -0.7117837
  0.58140206]
Sparsity at: 0.2684582886266094
Epoch 50/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5199e-04 - accuracy: 0.9999 - val_loss: 0.1372 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.28921196 -0.720552
  0.5840367 ]
Sparsity at: 0.2684582886266094
Epoch 51/500
Wanted sparsity 0.6458585
Upper percentile 0.10526492157142009
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.1655148892114795
Thresholhold 0.28851205110549927
Threshold over percentile. Lowering.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 0. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.6458585
Upper percentile 0.4916216782141518
Thresholhold -0.06144412234425545
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 50s 8ms/step - loss: 0.0023 - accuracy: 0.9996 - val_loss: 0.1612 - val_accuracy: 0.9711
[ 0.02126332  0.          0.04348067 ...  0.29993507 -0.73898196
  0.56850666]
Sparsity at: 0.3020553916309013
Epoch 52/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0133 - accuracy: 0.9957 - val_loss: 0.1444 - val_accuracy: 0.9741
[ 0.02126332  0.          0.04348067 ...  0.30290055 -0.73904043
  0.56436616]
Sparsity at: 0.3020553916309013
Epoch 53/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.1515 - val_accuracy: 0.9726
[ 0.02126332  0.          0.04348067 ...  0.30786392 -0.7549374
  0.56952095]
Sparsity at: 0.3020553916309013
Epoch 54/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 0.9998 - val_loss: 0.1384 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.30705678 -0.7540603
  0.5600867 ]
Sparsity at: 0.3020553916309013
Epoch 55/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7309e-04 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9760
[ 0.02126332  0.          0.04348067 ...  0.31005606 -0.7543497
  0.55939704]
Sparsity at: 0.3020553916309013
Epoch 56/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1628e-04 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9763
[ 0.02126332  0.          0.04348067 ...  0.31195587 -0.75820017
  0.5619109 ]
Sparsity at: 0.3020553916309013
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0808e-04 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9762
[ 0.02126332  0.          0.04348067 ...  0.31275234 -0.7608173
  0.5640171 ]
Sparsity at: 0.3020553916309013
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5035e-04 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.31359777 -0.7634921
  0.5655775 ]
Sparsity at: 0.3020553916309013
Epoch 59/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1010e-04 - accuracy: 1.0000 - val_loss: 0.1398 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.31444967 -0.76614916
  0.5670739 ]
Sparsity at: 0.3020553916309013
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7964e-04 - accuracy: 1.0000 - val_loss: 0.1405 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.315171   -0.76880765
  0.56849414]
Sparsity at: 0.3020553916309013
Epoch 61/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5418e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.31588772 -0.77158695
  0.5699023 ]
Sparsity at: 0.3020553916309013
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3220e-04 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9763
[ 0.02126332  0.          0.04348067 ...  0.31662792 -0.77442056
  0.5713052 ]
Sparsity at: 0.3020553916309013
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1239e-04 - accuracy: 1.0000 - val_loss: 0.1432 - val_accuracy: 0.9763
[ 0.02126332  0.          0.04348067 ...  0.31750235 -0.77733916
  0.5727592 ]
Sparsity at: 0.3020553916309013
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9539e-04 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9763
[ 0.02126332  0.          0.04348067 ...  0.31825516 -0.7803762
  0.57421046]
Sparsity at: 0.3020553916309013
Epoch 65/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7950e-04 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9763
[ 0.02126332  0.          0.04348067 ...  0.31920326 -0.783513
  0.57574946]
Sparsity at: 0.3020553916309013
Epoch 66/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6509e-04 - accuracy: 1.0000 - val_loss: 0.1463 - val_accuracy: 0.9763
[ 0.02126332  0.          0.04348067 ...  0.32006913 -0.7867509
  0.57729465]
Sparsity at: 0.3020553916309013
Epoch 67/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5180e-04 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9764
[ 0.02126332  0.          0.04348067 ...  0.32105178 -0.79012835
  0.57890093]
Sparsity at: 0.3020553916309013
Epoch 68/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3962e-04 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9764
[ 0.02126332  0.          0.04348067 ...  0.32199794 -0.7936606
  0.58049375]
Sparsity at: 0.3020553916309013
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2841e-04 - accuracy: 1.0000 - val_loss: 0.1498 - val_accuracy: 0.9767
[ 0.02126332  0.          0.04348067 ...  0.32292423 -0.7972948
  0.58221066]
Sparsity at: 0.3020553916309013
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1758e-04 - accuracy: 1.0000 - val_loss: 0.1511 - val_accuracy: 0.9769
[ 0.02126332  0.          0.04348067 ...  0.3238646  -0.8010634
  0.5840214 ]
Sparsity at: 0.3020553916309013
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0773e-04 - accuracy: 1.0000 - val_loss: 0.1524 - val_accuracy: 0.9768
[ 0.02126332  0.          0.04348067 ...  0.3248686  -0.80492693
  0.58584595]
Sparsity at: 0.3020553916309013
Epoch 72/500
235/235 [==============================] - 2s 8ms/step - loss: 9.8423e-05 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9769
[ 0.02126332  0.          0.04348067 ...  0.32573307 -0.80894053
  0.58765984]
Sparsity at: 0.3020553916309013
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 8.9830e-05 - accuracy: 1.0000 - val_loss: 0.1553 - val_accuracy: 0.9766
[ 0.02126332  0.          0.04348067 ...  0.326851   -0.81310886
  0.5895688 ]
Sparsity at: 0.3020553916309013
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2079e-05 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9767
[ 0.02126332  0.          0.04348067 ...  0.3277809  -0.8173641
  0.5915443 ]
Sparsity at: 0.3020553916309013
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 7.4837e-05 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9766
[ 0.02126332  0.          0.04348067 ...  0.32886505 -0.8217835
  0.59352183]
Sparsity at: 0.3020553916309013
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7851e-05 - accuracy: 1.0000 - val_loss: 0.1596 - val_accuracy: 0.9767
[ 0.02126332  0.          0.04348067 ...  0.33001345 -0.82630676
  0.5955734 ]
Sparsity at: 0.3020553916309013
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1525e-05 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9767
[ 0.02126332  0.          0.04348067 ...  0.33107185 -0.83086187
  0.5976295 ]
Sparsity at: 0.3020553916309013
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5719e-05 - accuracy: 1.0000 - val_loss: 0.1627 - val_accuracy: 0.9766
[ 0.02126332  0.          0.04348067 ...  0.33204454 -0.83557504
  0.59977317]
Sparsity at: 0.3020553916309013
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0418e-05 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9765
[ 0.02126332  0.          0.04348067 ...  0.33310527 -0.8404195
  0.60200197]
Sparsity at: 0.3020553916309013
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5482e-05 - accuracy: 1.0000 - val_loss: 0.1659 - val_accuracy: 0.9765
[ 0.02126332  0.          0.04348067 ...  0.3342645  -0.8453272
  0.60416013]
Sparsity at: 0.3020553916309013
Epoch 81/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1107e-05 - accuracy: 1.0000 - val_loss: 0.1674 - val_accuracy: 0.9764
[ 0.02126332  0.          0.04348067 ...  0.3353951  -0.8502975
  0.6063052 ]
Sparsity at: 0.3020553916309013
Epoch 82/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7013e-05 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9762
[ 0.02126332  0.          0.04348067 ...  0.33659205 -0.85541177
  0.6086061 ]
Sparsity at: 0.3020553916309013
Epoch 83/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3242e-05 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.337826   -0.86060476
  0.6109776 ]
Sparsity at: 0.3020553916309013
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9851e-05 - accuracy: 1.0000 - val_loss: 0.1726 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.3390802  -0.8658501
  0.61338425]
Sparsity at: 0.3020553916309013
Epoch 85/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6767e-05 - accuracy: 1.0000 - val_loss: 0.1741 - val_accuracy: 0.9762
[ 0.02126332  0.          0.04348067 ...  0.34021023 -0.8711478
  0.61562383]
Sparsity at: 0.3020553916309013
Epoch 86/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3972e-05 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.3413985  -0.876512
  0.6178698 ]
Sparsity at: 0.3020553916309013
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1448e-05 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9762
[ 0.02126332  0.          0.04348067 ...  0.3425436  -0.8819239
  0.62035364]
Sparsity at: 0.3020553916309013
Epoch 88/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9155e-05 - accuracy: 1.0000 - val_loss: 0.1795 - val_accuracy: 0.9760
[ 0.02126332  0.          0.04348067 ...  0.34374452 -0.88730526
  0.6225602 ]
Sparsity at: 0.3020553916309013
Epoch 89/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7146e-05 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9758
[ 0.02126332  0.          0.04348067 ...  0.34495136 -0.8927965
  0.6249054 ]
Sparsity at: 0.3020553916309013
Epoch 90/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5317e-05 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9758
[ 0.02126332  0.          0.04348067 ...  0.3461938  -0.89823115
  0.6273789 ]
Sparsity at: 0.3020553916309013
Epoch 91/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3632e-05 - accuracy: 1.0000 - val_loss: 0.1848 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.34751108 -0.9036638
  0.62979305]
Sparsity at: 0.3020553916309013
Epoch 92/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2109e-05 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.34891424 -0.9093175
  0.63210946]
Sparsity at: 0.3020553916309013
Epoch 93/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0789e-05 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.9758
[ 0.02126332  0.          0.04348067 ...  0.35007548 -0.91476864
  0.63449436]
Sparsity at: 0.3020553916309013
Epoch 94/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5815e-06 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9759
[ 0.02126332  0.          0.04348067 ...  0.3511449  -0.92030597
  0.63701326]
Sparsity at: 0.3020553916309013
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 8.5074e-06 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9760
[ 0.02126332  0.          0.04348067 ...  0.35229912 -0.9258466
  0.6392699 ]
Sparsity at: 0.3020553916309013
Epoch 96/500
235/235 [==============================] - 2s 8ms/step - loss: 7.5968e-06 - accuracy: 1.0000 - val_loss: 0.1940 - val_accuracy: 0.9759
[ 0.02126332  0.          0.04348067 ...  0.35351416 -0.93129647
  0.64141214]
Sparsity at: 0.3020553916309013
Epoch 97/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7495e-06 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.3545256  -0.93674505
  0.6439172 ]
Sparsity at: 0.3020553916309013
Epoch 98/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9862e-06 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9758
[ 0.02126332  0.          0.04348067 ...  0.3559423  -0.94216096
  0.64634335]
Sparsity at: 0.3020553916309013
Epoch 99/500
235/235 [==============================] - 2s 9ms/step - loss: 5.3127e-06 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9758
[ 0.02126332  0.          0.04348067 ...  0.35695285 -0.94764656
  0.64855397]
Sparsity at: 0.3020553916309013
Epoch 100/500
235/235 [==============================] - 2s 9ms/step - loss: 4.7206e-06 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9758
[ 0.02126332  0.          0.04348067 ...  0.35831973 -0.95307165
  0.6506135 ]
Sparsity at: 0.3020553916309013
Epoch 101/500
Wanted sparsity 0.7594532
Upper percentile 0.17641599869092062
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.28262760829185396
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 0. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.7594532
Upper percentile 0.7852845313879158
Thresholhold -0.05276104062795639
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 42s 7ms/step - loss: 4.1893e-06 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.35902867 -0.9584466
  0.65309083]
Sparsity at: 0.3020553916309013
Epoch 102/500
235/235 [==============================] - 2s 7ms/step - loss: 3.7123e-06 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.36011884 -0.96368843
  0.655142  ]
Sparsity at: 0.3020553916309013
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3057e-06 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9758
[ 0.02126332  0.          0.04348067 ...  0.36122075 -0.96900207
  0.657227  ]
Sparsity at: 0.3020553916309013
Epoch 104/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9384e-06 - accuracy: 1.0000 - val_loss: 0.2091 - val_accuracy: 0.9762
[ 0.02126332  0.          0.04348067 ...  0.3622366  -0.97432584
  0.6595657 ]
Sparsity at: 0.3020553916309013
Epoch 105/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6099e-06 - accuracy: 1.0000 - val_loss: 0.2111 - val_accuracy: 0.9760
[ 0.02126332  0.          0.04348067 ...  0.3635117  -0.9797349
  0.6614887 ]
Sparsity at: 0.3020553916309013
Epoch 106/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3120e-06 - accuracy: 1.0000 - val_loss: 0.2127 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.3645397  -0.9849432
  0.663842  ]
Sparsity at: 0.3020553916309013
Epoch 107/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0517e-06 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.3656114  -0.9901247
  0.6659095 ]
Sparsity at: 0.3020553916309013
Epoch 108/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8221e-06 - accuracy: 1.0000 - val_loss: 0.2165 - val_accuracy: 0.9761
[ 0.02126332  0.          0.04348067 ...  0.36670732 -0.995268
  0.6679974 ]
Sparsity at: 0.3020553916309013
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6132e-06 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9759
[ 0.02126332  0.          0.04348067 ...  0.3677123  -1.0003829
  0.6701143 ]
Sparsity at: 0.3020553916309013
Epoch 110/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4354e-06 - accuracy: 1.0000 - val_loss: 0.2200 - val_accuracy: 0.9759
[ 0.02126332  0.          0.04348067 ...  0.36882368 -1.0053213
  0.67198604]
Sparsity at: 0.3020553916309013
Epoch 111/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2790e-06 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.9759
[ 0.02126332  0.          0.04348067 ...  0.36966538 -1.0103289
  0.6740459 ]
Sparsity at: 0.3020553916309013
Epoch 112/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1421e-06 - accuracy: 1.0000 - val_loss: 0.2236 - val_accuracy: 0.9759
[ 0.02126332  0.          0.04348067 ...  0.37047738 -1.0152307
  0.67621154]
Sparsity at: 0.3020553916309013
Epoch 113/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0163e-06 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9758
[ 0.02126332  0.          0.04348067 ...  0.3714164  -1.0201155
  0.6778345 ]
Sparsity at: 0.3020553916309013
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0812e-07 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.37247437 -1.0249437
  0.6798683 ]
Sparsity at: 0.3020553916309013
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 8.0989e-07 - accuracy: 1.0000 - val_loss: 0.2291 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.37346327 -1.029692
  0.68162274]
Sparsity at: 0.3020553916309013
Epoch 116/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2154e-07 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.37434512 -1.0344833
  0.68347067]
Sparsity at: 0.3020553916309013
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 6.4616e-07 - accuracy: 1.0000 - val_loss: 0.2326 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.37512416 -1.0390948
  0.68535656]
Sparsity at: 0.3020553916309013
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7762e-07 - accuracy: 1.0000 - val_loss: 0.2341 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.37620085 -1.0436782
  0.6869813 ]
Sparsity at: 0.3020553916309013
Epoch 119/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1740e-07 - accuracy: 1.0000 - val_loss: 0.2358 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.37706652 -1.0483215
  0.68876547]
Sparsity at: 0.3020553916309013
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6413e-07 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.37786058 -1.0527827
  0.69044477]
Sparsity at: 0.3020553916309013
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1716e-07 - accuracy: 1.0000 - val_loss: 0.2392 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.37871218 -1.0572727
  0.6920911 ]
Sparsity at: 0.3020553916309013
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7494e-07 - accuracy: 1.0000 - val_loss: 0.2405 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.3795897  -1.0616144
  0.69364095]
Sparsity at: 0.3020553916309013
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3754e-07 - accuracy: 1.0000 - val_loss: 0.2422 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.3803709  -1.0658586
  0.6953236 ]
Sparsity at: 0.3020553916309013
Epoch 124/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0509e-07 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.38124338 -1.070061
  0.6966802 ]
Sparsity at: 0.3020553916309013
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7564e-07 - accuracy: 1.0000 - val_loss: 0.2454 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.38216603 -1.0741729
  0.69798815]
Sparsity at: 0.3020553916309013
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4975e-07 - accuracy: 1.0000 - val_loss: 0.2468 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.38285068 -1.0781872
  0.69955486]
Sparsity at: 0.3020553916309013
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2636e-07 - accuracy: 1.0000 - val_loss: 0.2479 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.38357112 -1.0821426
  0.70088595]
Sparsity at: 0.3020553916309013
Epoch 128/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0604e-07 - accuracy: 1.0000 - val_loss: 0.2494 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.38438904 -1.0860124
  0.70215183]
Sparsity at: 0.3020553916309013
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8822e-07 - accuracy: 1.0000 - val_loss: 0.2509 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.38513124 -1.089845
  0.70351726]
Sparsity at: 0.3020553916309013
Epoch 130/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7149e-07 - accuracy: 1.0000 - val_loss: 0.2524 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.38590807 -1.0934672
  0.70468336]
Sparsity at: 0.3020553916309013
Epoch 131/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5698e-07 - accuracy: 1.0000 - val_loss: 0.2531 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.38650522 -1.0970206
  0.70582664]
Sparsity at: 0.3020553916309013
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4371e-07 - accuracy: 1.0000 - val_loss: 0.2545 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.38720056 -1.1004738
  0.70704263]
Sparsity at: 0.3020553916309013
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3193e-07 - accuracy: 1.0000 - val_loss: 0.2562 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.38784903 -1.1038669
  0.70813674]
Sparsity at: 0.3020553916309013
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2162e-07 - accuracy: 1.0000 - val_loss: 0.2566 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.38855568 -1.1071227
  0.7092101 ]
Sparsity at: 0.3020553916309013
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1204e-07 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.38921872 -1.1103287
  0.71025485]
Sparsity at: 0.3020553916309013
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0355e-07 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.38985908 -1.1133968
  0.71128625]
Sparsity at: 0.3020553916309013
Epoch 137/500
235/235 [==============================] - 2s 8ms/step - loss: 9.6252e-08 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.39054394 -1.1163673
  0.71217316]
Sparsity at: 0.3020553916309013
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 8.9405e-08 - accuracy: 1.0000 - val_loss: 0.2607 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.39112422 -1.1192385
  0.713121  ]
Sparsity at: 0.3020553916309013
Epoch 139/500
235/235 [==============================] - 2s 9ms/step - loss: 8.2993e-08 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.39175558 -1.1220284
  0.71397656]
Sparsity at: 0.3020553916309013
Epoch 140/500
235/235 [==============================] - 2s 9ms/step - loss: 7.7571e-08 - accuracy: 1.0000 - val_loss: 0.2630 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.3922613  -1.1247317
  0.71482986]
Sparsity at: 0.3020553916309013
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2612e-08 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.39287773 -1.12731
  0.7156162 ]
Sparsity at: 0.3020553916309013
Epoch 142/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7987e-08 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.3934136  -1.1297511
  0.7163169 ]
Sparsity at: 0.3020553916309013
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 6.4065e-08 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.39389396 -1.1321375
  0.71714973]
Sparsity at: 0.3020553916309013
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0185e-08 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.39440697 -1.1344634
  0.7179217 ]
Sparsity at: 0.3020553916309013
Epoch 145/500
235/235 [==============================] - 2s 8ms/step - loss: 5.6734e-08 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.39489898 -1.1367143
  0.7186596 ]
Sparsity at: 0.3020553916309013
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3511e-08 - accuracy: 1.0000 - val_loss: 0.2677 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.3953859  -1.1388718
  0.71936715]
Sparsity at: 0.3020553916309013
Epoch 147/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0735e-08 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.39587575 -1.1409422
  0.7201189 ]
Sparsity at: 0.3020553916309013
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8065e-08 - accuracy: 1.0000 - val_loss: 0.2694 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.39630726 -1.1429724
  0.7207858 ]
Sparsity at: 0.3020553916309013
Epoch 149/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5667e-08 - accuracy: 1.0000 - val_loss: 0.2698 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.39672115 -1.1449288
  0.7214309 ]
Sparsity at: 0.3020553916309013
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3609e-08 - accuracy: 1.0000 - val_loss: 0.2708 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.39718214 -1.1468433
  0.72199345]
Sparsity at: 0.3020553916309013
Epoch 151/500
Wanted sparsity 0.84481686
Upper percentile 0.25270230488357726
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 0.41199740619523517
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 0. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.84481686
Upper percentile 1.114794101674569
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 42s 7ms/step - loss: 4.1533e-08 - accuracy: 1.0000 - val_loss: 0.2711 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.39754912 -1.1486295
  0.7226232 ]
Sparsity at: 0.3020553916309013
Epoch 152/500
235/235 [==============================] - 2s 7ms/step - loss: 3.9657e-08 - accuracy: 1.0000 - val_loss: 0.2717 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.39796552 -1.150314
  0.7231762 ]
Sparsity at: 0.3020553916309013
Epoch 153/500
235/235 [==============================] - 2s 7ms/step - loss: 3.7913e-08 - accuracy: 1.0000 - val_loss: 0.2724 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.39832076 -1.1520225
  0.7236695 ]
Sparsity at: 0.3020553916309013
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6331e-08 - accuracy: 1.0000 - val_loss: 0.2730 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.3987034  -1.1536518
  0.72415704]
Sparsity at: 0.3020553916309013
Epoch 155/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4650e-08 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.39907932 -1.1551743
  0.72460157]
Sparsity at: 0.3020553916309013
Epoch 156/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3337e-08 - accuracy: 1.0000 - val_loss: 0.2744 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.3994371  -1.1566299
  0.72514033]
Sparsity at: 0.3020553916309013
Epoch 157/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2123e-08 - accuracy: 1.0000 - val_loss: 0.2748 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.39980236 -1.1580284
  0.7256011 ]
Sparsity at: 0.3020553916309013
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0939e-08 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 0.9754
[ 0.02126332  0.          0.04348067 ...  0.4001185  -1.1594058
  0.7260858 ]
Sparsity at: 0.3020553916309013
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9878e-08 - accuracy: 1.0000 - val_loss: 0.2760 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.40044922 -1.1607648
  0.72651744]
Sparsity at: 0.3020553916309013
Epoch 160/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8723e-08 - accuracy: 1.0000 - val_loss: 0.2763 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.4007979  -1.1620554
  0.72695684]
Sparsity at: 0.3020553916309013
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7802e-08 - accuracy: 1.0000 - val_loss: 0.2769 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.40113485 -1.1632669
  0.72736925]
Sparsity at: 0.3020553916309013
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6824e-08 - accuracy: 1.0000 - val_loss: 0.2776 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.40147457 -1.1644619
  0.7277867 ]
Sparsity at: 0.3020553916309013
Epoch 163/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5978e-08 - accuracy: 1.0000 - val_loss: 0.2782 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.40179595 -1.1656052
  0.7281475 ]
Sparsity at: 0.3020553916309013
Epoch 164/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5207e-08 - accuracy: 1.0000 - val_loss: 0.2787 - val_accuracy: 0.9755
[ 0.02126332  0.          0.04348067 ...  0.40215182 -1.1667194
  0.7285153 ]
Sparsity at: 0.3020553916309013
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4523e-08 - accuracy: 1.0000 - val_loss: 0.2790 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.40246183 -1.1678003
  0.72886705]
Sparsity at: 0.3020553916309013
Epoch 166/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3774e-08 - accuracy: 1.0000 - val_loss: 0.2796 - val_accuracy: 0.9757
[ 0.02126332  0.          0.04348067 ...  0.40277153 -1.1688373
  0.7291956 ]
Sparsity at: 0.3020553916309013
Epoch 167/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3137e-08 - accuracy: 1.0000 - val_loss: 0.2801 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.4030929  -1.1698108
  0.7295016 ]
Sparsity at: 0.3020553916309013
Epoch 168/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2471e-08 - accuracy: 1.0000 - val_loss: 0.2807 - val_accuracy: 0.9756
[ 0.02126332  0.          0.04348067 ...  0.40340135 -1.1707615
  0.72982633]
Sparsity at: 0.3020553916309013
Epoch 169/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1911e-08 - accuracy: 1.0000 - val_loss: 0.2813 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.40372908 -1.1717043
  0.7301472 ]
Sparsity at: 0.3020553916309013
Epoch 170/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1271e-08 - accuracy: 1.0000 - val_loss: 0.2818 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.40403262 -1.172613
  0.73045737]
Sparsity at: 0.3020553916309013
Epoch 171/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0782e-08 - accuracy: 1.0000 - val_loss: 0.2823 - val_accuracy: 0.9753
[ 0.02126332  0.          0.04348067 ...  0.40432006 -1.1735181
  0.7308144 ]
Sparsity at: 0.3020553916309013
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0226e-08 - accuracy: 1.0000 - val_loss: 0.2826 - val_accuracy: 0.9751
[ 0.02126332  0.          0.04348067 ...  0.4046306  -1.1743859
  0.73115295]
Sparsity at: 0.3020553916309013
Epoch 173/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9652e-08 - accuracy: 1.0000 - val_loss: 0.2832 - val_accuracy: 0.9752
[ 0.02126332  0.          0.04348067 ...  0.4049353  -1.1752218
  0.7314857 ]
Sparsity at: 0.3020553916309013
Epoch 174/500
235/235 [==============================] - 2s 8ms/step - loss: 1.9163e-08 - accuracy: 1.0000 - val_loss: 0.2834 - val_accuracy: 0.9752
[ 0.02126332  0.          0.04348067 ...  0.40523517 -1.1760334
  0.7317776 ]
Sparsity at: 0.3020553916309013
Epoch 175/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8732e-08 - accuracy: 1.0000 - val_loss: 0.2839 - val_accuracy: 0.9751
[ 0.02126332  0.          0.04348067 ...  0.405517   -1.1768292
  0.73207796]
Sparsity at: 0.3020553916309013
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 1.8219e-08 - accuracy: 1.0000 - val_loss: 0.2845 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.40579262 -1.1775489
  0.7324003 ]
Sparsity at: 0.3020553916309013
Epoch 177/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7794e-08 - accuracy: 1.0000 - val_loss: 0.2847 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.40606073 -1.1782804
  0.7327133 ]
Sparsity at: 0.3020553916309013
Epoch 178/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7353e-08 - accuracy: 1.0000 - val_loss: 0.2852 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.40634128 -1.1789798
  0.7330158 ]
Sparsity at: 0.3020553916309013
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7029e-08 - accuracy: 1.0000 - val_loss: 0.2856 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.40660667 -1.1796848
  0.7333078 ]
Sparsity at: 0.3020553916309013
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6648e-08 - accuracy: 1.0000 - val_loss: 0.2859 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.4068455  -1.1803658
  0.73362   ]
Sparsity at: 0.3020553916309013
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6304e-08 - accuracy: 1.0000 - val_loss: 0.2864 - val_accuracy: 0.9751
[ 0.02126332  0.          0.04348067 ...  0.40710312 -1.1810097
  0.7339123 ]
Sparsity at: 0.3020553916309013
Epoch 182/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5875e-08 - accuracy: 1.0000 - val_loss: 0.2866 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.40735534 -1.1816602
  0.7341666 ]
Sparsity at: 0.3020553916309013
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5628e-08 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.40760913 -1.1822644
  0.7344539 ]
Sparsity at: 0.3020553916309013
Epoch 184/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5267e-08 - accuracy: 1.0000 - val_loss: 0.2874 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.4078724  -1.1828725
  0.7347268 ]
Sparsity at: 0.3020553916309013
Epoch 185/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4947e-08 - accuracy: 1.0000 - val_loss: 0.2877 - val_accuracy: 0.9751
[ 0.02126332  0.          0.04348067 ...  0.40814027 -1.1834705
  0.73498434]
Sparsity at: 0.3020553916309013
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4679e-08 - accuracy: 1.0000 - val_loss: 0.2879 - val_accuracy: 0.9751
[ 0.02126332  0.          0.04348067 ...  0.4084104  -1.1840194
  0.7352781 ]
Sparsity at: 0.3020553916309013
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4317e-08 - accuracy: 1.0000 - val_loss: 0.2883 - val_accuracy: 0.9751
[ 0.02126332  0.          0.04348067 ...  0.40868747 -1.184552
  0.7355466 ]
Sparsity at: 0.3020553916309013
Epoch 188/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4057e-08 - accuracy: 1.0000 - val_loss: 0.2885 - val_accuracy: 0.9752
[ 0.02126332  0.          0.04348067 ...  0.40896714 -1.1851096
  0.73580366]
Sparsity at: 0.3020553916309013
Epoch 189/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3765e-08 - accuracy: 1.0000 - val_loss: 0.2888 - val_accuracy: 0.9752
[ 0.02126332  0.          0.04348067 ...  0.40923104 -1.1856184
  0.7360505 ]
Sparsity at: 0.3020553916309013
Epoch 190/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3514e-08 - accuracy: 1.0000 - val_loss: 0.2889 - val_accuracy: 0.9752
[ 0.02126332  0.          0.04348067 ...  0.40945876 -1.1861299
  0.7362822 ]
Sparsity at: 0.3020553916309013
Epoch 191/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3244e-08 - accuracy: 1.0000 - val_loss: 0.2892 - val_accuracy: 0.9752
[ 0.02126332  0.          0.04348067 ...  0.4096933  -1.1866218
  0.7365049 ]
Sparsity at: 0.3020553916309013
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2978e-08 - accuracy: 1.0000 - val_loss: 0.2896 - val_accuracy: 0.9751
[ 0.02126332  0.          0.04348067 ...  0.40992075 -1.1871115
  0.73672694]
Sparsity at: 0.3020553916309013
Epoch 193/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2726e-08 - accuracy: 1.0000 - val_loss: 0.2897 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.41014296 -1.1876031
  0.7369361 ]
Sparsity at: 0.3020553916309013
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2491e-08 - accuracy: 1.0000 - val_loss: 0.2900 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.4103491  -1.188099
  0.73713005]
Sparsity at: 0.3020553916309013
Epoch 195/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2253e-08 - accuracy: 1.0000 - val_loss: 0.2901 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.41056684 -1.1885579
  0.7373313 ]
Sparsity at: 0.3020553916309013
Epoch 196/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2066e-08 - accuracy: 1.0000 - val_loss: 0.2903 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.4107773  -1.1890384
  0.73757356]
Sparsity at: 0.3020553916309013
Epoch 197/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1841e-08 - accuracy: 1.0000 - val_loss: 0.2904 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.41100466 -1.1894847
  0.73778695]
Sparsity at: 0.3020553916309013
Epoch 198/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1617e-08 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41123056 -1.1899287
  0.7379922 ]
Sparsity at: 0.3020553916309013
Epoch 199/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1440e-08 - accuracy: 1.0000 - val_loss: 0.2909 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41144383 -1.1903454
  0.73823285]
Sparsity at: 0.3020553916309013
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1230e-08 - accuracy: 1.0000 - val_loss: 0.2909 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.41168195 -1.1907562
  0.7384579 ]
Sparsity at: 0.3020553916309013
Epoch 201/500
Wanted sparsity 0.90598273
Upper percentile 0.32897635305230466
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 0.5186788086043634
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 0. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.90598273
Upper percentile 1.3412633376150325
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 42s 7ms/step - loss: 1.0997e-08 - accuracy: 1.0000 - val_loss: 0.2911 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41189814 -1.1911304
  0.7386835 ]
Sparsity at: 0.3020553916309013
Epoch 202/500
235/235 [==============================] - 2s 7ms/step - loss: 1.0810e-08 - accuracy: 1.0000 - val_loss: 0.2913 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.4121093  -1.1915112
  0.73892367]
Sparsity at: 0.3020553916309013
Epoch 203/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0655e-08 - accuracy: 1.0000 - val_loss: 0.2914 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.41230246 -1.1919066
  0.7391482 ]
Sparsity at: 0.3020553916309013
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0480e-08 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.41250992 -1.1922907
  0.73936963]
Sparsity at: 0.3020553916309013
Epoch 205/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0312e-08 - accuracy: 1.0000 - val_loss: 0.2917 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.41270247 -1.1926603
  0.7396002 ]
Sparsity at: 0.3020553916309013
Epoch 206/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0145e-08 - accuracy: 1.0000 - val_loss: 0.2920 - val_accuracy: 0.9750
[ 0.02126332  0.          0.04348067 ...  0.41290188 -1.1930207
  0.73980415]
Sparsity at: 0.3020553916309013
Epoch 207/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0018e-08 - accuracy: 1.0000 - val_loss: 0.2921 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41311085 -1.1933827
  0.74001896]
Sparsity at: 0.3020553916309013
Epoch 208/500
235/235 [==============================] - 2s 8ms/step - loss: 9.8586e-09 - accuracy: 1.0000 - val_loss: 0.2922 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.4133249  -1.193743
  0.7401866 ]
Sparsity at: 0.3020553916309013
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7156e-09 - accuracy: 1.0000 - val_loss: 0.2924 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41352913 -1.1940681
  0.7403836 ]
Sparsity at: 0.3020553916309013
Epoch 210/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5646e-09 - accuracy: 1.0000 - val_loss: 0.2925 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41374967 -1.1943852
  0.7405639 ]
Sparsity at: 0.3020553916309013
Epoch 211/500
235/235 [==============================] - 2s 8ms/step - loss: 9.4632e-09 - accuracy: 1.0000 - val_loss: 0.2926 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41394046 -1.1947309
  0.7407478 ]
Sparsity at: 0.3020553916309013
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 9.2804e-09 - accuracy: 1.0000 - val_loss: 0.2928 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41413978 -1.1950192
  0.74092835]
Sparsity at: 0.3020553916309013
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 9.2129e-09 - accuracy: 1.0000 - val_loss: 0.2928 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.4143285  -1.1953473
  0.74111116]
Sparsity at: 0.3020553916309013
Epoch 214/500
235/235 [==============================] - 2s 8ms/step - loss: 9.0778e-09 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.41453606 -1.1956526
  0.7412978 ]
Sparsity at: 0.3020553916309013
Epoch 215/500
235/235 [==============================] - 2s 8ms/step - loss: 8.9387e-09 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9748
[ 0.02126332  0.          0.04348067 ...  0.41472086 -1.1959534
  0.7414813 ]
Sparsity at: 0.3020553916309013
Epoch 216/500
235/235 [==============================] - 2s 8ms/step - loss: 8.7937e-09 - accuracy: 1.0000 - val_loss: 0.2933 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.41488424 -1.1962414
  0.7416832 ]
Sparsity at: 0.3020553916309013
Epoch 217/500
235/235 [==============================] - 2s 8ms/step - loss: 8.6745e-09 - accuracy: 1.0000 - val_loss: 0.2934 - val_accuracy: 0.9748
[ 0.02126332  0.          0.04348067 ...  0.41507727 -1.1965115
  0.7418821 ]
Sparsity at: 0.3020553916309013
Epoch 218/500
235/235 [==============================] - 2s 8ms/step - loss: 8.5235e-09 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.41527244 -1.1967857
  0.74203086]
Sparsity at: 0.3020553916309013
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 8.4698e-09 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.41544256 -1.1970468
  0.7422091 ]
Sparsity at: 0.3020553916309013
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 8.3486e-09 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.41562253 -1.1973232
  0.7423906 ]
Sparsity at: 0.3020553916309013
Epoch 221/500
235/235 [==============================] - 2s 8ms/step - loss: 8.2215e-09 - accuracy: 1.0000 - val_loss: 0.2939 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.41581276 -1.1975768
  0.7425695 ]
Sparsity at: 0.3020553916309013
Epoch 222/500
235/235 [==============================] - 2s 8ms/step - loss: 8.1062e-09 - accuracy: 1.0000 - val_loss: 0.2939 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4159862  -1.197835
  0.74273264]
Sparsity at: 0.3020553916309013
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 8.0268e-09 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.41616452 -1.1980891
  0.74292314]
Sparsity at: 0.3020553916309013
Epoch 224/500
235/235 [==============================] - 2s 8ms/step - loss: 7.9572e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.41632533 -1.1983374
  0.74309444]
Sparsity at: 0.3020553916309013
Epoch 225/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8599e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.41648182 -1.1985697
  0.7432732 ]
Sparsity at: 0.3020553916309013
Epoch 226/500
235/235 [==============================] - 2s 8ms/step - loss: 7.7685e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4166369  -1.1987923
  0.7434664 ]
Sparsity at: 0.3020553916309013
Epoch 227/500
235/235 [==============================] - 2s 8ms/step - loss: 7.6791e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.41679347 -1.1990248
  0.7436146 ]
Sparsity at: 0.3020553916309013
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5261e-09 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.41694933 -1.1992478
  0.7438167 ]
Sparsity at: 0.3020553916309013
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 7.4506e-09 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.41711432 -1.1994696
  0.7439899 ]
Sparsity at: 0.3020553916309013
Epoch 230/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3949e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.41729105 -1.1996717
  0.7441569 ]
Sparsity at: 0.3020553916309013
Epoch 231/500
235/235 [==============================] - 2s 8ms/step - loss: 7.3175e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.41745865 -1.1999032
  0.74432015]
Sparsity at: 0.3020553916309013
Epoch 232/500
235/235 [==============================] - 2s 8ms/step - loss: 7.1943e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.41764727 -1.2000784
  0.74449575]
Sparsity at: 0.3020553916309013
Epoch 233/500
235/235 [==============================] - 2s 8ms/step - loss: 7.1406e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4178118  -1.2002548
  0.7446694 ]
Sparsity at: 0.3020553916309013
Epoch 234/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0095e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.41799542 -1.2004259
  0.74486655]
Sparsity at: 0.3020553916309013
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 6.9042e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4181846  -1.2005795
  0.7450589 ]
Sparsity at: 0.3020553916309013
Epoch 236/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8863e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.41836572 -1.2007366
  0.74526924]
Sparsity at: 0.3020553916309013
Epoch 237/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8128e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.41854388 -1.2008815
  0.74544704]
Sparsity at: 0.3020553916309013
Epoch 238/500
235/235 [==============================] - 2s 8ms/step - loss: 6.7353e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.41874114 -1.2010219
  0.7456279 ]
Sparsity at: 0.3020553916309013
Epoch 239/500
235/235 [==============================] - 2s 8ms/step - loss: 6.6340e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4189321  -1.2011335
  0.74580586]
Sparsity at: 0.3020553916309013
Epoch 240/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5962e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.41911608 -1.2012739
  0.74599755]
Sparsity at: 0.3020553916309013
Epoch 241/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5088e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.41929668 -1.2013761
  0.7461803 ]
Sparsity at: 0.3020553916309013
Epoch 242/500
235/235 [==============================] - 2s 8ms/step - loss: 6.4095e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4194813  -1.2014809
  0.7463962 ]
Sparsity at: 0.3020553916309013
Epoch 243/500
235/235 [==============================] - 2s 8ms/step - loss: 6.3658e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4196692  -1.2015927
  0.746583  ]
Sparsity at: 0.3020553916309013
Epoch 244/500
235/235 [==============================] - 2s 8ms/step - loss: 6.3399e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4198622  -1.2017004
  0.74678266]
Sparsity at: 0.3020553916309013
Epoch 245/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2803e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.42005274 -1.2017969
  0.74694496]
Sparsity at: 0.3020553916309013
Epoch 246/500
235/235 [==============================] - 2s 8ms/step - loss: 6.2168e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.42024437 -1.201914
  0.7471218 ]
Sparsity at: 0.3020553916309013
Epoch 247/500
235/235 [==============================] - 2s 8ms/step - loss: 6.1452e-09 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4204311  -1.2019912
  0.74729294]
Sparsity at: 0.3020553916309013
Epoch 248/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0638e-09 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4206216  -1.2020781
  0.7474642 ]
Sparsity at: 0.3020553916309013
Epoch 249/500
235/235 [==============================] - 2s 8ms/step - loss: 6.0201e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.42082593 -1.2021646
  0.74762857]
Sparsity at: 0.3020553916309013
Epoch 250/500
235/235 [==============================] - 2s 8ms/step - loss: 5.9346e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4210017  -1.202281
  0.747803  ]
Sparsity at: 0.3020553916309013
Epoch 251/500
Wanted sparsity 0.9469837
Upper percentile 0.41203891892625677
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 0.6075214533092677
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 0. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9469837
Upper percentile 1.5514764097761145
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 44s 7ms/step - loss: 5.9048e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4211976  -1.2023685
  0.7479691 ]
Sparsity at: 0.3020553916309013
Epoch 252/500
235/235 [==============================] - 2s 7ms/step - loss: 5.8929e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4213776  -1.2024628
  0.74814194]
Sparsity at: 0.3020553916309013
Epoch 253/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7836e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4215615  -1.2025505
  0.74830467]
Sparsity at: 0.3020553916309013
Epoch 254/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7419e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42171392 -1.2026299
  0.74848413]
Sparsity at: 0.3020553916309013
Epoch 255/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7101e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42191166 -1.2027004
  0.74866796]
Sparsity at: 0.3020553916309013
Epoch 256/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5909e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42208704 -1.2027755
  0.7488535 ]
Sparsity at: 0.3020553916309013
Epoch 257/500
235/235 [==============================] - 2s 8ms/step - loss: 5.5333e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42225656 -1.202837
  0.74905425]
Sparsity at: 0.3020553916309013
Epoch 258/500
235/235 [==============================] - 2s 9ms/step - loss: 5.5214e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42242593 -1.2029052
  0.7492231 ]
Sparsity at: 0.3020553916309013
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 5.4797e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42260206 -1.2029502
  0.7494042 ]
Sparsity at: 0.3020553916309013
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 5.4677e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42277065 -1.2030219
  0.7495975 ]
Sparsity at: 0.3020553916309013
Epoch 261/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3783e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42294705 -1.2030613
  0.7497704 ]
Sparsity at: 0.3020553916309013
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 5.3386e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42310962 -1.2031165
  0.7499439 ]
Sparsity at: 0.3020553916309013
Epoch 263/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2949e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4232688  -1.2031521
  0.7501253 ]
Sparsity at: 0.3020553916309013
Epoch 264/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2472e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42344227 -1.2031747
  0.750311  ]
Sparsity at: 0.3020553916309013
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2373e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42360845 -1.2032051
  0.7504784 ]
Sparsity at: 0.3020553916309013
Epoch 266/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1518e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42377922 -1.2032326
  0.7506681 ]
Sparsity at: 0.3020553916309013
Epoch 267/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1737e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4239427  -1.2032685
  0.7508458 ]
Sparsity at: 0.3020553916309013
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0545e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42411447 -1.2032768
  0.7510166 ]
Sparsity at: 0.3020553916309013
Epoch 269/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0803e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42429924 -1.2032886
  0.7512091 ]
Sparsity at: 0.3020553916309013
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 5.0207e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42444593 -1.2032864
  0.7513876 ]
Sparsity at: 0.3020553916309013
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 4.9969e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42461672 -1.2032986
  0.75157887]
Sparsity at: 0.3020553916309013
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9452e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42478597 -1.203322
  0.7517516 ]
Sparsity at: 0.3020553916309013
Epoch 273/500
235/235 [==============================] - 2s 8ms/step - loss: 4.9194e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42497346 -1.2033162
  0.75192946]
Sparsity at: 0.3020553916309013
Epoch 274/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8379e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42515004 -1.2033033
  0.7521158 ]
Sparsity at: 0.3020553916309013
Epoch 275/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7942e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.425301   -1.2032802
  0.75227886]
Sparsity at: 0.3020553916309013
Epoch 276/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7922e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42546347 -1.2032843
  0.75244486]
Sparsity at: 0.3020553916309013
Epoch 277/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7664e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42562312 -1.2032888
  0.7526034 ]
Sparsity at: 0.3020553916309013
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6968e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42579433 -1.2032748
  0.75278455]
Sparsity at: 0.3020553916309013
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 4.7366e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42594197 -1.2032696
  0.75295717]
Sparsity at: 0.3020553916309013
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6035e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42612684 -1.2032402
  0.7531086 ]
Sparsity at: 0.3020553916309013
Epoch 281/500
235/235 [==============================] - 2s 8ms/step - loss: 4.6213e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42630962 -1.2032082
  0.7532856 ]
Sparsity at: 0.3020553916309013
Epoch 282/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5995e-09 - accuracy: 1.0000 - val_loss: 0.2981 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4264684  -1.2032002
  0.7534619 ]
Sparsity at: 0.3020553916309013
Epoch 283/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5637e-09 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42664525 -1.2031788
  0.7536176 ]
Sparsity at: 0.3020553916309013
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5121e-09 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42681187 -1.2031475
  0.75380975]
Sparsity at: 0.3020553916309013
Epoch 285/500
235/235 [==============================] - 2s 8ms/step - loss: 4.5439e-09 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42696738 -1.2031196
  0.75398743]
Sparsity at: 0.3020553916309013
Epoch 286/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4227e-09 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42713678 -1.2030857
  0.7541824 ]
Sparsity at: 0.3020553916309013
Epoch 287/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4227e-09 - accuracy: 1.0000 - val_loss: 0.2983 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42729527 -1.2030532
  0.75435305]
Sparsity at: 0.3020553916309013
Epoch 288/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4068e-09 - accuracy: 1.0000 - val_loss: 0.2983 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4274472  -1.2030358
  0.754525  ]
Sparsity at: 0.3020553916309013
Epoch 289/500
235/235 [==============================] - 2s 8ms/step - loss: 4.4207e-09 - accuracy: 1.0000 - val_loss: 0.2983 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.42759472 -1.2030027
  0.75467825]
Sparsity at: 0.3020553916309013
Epoch 290/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3511e-09 - accuracy: 1.0000 - val_loss: 0.2984 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.42777023 -1.2029535
  0.7548717 ]
Sparsity at: 0.3020553916309013
Epoch 291/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3015e-09 - accuracy: 1.0000 - val_loss: 0.2984 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42794365 -1.2029169
  0.75503725]
Sparsity at: 0.3020553916309013
Epoch 292/500
235/235 [==============================] - 2s 8ms/step - loss: 4.3333e-09 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4281233  -1.2028645
  0.75522673]
Sparsity at: 0.3020553916309013
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2975e-09 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42827424 -1.2028269
  0.75540996]
Sparsity at: 0.3020553916309013
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2379e-09 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.42845312 -1.2027583
  0.75557715]
Sparsity at: 0.3020553916309013
Epoch 295/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2538e-09 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.42860255 -1.2027283
  0.75573504]
Sparsity at: 0.3020553916309013
Epoch 296/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1823e-09 - accuracy: 1.0000 - val_loss: 0.2986 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.4287646  -1.2026851
  0.7559277 ]
Sparsity at: 0.3020553916309013
Epoch 297/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1982e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.4289348  -1.2026632
  0.7561077 ]
Sparsity at: 0.3020553916309013
Epoch 298/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1564e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.4290832  -1.2025799
  0.7562703 ]
Sparsity at: 0.3020553916309013
Epoch 299/500
235/235 [==============================] - 2s 8ms/step - loss: 4.1803e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.42926866 -1.2025566
  0.75647074]
Sparsity at: 0.3020553916309013
Epoch 300/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0551e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.42943448 -1.2025026
  0.75665396]
Sparsity at: 0.3020553916309013
Epoch 301/500
Wanted sparsity 0.9718529
Upper percentile 0.5041835091003932
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 0.6901974902365353
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 0. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9718529
Upper percentile 1.7779325935055965
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 44s 8ms/step - loss: 4.0710e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.42962724 -1.2024461
  0.75683504]
Sparsity at: 0.3020553916309013
Epoch 302/500
235/235 [==============================] - 2s 7ms/step - loss: 4.0730e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.42979398 -1.2023813
  0.75702316]
Sparsity at: 0.3020553916309013
Epoch 303/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0670e-09 - accuracy: 1.0000 - val_loss: 0.2988 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.4299462  -1.2023201
  0.7571892 ]
Sparsity at: 0.3020553916309013
Epoch 304/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9955e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43011278 -1.2022681
  0.7573853 ]
Sparsity at: 0.3020553916309013
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0392e-09 - accuracy: 1.0000 - val_loss: 0.2988 - val_accuracy: 0.9748
[ 0.02126332  0.          0.04348067 ...  0.43027496 -1.2022394
  0.75756574]
Sparsity at: 0.3020553916309013
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9399e-09 - accuracy: 1.0000 - val_loss: 0.2988 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43044317 -1.2021407
  0.7577472 ]
Sparsity at: 0.3020553916309013
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0074e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43062112 -1.2021099
  0.75792235]
Sparsity at: 0.3020553916309013
Epoch 308/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9538e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43080148 -1.202039
  0.75811857]
Sparsity at: 0.3020553916309013
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9220e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43096304 -1.2019972
  0.7582874 ]
Sparsity at: 0.3020553916309013
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 3.9160e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43112087 -1.2019482
  0.7584692 ]
Sparsity at: 0.3020553916309013
Epoch 311/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8226e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43127662 -1.2018526
  0.75867367]
Sparsity at: 0.3020553916309013
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8485e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.4314353  -1.2017882
  0.7588634 ]
Sparsity at: 0.3020553916309013
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8445e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.4316052  -1.201714
  0.7590403 ]
Sparsity at: 0.3020553916309013
Epoch 314/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8485e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43175533 -1.2016523
  0.75923043]
Sparsity at: 0.3020553916309013
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8107e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43192315 -1.201576
  0.75943536]
Sparsity at: 0.3020553916309013
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8048e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9749
[ 0.02126332  0.          0.04348067 ...  0.4320782  -1.2015322
  0.7596271 ]
Sparsity at: 0.3020553916309013
Epoch 317/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7988e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43222234 -1.201458
  0.7598009 ]
Sparsity at: 0.3020553916309013
Epoch 318/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7849e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43237486 -1.2014086
  0.75997895]
Sparsity at: 0.3020553916309013
Epoch 319/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7412e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43253955 -1.2013321
  0.7601703 ]
Sparsity at: 0.3020553916309013
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7611e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9748
[ 0.02126332  0.          0.04348067 ...  0.43270895 -1.201246
  0.76035815]
Sparsity at: 0.3020553916309013
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6637e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43286926 -1.201169
  0.76052773]
Sparsity at: 0.3020553916309013
Epoch 322/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7034e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9747
[ 0.02126332  0.          0.04348067 ...  0.43304655 -1.201109
  0.76072556]
Sparsity at: 0.3020553916309013
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6816e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.43320101 -1.2010474
  0.7609369 ]
Sparsity at: 0.3020553916309013
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6677e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.43337584 -1.2009869
  0.7611411 ]
Sparsity at: 0.3020553916309013
Epoch 325/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6617e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43353876 -1.2009194
  0.7613162 ]
Sparsity at: 0.3020553916309013
Epoch 326/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6418e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4336833  -1.2008481
  0.7615021 ]
Sparsity at: 0.3020553916309013
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6220e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.43385506 -1.2007889
  0.76169956]
Sparsity at: 0.3020553916309013
Epoch 328/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6259e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43402994 -1.2007078
  0.76188004]
Sparsity at: 0.3020553916309013
Epoch 329/500
235/235 [==============================] - 2s 8ms/step - loss: 3.6200e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43420014 -1.2006179
  0.7620791 ]
Sparsity at: 0.3020553916309013
Epoch 330/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5604e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.43435845 -1.2005686
  0.7622587 ]
Sparsity at: 0.3020553916309013
Epoch 331/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5723e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43451008 -1.2004873
  0.7624602 ]
Sparsity at: 0.3020553916309013
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5385e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43466958 -1.2004151
  0.7626764 ]
Sparsity at: 0.3020553916309013
Epoch 333/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5544e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43484166 -1.2003417
  0.7628837 ]
Sparsity at: 0.3020553916309013
Epoch 334/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4451e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43500692 -1.2002516
  0.76308435]
Sparsity at: 0.3020553916309013
Epoch 335/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4948e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4351614  -1.2001725
  0.7632671 ]
Sparsity at: 0.3020553916309013
Epoch 336/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5127e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4353184  -1.2001033
  0.763446  ]
Sparsity at: 0.3020553916309013
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4908e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43546447 -1.2000476
  0.7636349 ]
Sparsity at: 0.3020553916309013
Epoch 338/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4670e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.43562397 -1.1999848
  0.76381   ]
Sparsity at: 0.3020553916309013
Epoch 339/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4571e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43578553 -1.1999032
  0.76400614]
Sparsity at: 0.3020553916309013
Epoch 340/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4332e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4359646  -1.199801
  0.7642023 ]
Sparsity at: 0.3020553916309013
Epoch 341/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4253e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4361326  -1.1997018
  0.7644077 ]
Sparsity at: 0.3020553916309013
Epoch 342/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3895e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4362772  -1.1996224
  0.7645682 ]
Sparsity at: 0.3020553916309013
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4054e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43643862 -1.1995416
  0.7647525 ]
Sparsity at: 0.3020553916309013
Epoch 344/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4193e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4366154  -1.1994528
  0.76491815]
Sparsity at: 0.3020553916309013
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 3.4114e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.43677664 -1.1993619
  0.76511544]
Sparsity at: 0.3020553916309013
Epoch 346/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3776e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4369398  -1.1992807
  0.7653003 ]
Sparsity at: 0.3020553916309013
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3259e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.43709382 -1.1992131
  0.76547414]
Sparsity at: 0.3020553916309013
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 3.3577e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.43722713 -1.1991279
  0.76565117]
Sparsity at: 0.3020553916309013
Epoch 349/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3319e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.43739134 -1.1990519
  0.7658348 ]
Sparsity at: 0.3020553916309013
Epoch 350/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3677e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.43754336 -1.1989881
  0.76600736]
Sparsity at: 0.3020553916309013
Epoch 351/500
Wanted sparsity 0.9846233
Upper percentile 0.5842941218939899
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 0.7564832252152556
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 0. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.9846233
Upper percentile 1.9474849072882847
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 44s 7ms/step - loss: 3.2524e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.43771684 -1.1988655
  0.76617086]
Sparsity at: 0.3020553916309013
Epoch 352/500
235/235 [==============================] - 2s 7ms/step - loss: 3.3597e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43789226 -1.1987897
  0.7663675 ]
Sparsity at: 0.3020553916309013
Epoch 353/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2802e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4380473  -1.1986862
  0.76655674]
Sparsity at: 0.3020553916309013
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2564e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4381995  -1.1985962
  0.7667392 ]
Sparsity at: 0.3020553916309013
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2802e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43839976 -1.1985067
  0.766919  ]
Sparsity at: 0.3020553916309013
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2345e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.43855318 -1.198407
  0.76711196]
Sparsity at: 0.3020553916309013
Epoch 357/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2743e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.43871936 -1.1983039
  0.76730174]
Sparsity at: 0.3020553916309013
Epoch 358/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2504e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9746
[ 0.02126332  0.          0.04348067 ...  0.4388812  -1.1982012
  0.7674936 ]
Sparsity at: 0.3020553916309013
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1888e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.43904704 -1.198101
  0.76766443]
Sparsity at: 0.3020553916309013
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2187e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4392217  -1.1980063
  0.7678803 ]
Sparsity at: 0.3020553916309013
Epoch 361/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2465e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.43938953 -1.1979202
  0.7680611 ]
Sparsity at: 0.3020553916309013
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1749e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4395592  -1.1978035
  0.7682446 ]
Sparsity at: 0.3020553916309013
Epoch 363/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2206e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43974194 -1.1976755
  0.76841307]
Sparsity at: 0.3020553916309013
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1928e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.43991554 -1.1975871
  0.76861924]
Sparsity at: 0.3020553916309013
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1590e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4400882  -1.197485
  0.7687996 ]
Sparsity at: 0.3020553916309013
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1650e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44028583 -1.197361
  0.76899344]
Sparsity at: 0.3020553916309013
Epoch 367/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1630e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4404426  -1.1972709
  0.76919097]
Sparsity at: 0.3020553916309013
Epoch 368/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1332e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44061518 -1.1971676
  0.7693669 ]
Sparsity at: 0.3020553916309013
Epoch 369/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1412e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4407775  -1.1970512
  0.7695454 ]
Sparsity at: 0.3020553916309013
Epoch 370/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1153e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44095668 -1.1969222
  0.7697713 ]
Sparsity at: 0.3020553916309013
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1372e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44114006 -1.196836
  0.76995057]
Sparsity at: 0.3020553916309013
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1094e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44129962 -1.1967254
  0.7701492 ]
Sparsity at: 0.3020553916309013
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1352e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.44148386 -1.1966279
  0.77036554]
Sparsity at: 0.3020553916309013
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1014e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44165543 -1.1965252
  0.77054507]
Sparsity at: 0.3020553916309013
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0875e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44183984 -1.1964163
  0.77074057]
Sparsity at: 0.3020553916309013
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0518e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4420131  -1.1962901
  0.7709345 ]
Sparsity at: 0.3020553916309013
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0875e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4421901  -1.1961501
  0.7711171 ]
Sparsity at: 0.3020553916309013
Epoch 378/500
235/235 [==============================] - 2s 10ms/step - loss: 3.0776e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4423621  -1.196039
  0.77131116]
Sparsity at: 0.3020553916309013
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0498e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44252345 -1.1959181
  0.77150357]
Sparsity at: 0.3020553916309013
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0458e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44269204 -1.1958115
  0.7716891 ]
Sparsity at: 0.3020553916309013
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0696e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44286236 -1.1956986
  0.77189344]
Sparsity at: 0.3020553916309013
Epoch 382/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0577e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44303998 -1.195598
  0.77208185]
Sparsity at: 0.3020553916309013
Epoch 383/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0061e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4432085  -1.1954632
  0.77227503]
Sparsity at: 0.3020553916309013
Epoch 384/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0339e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.44337362 -1.1953372
  0.77246135]
Sparsity at: 0.3020553916309013
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0438e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4435464  -1.1952161
  0.7726563 ]
Sparsity at: 0.3020553916309013
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0478e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44370872 -1.1951019
  0.77284473]
Sparsity at: 0.3020553916309013
Epoch 387/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9981e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.44391263 -1.1949868
  0.77302736]
Sparsity at: 0.3020553916309013
Epoch 388/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0359e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.44407594 -1.194874
  0.7731918 ]
Sparsity at: 0.3020553916309013
Epoch 389/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0180e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44422042 -1.1947584
  0.77340823]
Sparsity at: 0.3020553916309013
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9782e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.4444093  -1.1946319
  0.7736053 ]
Sparsity at: 0.3020553916309013
Epoch 391/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0001e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.44458893 -1.1944842
  0.77379614]
Sparsity at: 0.3020553916309013
Epoch 392/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0239e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4447755  -1.194372
  0.77400494]
Sparsity at: 0.3020553916309013
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0617e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44496253 -1.1942661
  0.77418697]
Sparsity at: 0.3020553916309013
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9425e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44516352 -1.1941184
  0.7743932 ]
Sparsity at: 0.3020553916309013
Epoch 395/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9723e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44534633 -1.1939843
  0.7745614 ]
Sparsity at: 0.3020553916309013
Epoch 396/500
235/235 [==============================] - 2s 9ms/step - loss: 2.9445e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44553906 -1.1938394
  0.7747833 ]
Sparsity at: 0.3020553916309013
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 3.0001e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4457185  -1.193709
  0.7749944 ]
Sparsity at: 0.3020553916309013
Epoch 398/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9147e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44591537 -1.1935756
  0.77520996]
Sparsity at: 0.3020553916309013
Epoch 399/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9345e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44609883 -1.1934432
  0.7754135 ]
Sparsity at: 0.3020553916309013
Epoch 400/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9862e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44627932 -1.1932997
  0.77559716]
Sparsity at: 0.3020553916309013
Epoch 401/500
Wanted sparsity 0.989328
Upper percentile 0.6257745814666933
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.25362724
tf.Tensor(
[[1. 0. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 0. 1.]
 [0. 1. 0. ... 1. 1. 1.]
 ...
 [0. 1. 0. ... 1. 1. 1.]
 [1. 1. 0. ... 1. 1. 0.]
 [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 0.7970864249869294
Thresholhold 0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.645874
tf.Tensor(
[[1. 0. 0. ... 0. 1. 0.]
 [1. 0. 0. ... 0. 1. 1.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [1. 0. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 0. 1.]
 [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32)
Wanted sparsity 0.989328
Upper percentile 2.02338800751275
Thresholhold -0.0
Using suggest threshold.
Applying new mask
Percentage zeros 0.0
tf.Tensor(
[[1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 ...
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]
 [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32)
235/235 [==============================] - 43s 7ms/step - loss: 2.9643e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4464653  -1.1931634
  0.77580684]
Sparsity at: 0.3020553916309013
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 2.8749e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.44666585 -1.193022
  0.77600807]
Sparsity at: 0.3020553916309013
Epoch 403/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9763e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44685015 -1.1928682
  0.77620107]
Sparsity at: 0.3020553916309013
Epoch 404/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9306e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.44704214 -1.1927228
  0.7764128 ]
Sparsity at: 0.3020553916309013
Epoch 405/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9425e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44722834 -1.1925759
  0.7766009 ]
Sparsity at: 0.3020553916309013
Epoch 406/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9425e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.44739285 -1.1924493
  0.7768046 ]
Sparsity at: 0.3020553916309013
Epoch 407/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9107e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.447587   -1.1923018
  0.77698785]
Sparsity at: 0.3020553916309013
Epoch 408/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9286e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44777647 -1.1921719
  0.77718914]
Sparsity at: 0.3020553916309013
Epoch 409/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8948e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4479641  -1.192013
  0.7773983 ]
Sparsity at: 0.3020553916309013
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9087e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44814655 -1.1918768
  0.777601  ]
Sparsity at: 0.3020553916309013
Epoch 411/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8849e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44832662 -1.1917466
  0.7777895 ]
Sparsity at: 0.3020553916309013
Epoch 412/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8869e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44851652 -1.1915841
  0.77799135]
Sparsity at: 0.3020553916309013
Epoch 413/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8988e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4486965  -1.1914351
  0.7781718 ]
Sparsity at: 0.3020553916309013
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8491e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44886136 -1.1912953
  0.77836585]
Sparsity at: 0.3020553916309013
Epoch 415/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9047e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4490435  -1.1911824
  0.7785585 ]
Sparsity at: 0.3020553916309013
Epoch 416/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8968e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44922873 -1.1910223
  0.77873755]
Sparsity at: 0.3020553916309013
Epoch 417/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8372e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.44943118 -1.1908526
  0.7789373 ]
Sparsity at: 0.3020553916309013
Epoch 418/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8968e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4495905  -1.1906978
  0.7791303 ]
Sparsity at: 0.3020553916309013
Epoch 419/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9745
[ 0.02126332  0.          0.04348067 ...  0.44978005 -1.1905614
  0.7793366 ]
Sparsity at: 0.3020553916309013
Epoch 420/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8431e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.44995713 -1.1904019
  0.7795473 ]
Sparsity at: 0.3020553916309013
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8491e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45017117 -1.1902528
  0.77974164]
Sparsity at: 0.3020553916309013
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8233e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45034805 -1.190086
  0.7799482 ]
Sparsity at: 0.3020553916309013
Epoch 423/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8531e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45054942 -1.1899395
  0.78016704]
Sparsity at: 0.3020553916309013
Epoch 424/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8590e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45073333 -1.1897812
  0.78037673]
Sparsity at: 0.3020553916309013
Epoch 425/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45089406 -1.1896098
  0.7805784 ]
Sparsity at: 0.3020553916309013
Epoch 426/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7855e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45110574 -1.1894555
  0.7807969 ]
Sparsity at: 0.3020553916309013
Epoch 427/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8233e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45128655 -1.1893088
  0.780993  ]
Sparsity at: 0.3020553916309013
Epoch 428/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8551e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4514446  -1.1891822
  0.7811973 ]
Sparsity at: 0.3020553916309013
Epoch 429/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8173e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4516546  -1.1890148
  0.78139   ]
Sparsity at: 0.3020553916309013
Epoch 430/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8253e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.4518591  -1.188885
  0.78159463]
Sparsity at: 0.3020553916309013
Epoch 431/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8074e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45200548 -1.1887137
  0.78179383]
Sparsity at: 0.3020553916309013
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8094e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45221624 -1.1885562
  0.7819971 ]
Sparsity at: 0.3020553916309013
Epoch 433/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8213e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45239007 -1.1884062
  0.78220433]
Sparsity at: 0.3020553916309013
Epoch 434/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7875e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45258963 -1.1882571
  0.7824035 ]
Sparsity at: 0.3020553916309013
Epoch 435/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7895e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45276022 -1.1880857
  0.7825916 ]
Sparsity at: 0.3020553916309013
Epoch 436/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8332e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45294923 -1.187932
  0.7827864 ]
Sparsity at: 0.3020553916309013
Epoch 437/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8312e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4531503  -1.1877836
  0.78298897]
Sparsity at: 0.3020553916309013
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8054e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45332214 -1.1876085
  0.783185  ]
Sparsity at: 0.3020553916309013
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8372e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744
[ 0.02126332  0.          0.04348067 ...  0.45350477 -1.1874613
  0.7834121 ]
Sparsity at: 0.3020553916309013
Epoch 440/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8133e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45370334 -1.187304
  0.78361535]
Sparsity at: 0.3020553916309013
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7895e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45388106 -1.1871562
  0.783797  ]
Sparsity at: 0.3020553916309013
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7935e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45408076 -1.1870052
  0.7840216 ]
Sparsity at: 0.3020553916309013
Epoch 443/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45426437 -1.1868759
  0.78422976]
Sparsity at: 0.3020553916309013
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8094e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45445177 -1.1867199
  0.7844034 ]
Sparsity at: 0.3020553916309013
Epoch 445/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7935e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45464307 -1.1865838
  0.7845981 ]
Sparsity at: 0.3020553916309013
Epoch 446/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7796e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45481792 -1.1864139
  0.7848045 ]
Sparsity at: 0.3020553916309013
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7537e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4550326  -1.186258
  0.7850342 ]
Sparsity at: 0.3020553916309013
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45521167 -1.1861291
  0.7852498 ]
Sparsity at: 0.3020553916309013
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.45540008 -1.1859567
  0.7854605 ]
Sparsity at: 0.3020553916309013
Epoch 450/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8034e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45559126 -1.1857984
  0.78563416]
Sparsity at: 0.3020553916309013
Epoch 451/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7796e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.45577103 -1.1856498
  0.78586733]
Sparsity at: 0.3020553916309013
Epoch 452/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8014e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4559395  -1.1854857
  0.786054  ]
Sparsity at: 0.3020553916309013
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7259e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.4561282  -1.1853367
  0.78625625]
Sparsity at: 0.3020553916309013
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7458e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4563365  -1.1851479
  0.78644043]
Sparsity at: 0.3020553916309013
Epoch 455/500
235/235 [==============================] - 2s 9ms/step - loss: 2.7696e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.45655072 -1.1849878
  0.7866431 ]
Sparsity at: 0.3020553916309013
Epoch 456/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7577e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.45673206 -1.1848509
  0.78683716]
Sparsity at: 0.3020553916309013
Epoch 457/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7597e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.45693666 -1.184674
  0.7870373 ]
Sparsity at: 0.3020553916309013
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7815e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45710137 -1.1845218
  0.7872344 ]
Sparsity at: 0.3020553916309013
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7716e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4573101  -1.1843754
  0.78742903]
Sparsity at: 0.3020553916309013
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.4575042  -1.1842284
  0.7876205 ]
Sparsity at: 0.3020553916309013
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7657e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.45767817 -1.1840662
  0.78782666]
Sparsity at: 0.3020553916309013
Epoch 462/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7855e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.457872   -1.1839128
  0.78804356]
Sparsity at: 0.3020553916309013
Epoch 463/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.458048   -1.1837468
  0.7882357 ]
Sparsity at: 0.3020553916309013
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7815e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.4582476  -1.1836182
  0.78843224]
Sparsity at: 0.3020553916309013
Epoch 465/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45845062 -1.1834662
  0.7886283 ]
Sparsity at: 0.3020553916309013
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7696e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45864183 -1.1832778
  0.7888266 ]
Sparsity at: 0.3020553916309013
Epoch 467/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742
[ 0.02126332  0.          0.04348067 ...  0.4588146  -1.1831131
  0.7890437 ]
Sparsity at: 0.3020553916309013
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7577e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45900393 -1.182952
  0.78922045]
Sparsity at: 0.3020553916309013
Epoch 469/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7617e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45919523 -1.1827818
  0.7894301 ]
Sparsity at: 0.3020553916309013
Epoch 470/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4593799  -1.1826422
  0.78964746]
Sparsity at: 0.3020553916309013
Epoch 471/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7517e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45956725 -1.1824613
  0.78988355]
Sparsity at: 0.3020553916309013
Epoch 472/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45975965 -1.1822757
  0.7900747 ]
Sparsity at: 0.3020553916309013
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7458e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.45996156 -1.1821334
  0.79028213]
Sparsity at: 0.3020553916309013
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7478e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4601602  -1.1819777
  0.79047465]
Sparsity at: 0.3020553916309013
Epoch 475/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7378e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46035957 -1.1818355
  0.7906642 ]
Sparsity at: 0.3020553916309013
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7557e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46057132 -1.1816895
  0.79084545]
Sparsity at: 0.3020553916309013
Epoch 477/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6842e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46074736 -1.1815083
  0.79105663]
Sparsity at: 0.3020553916309013
Epoch 478/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7657e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46093693 -1.1813244
  0.7912409 ]
Sparsity at: 0.3020553916309013
Epoch 479/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46114072 -1.1811274
  0.7914706 ]
Sparsity at: 0.3020553916309013
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7498e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4613267  -1.1809651
  0.79168445]
Sparsity at: 0.3020553916309013
Epoch 481/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7041e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46152785 -1.1807996
  0.7918849 ]
Sparsity at: 0.3020553916309013
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6921e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4617256  -1.1806245
  0.79206854]
Sparsity at: 0.3020553916309013
Epoch 483/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7359e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46190038 -1.1804578
  0.7922776 ]
Sparsity at: 0.3020553916309013
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7478e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4620971  -1.180308
  0.7924722 ]
Sparsity at: 0.3020553916309013
Epoch 485/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46229157 -1.1801437
  0.79266745]
Sparsity at: 0.3020553916309013
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7259e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46247852 -1.1799914
  0.79288936]
Sparsity at: 0.3020553916309013
Epoch 487/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6981e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46268556 -1.179811
  0.7931087 ]
Sparsity at: 0.3020553916309013
Epoch 488/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7219e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4628794  -1.1796154
  0.7932919 ]
Sparsity at: 0.3020553916309013
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46305576 -1.1794428
  0.7934727 ]
Sparsity at: 0.3020553916309013
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7517e-09 - accuracy: 1.0000 - val_loss: 0.3002 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46326023 -1.1793092
  0.7936953 ]
Sparsity at: 0.3020553916309013
Epoch 491/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7021e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46345156 -1.1791304
  0.79389167]
Sparsity at: 0.3020553916309013
Epoch 492/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6862e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46365586 -1.1789601
  0.7940967 ]
Sparsity at: 0.3020553916309013
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7041e-09 - accuracy: 1.0000 - val_loss: 0.3002 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46385068 -1.1787962
  0.7943191 ]
Sparsity at: 0.3020553916309013
Epoch 494/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7378e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4640543  -1.1786457
  0.7944923 ]
Sparsity at: 0.3020553916309013
Epoch 495/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46425107 -1.1784854
  0.7946855 ]
Sparsity at: 0.3020553916309013
Epoch 496/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7498e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46444574 -1.1783222
  0.794889  ]
Sparsity at: 0.3020553916309013
Epoch 497/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7478e-09 - accuracy: 1.0000 - val_loss: 0.3002 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46466246 -1.178155
  0.7950975 ]
Sparsity at: 0.3020553916309013
Epoch 498/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6921e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46485028 -1.1779854
  0.7953271 ]
Sparsity at: 0.3020553916309013
Epoch 499/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7279e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.46504995 -1.1778473
  0.7954936 ]
Sparsity at: 0.3020553916309013
Epoch 500/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7160e-09 - accuracy: 1.0000 - val_loss: 0.3002 - val_accuracy: 0.9743
[ 0.02126332  0.          0.04348067 ...  0.4652491  -1.1776524
  0.79569346]
Sparsity at: 0.3020553916309013
Epoch 1/500
235/235 [==============================] - 5s 15ms/step - loss: 0.1401 - accuracy: 0.9780 - val_loss: 0.2065 - val_accuracy: 0.9596
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9785 - val_loss: 0.2130 - val_accuracy: 0.9584
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1356 - accuracy: 0.9796 - val_loss: 0.1976 - val_accuracy: 0.9622
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1380 - accuracy: 0.9793 - val_loss: 0.1975 - val_accuracy: 0.9633
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9791 - val_loss: 0.1915 - val_accuracy: 0.9636
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9793 - val_loss: 0.2113 - val_accuracy: 0.9592
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9799 - val_loss: 0.2494 - val_accuracy: 0.9497
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9788 - val_loss: 0.1922 - val_accuracy: 0.9637
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9802 - val_loss: 0.2207 - val_accuracy: 0.9556
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9801 - val_loss: 0.1977 - val_accuracy: 0.9638
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9793 - val_loss: 0.2159 - val_accuracy: 0.9591
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9797 - val_loss: 0.2132 - val_accuracy: 0.9583
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9794 - val_loss: 0.2097 - val_accuracy: 0.9583
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9793 - val_loss: 0.2139 - val_accuracy: 0.9579
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9791 - val_loss: 0.2554 - val_accuracy: 0.9466
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9797 - val_loss: 0.2394 - val_accuracy: 0.9506
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9796 - val_loss: 0.1958 - val_accuracy: 0.9636
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1418 - accuracy: 0.9783 - val_loss: 0.2530 - val_accuracy: 0.9462
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1398 - accuracy: 0.9787 - val_loss: 0.2078 - val_accuracy: 0.9609
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9804 - val_loss: 0.2216 - val_accuracy: 0.9576
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9797 - val_loss: 0.2464 - val_accuracy: 0.9487
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9800 - val_loss: 0.2180 - val_accuracy: 0.9562
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9787 - val_loss: 0.2392 - val_accuracy: 0.9517
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9795 - val_loss: 0.2144 - val_accuracy: 0.9574
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9785 - val_loss: 0.2745 - val_accuracy: 0.9417
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9792 - val_loss: 0.2215 - val_accuracy: 0.9546
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9793 - val_loss: 0.2714 - val_accuracy: 0.9444
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9782 - val_loss: 0.2698 - val_accuracy: 0.9430
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9796 - val_loss: 0.2208 - val_accuracy: 0.9583
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9793 - val_loss: 0.2264 - val_accuracy: 0.9555
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9799 - val_loss: 0.2182 - val_accuracy: 0.9584
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1393 - accuracy: 0.9797 - val_loss: 0.2449 - val_accuracy: 0.9502
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9793 - val_loss: 0.2364 - val_accuracy: 0.9529
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1384 - accuracy: 0.9793 - val_loss: 0.2251 - val_accuracy: 0.9546
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9793 - val_loss: 0.2399 - val_accuracy: 0.9527
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9797 - val_loss: 0.1995 - val_accuracy: 0.9622
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9790 - val_loss: 0.2337 - val_accuracy: 0.9521
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9794 - val_loss: 0.2439 - val_accuracy: 0.9523
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9786 - val_loss: 0.2179 - val_accuracy: 0.9557
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9798 - val_loss: 0.2103 - val_accuracy: 0.9605
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9798 - val_loss: 0.2033 - val_accuracy: 0.9623
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1400 - accuracy: 0.9783 - val_loss: 0.2246 - val_accuracy: 0.9561
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9801 - val_loss: 0.2241 - val_accuracy: 0.9556
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9786 - val_loss: 0.2306 - val_accuracy: 0.9541
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9789 - val_loss: 0.2016 - val_accuracy: 0.9626
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2437 - val_accuracy: 0.9520
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9791 - val_loss: 0.2127 - val_accuracy: 0.9582
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9797 - val_loss: 0.2123 - val_accuracy: 0.9608
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9794 - val_loss: 0.2293 - val_accuracy: 0.9537
[ 0.000000e+00  4.959844e-34  0.000000e+00 ...  0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9794 - val_loss: 0.2470 - val_accuracy: 0.9504
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9804 - val_loss: 0.2161 - val_accuracy: 0.9566
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9796 - val_loss: 0.2300 - val_accuracy: 0.9519
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9791 - val_loss: 0.2104 - val_accuracy: 0.9594
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9793 - val_loss: 0.2224 - val_accuracy: 0.9558
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9793 - val_loss: 0.2497 - val_accuracy: 0.9489
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9798 - val_loss: 0.2232 - val_accuracy: 0.9540
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9787 - val_loss: 0.2255 - val_accuracy: 0.9535
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9802 - val_loss: 0.2120 - val_accuracy: 0.9595
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9792 - val_loss: 0.1988 - val_accuracy: 0.9630
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9803 - val_loss: 0.1931 - val_accuracy: 0.9643
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9795 - val_loss: 0.2089 - val_accuracy: 0.9608
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9792 - val_loss: 0.2216 - val_accuracy: 0.9573
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9797 - val_loss: 0.1922 - val_accuracy: 0.9619
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9786 - val_loss: 0.1959 - val_accuracy: 0.9641
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9794 - val_loss: 0.2167 - val_accuracy: 0.9572
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9797 - val_loss: 0.2070 - val_accuracy: 0.9598
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9788 - val_loss: 0.2018 - val_accuracy: 0.9611
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9781 - val_loss: 0.2150 - val_accuracy: 0.9578
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9781 - val_loss: 0.1931 - val_accuracy: 0.9648
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9800 - val_loss: 0.2036 - val_accuracy: 0.9593
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9801 - val_loss: 0.1914 - val_accuracy: 0.9613
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9792 - val_loss: 0.1978 - val_accuracy: 0.9620
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9799 - val_loss: 0.2292 - val_accuracy: 0.9561
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9782 - val_loss: 0.2042 - val_accuracy: 0.9619
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9798 - val_loss: 0.2066 - val_accuracy: 0.9627
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9789 - val_loss: 0.2095 - val_accuracy: 0.9609
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9792 - val_loss: 0.1966 - val_accuracy: 0.9629
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9795 - val_loss: 0.2043 - val_accuracy: 0.9604
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9794 - val_loss: 0.2204 - val_accuracy: 0.9547
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9791 - val_loss: 0.2195 - val_accuracy: 0.9555
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1356 - accuracy: 0.9797 - val_loss: 0.2028 - val_accuracy: 0.9622
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9798 - val_loss: 0.2102 - val_accuracy: 0.9597
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9793 - val_loss: 0.2086 - val_accuracy: 0.9610
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9781 - val_loss: 0.2051 - val_accuracy: 0.9619
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9797 - val_loss: 0.2299 - val_accuracy: 0.9525
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9782 - val_loss: 0.2004 - val_accuracy: 0.9626
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.2376 - val_accuracy: 0.9507
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9801 - val_loss: 0.2145 - val_accuracy: 0.9584
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9798 - val_loss: 0.2123 - val_accuracy: 0.9574
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9794 - val_loss: 0.2600 - val_accuracy: 0.9426
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9788 - val_loss: 0.2072 - val_accuracy: 0.9588
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9793 - val_loss: 0.2264 - val_accuracy: 0.9547
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1323 - accuracy: 0.9800 - val_loss: 0.2054 - val_accuracy: 0.9594
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9785 - val_loss: 0.2392 - val_accuracy: 0.9537
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9793 - val_loss: 0.2156 - val_accuracy: 0.9580
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9798 - val_loss: 0.2018 - val_accuracy: 0.9596
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.1876 - val_accuracy: 0.9674
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9790 - val_loss: 0.2342 - val_accuracy: 0.9520
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1383 - accuracy: 0.9786 - val_loss: 0.2103 - val_accuracy: 0.9579
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9801 - val_loss: 0.1970 - val_accuracy: 0.9637
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9780 - val_loss: 0.2047 - val_accuracy: 0.9604
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9798 - val_loss: 0.1923 - val_accuracy: 0.9626
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.2094 - val_accuracy: 0.9596
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1348 - accuracy: 0.9797 - val_loss: 0.1940 - val_accuracy: 0.9636
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9782 - val_loss: 0.2070 - val_accuracy: 0.9599
[ 0.000000e+00  4.959844e-34  0.000000e+00 ...  0.000000e+00  0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9783 - val_loss: 0.2219 - val_accuracy: 0.9546
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9796 - val_loss: 0.2048 - val_accuracy: 0.9588
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9787 - val_loss: 0.1980 - val_accuracy: 0.9626
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9789 - val_loss: 0.1999 - val_accuracy: 0.9648
[ 0.000000e+00  4.959844e-34  0.000000e+00 ...  0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9787 - val_loss: 0.1869 - val_accuracy: 0.9659
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9789 - val_loss: 0.2116 - val_accuracy: 0.9601
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9800 - val_loss: 0.2233 - val_accuracy: 0.9568
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9790 - val_loss: 0.1947 - val_accuracy: 0.9631
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9796 - val_loss: 0.2144 - val_accuracy: 0.9574
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9784 - val_loss: 0.2298 - val_accuracy: 0.9541
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9792 - val_loss: 0.1916 - val_accuracy: 0.9640
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9788 - val_loss: 0.2359 - val_accuracy: 0.9528
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9790 - val_loss: 0.1998 - val_accuracy: 0.9639
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9794 - val_loss: 0.2064 - val_accuracy: 0.9605
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9802 - val_loss: 0.2034 - val_accuracy: 0.9593
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9792 - val_loss: 0.1834 - val_accuracy: 0.9661
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9793 - val_loss: 0.2260 - val_accuracy: 0.9523
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9785 - val_loss: 0.2308 - val_accuracy: 0.9518
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9798 - val_loss: 0.1985 - val_accuracy: 0.9628
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9796 - val_loss: 0.2246 - val_accuracy: 0.9524
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2023 - val_accuracy: 0.9607
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9790 - val_loss: 0.1964 - val_accuracy: 0.9637
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9795 - val_loss: 0.2175 - val_accuracy: 0.9575
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9791 - val_loss: 0.2318 - val_accuracy: 0.9531
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9785 - val_loss: 0.2077 - val_accuracy: 0.9608
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.2569 - val_accuracy: 0.9448
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.2332 - val_accuracy: 0.9513
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9787 - val_loss: 0.2629 - val_accuracy: 0.9458
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9797 - val_loss: 0.2189 - val_accuracy: 0.9568
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9791 - val_loss: 0.2435 - val_accuracy: 0.9494
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9794 - val_loss: 0.2161 - val_accuracy: 0.9576
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.2385 - val_accuracy: 0.9519
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9792 - val_loss: 0.2290 - val_accuracy: 0.9530
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9792 - val_loss: 0.2236 - val_accuracy: 0.9549
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9791 - val_loss: 0.1935 - val_accuracy: 0.9651
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9778 - val_loss: 0.3444 - val_accuracy: 0.9281
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00  0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1359 - accuracy: 0.9797 - val_loss: 0.1938 - val_accuracy: 0.9625
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9790 - val_loss: 0.1955 - val_accuracy: 0.9629
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9792 - val_loss: 0.1988 - val_accuracy: 0.9599
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9794 - val_loss: 0.1867 - val_accuracy: 0.9651
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9797 - val_loss: 0.2083 - val_accuracy: 0.9599
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
 -0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9789 - val_loss: 0.2351 - val_accuracy: 0.9510
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9805 - val_loss: 0.2125 - val_accuracy: 0.9552
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9795 - val_loss: 0.2413 - val_accuracy: 0.9498
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9783 - val_loss: 0.2332 - val_accuracy: 0.9507
[ 0.000000e+00  4.959844e-34  0.000000e+00 ... -0.000000e+00 -0.000000e+00
  0.000000e+00]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9778 - val_loss: 0.2033 - val_accuracy: 0.9611
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9784 - val_loss: 0.1820 - val_accuracy: 0.9664
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9794 - val_loss: 0.2079 - val_accuracy: 0.9579
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9793 - val_loss: 0.1837 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9793 - val_loss: 0.1971 - val_accuracy: 0.9615
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9773 - val_loss: 0.2044 - val_accuracy: 0.9606
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9786 - val_loss: 0.2568 - val_accuracy: 0.9445
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9782 - val_loss: 0.2040 - val_accuracy: 0.9601
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9793 - val_loss: 0.2419 - val_accuracy: 0.9506
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9793 - val_loss: 0.2334 - val_accuracy: 0.9533
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9785 - val_loss: 0.2011 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9782 - val_loss: 0.2106 - val_accuracy: 0.9573
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9796 - val_loss: 0.2322 - val_accuracy: 0.9518
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9786 - val_loss: 0.2090 - val_accuracy: 0.9588
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9789 - val_loss: 0.1974 - val_accuracy: 0.9623
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9793 - val_loss: 0.2428 - val_accuracy: 0.9511
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9789 - val_loss: 0.2391 - val_accuracy: 0.9505
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9798 - val_loss: 0.2189 - val_accuracy: 0.9557
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9785 - val_loss: 0.1919 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9790 - val_loss: 0.2303 - val_accuracy: 0.9514
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9790 - val_loss: 0.2127 - val_accuracy: 0.9578
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9785 - val_loss: 0.2441 - val_accuracy: 0.9494
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9790 - val_loss: 0.2100 - val_accuracy: 0.9581
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9790 - val_loss: 0.2044 - val_accuracy: 0.9600
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9790 - val_loss: 0.2156 - val_accuracy: 0.9567
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9787 - val_loss: 0.1957 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9790 - val_loss: 0.2121 - val_accuracy: 0.9591
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9787 - val_loss: 0.1967 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2129 - val_accuracy: 0.9587
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9787 - val_loss: 0.1845 - val_accuracy: 0.9661
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9791 - val_loss: 0.2376 - val_accuracy: 0.9520
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.2191 - val_accuracy: 0.9576
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1362 - accuracy: 0.9795 - val_loss: 0.2074 - val_accuracy: 0.9608
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9782 - val_loss: 0.2007 - val_accuracy: 0.9612
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1322 - accuracy: 0.9799 - val_loss: 0.2006 - val_accuracy: 0.9604
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2475 - val_accuracy: 0.9514
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9791 - val_loss: 0.2229 - val_accuracy: 0.9560
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9781 - val_loss: 0.2093 - val_accuracy: 0.9578
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9795 - val_loss: 0.2842 - val_accuracy: 0.9390
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9790 - val_loss: 0.2087 - val_accuracy: 0.9588
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9787 - val_loss: 0.1854 - val_accuracy: 0.9650
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2111 - val_accuracy: 0.9574
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.1872 - val_accuracy: 0.9645
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9785 - val_loss: 0.1969 - val_accuracy: 0.9617
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9788 - val_loss: 0.2143 - val_accuracy: 0.9571
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9794 - val_loss: 0.2162 - val_accuracy: 0.9572
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9788 - val_loss: 0.1886 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9791 - val_loss: 0.1936 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9779 - val_loss: 0.1982 - val_accuracy: 0.9613
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9794 - val_loss: 0.2208 - val_accuracy: 0.9565
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9769 - val_loss: 0.1969 - val_accuracy: 0.9625
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1407 - accuracy: 0.9767 - val_loss: 0.2018 - val_accuracy: 0.9617
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9785 - val_loss: 0.2265 - val_accuracy: 0.9529
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9785 - val_loss: 0.2078 - val_accuracy: 0.9599
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9778 - val_loss: 0.2362 - val_accuracy: 0.9514
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9792 - val_loss: 0.1896 - val_accuracy: 0.9618
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9779 - val_loss: 0.2511 - val_accuracy: 0.9485
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.2220 - val_accuracy: 0.9557
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9794 - val_loss: 0.2070 - val_accuracy: 0.9600
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9773 - val_loss: 0.2001 - val_accuracy: 0.9606
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9789 - val_loss: 0.2279 - val_accuracy: 0.9506
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1400 - accuracy: 0.9776 - val_loss: 0.2071 - val_accuracy: 0.9609
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9788 - val_loss: 0.1930 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9778 - val_loss: 0.2154 - val_accuracy: 0.9580
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1404 - accuracy: 0.9778 - val_loss: 0.2028 - val_accuracy: 0.9597
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1375 - accuracy: 0.9784 - val_loss: 0.1996 - val_accuracy: 0.9620
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9791 - val_loss: 0.2119 - val_accuracy: 0.9580
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9767 - val_loss: 0.1901 - val_accuracy: 0.9661
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9788 - val_loss: 0.1973 - val_accuracy: 0.9606
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9789 - val_loss: 0.2200 - val_accuracy: 0.9521
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9790 - val_loss: 0.2093 - val_accuracy: 0.9591
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9779 - val_loss: 0.1948 - val_accuracy: 0.9625
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1389 - accuracy: 0.9784 - val_loss: 0.1943 - val_accuracy: 0.9621
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9783 - val_loss: 0.2476 - val_accuracy: 0.9496
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1394 - accuracy: 0.9773 - val_loss: 0.1763 - val_accuracy: 0.9697
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9786 - val_loss: 0.2130 - val_accuracy: 0.9570
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9796 - val_loss: 0.2019 - val_accuracy: 0.9624
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9798 - val_loss: 0.2137 - val_accuracy: 0.9577
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.2088 - val_accuracy: 0.9592
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9767 - val_loss: 0.2022 - val_accuracy: 0.9635
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9796 - val_loss: 0.1891 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9783 - val_loss: 0.1870 - val_accuracy: 0.9663
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9790 - val_loss: 0.2036 - val_accuracy: 0.9604
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9788 - val_loss: 0.2638 - val_accuracy: 0.9439
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1412 - accuracy: 0.9773 - val_loss: 0.2368 - val_accuracy: 0.9524
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9788 - val_loss: 0.2061 - val_accuracy: 0.9591
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9791 - val_loss: 0.1954 - val_accuracy: 0.9599
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9782 - val_loss: 0.2340 - val_accuracy: 0.9505
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9786 - val_loss: 0.2044 - val_accuracy: 0.9600
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.2382 - val_accuracy: 0.9500
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9797 - val_loss: 0.2104 - val_accuracy: 0.9573
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9785 - val_loss: 0.2074 - val_accuracy: 0.9620
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9779 - val_loss: 0.1875 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9804 - val_loss: 0.2207 - val_accuracy: 0.9550
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9782 - val_loss: 0.2341 - val_accuracy: 0.9523
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9785 - val_loss: 0.2124 - val_accuracy: 0.9580
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9800 - val_loss: 0.2386 - val_accuracy: 0.9488
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.2044 - val_accuracy: 0.9617
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9782 - val_loss: 0.1935 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9789 - val_loss: 0.2173 - val_accuracy: 0.9581
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9780 - val_loss: 0.1939 - val_accuracy: 0.9616
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9785 - val_loss: 0.1725 - val_accuracy: 0.9660
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9788 - val_loss: 0.1798 - val_accuracy: 0.9664
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9801 - val_loss: 0.1709 - val_accuracy: 0.9671
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9800 - val_loss: 0.1719 - val_accuracy: 0.9689
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9791 - val_loss: 0.1871 - val_accuracy: 0.9637
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9799 - val_loss: 0.1820 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9804 - val_loss: 0.1782 - val_accuracy: 0.9657
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9801 - val_loss: 0.1816 - val_accuracy: 0.9623
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9801 - val_loss: 0.1778 - val_accuracy: 0.9653
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9804 - val_loss: 0.1733 - val_accuracy: 0.9670
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1247 - accuracy: 0.9793 - val_loss: 0.1760 - val_accuracy: 0.9679
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9803 - val_loss: 0.1821 - val_accuracy: 0.9658
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9801 - val_loss: 0.1779 - val_accuracy: 0.9657
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9805 - val_loss: 0.1819 - val_accuracy: 0.9644
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9801 - val_loss: 0.1679 - val_accuracy: 0.9692
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9804 - val_loss: 0.1812 - val_accuracy: 0.9655
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9800 - val_loss: 0.1694 - val_accuracy: 0.9674
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9805 - val_loss: 0.1739 - val_accuracy: 0.9658
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9806 - val_loss: 0.1581 - val_accuracy: 0.9710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9807 - val_loss: 0.1852 - val_accuracy: 0.9626
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9804 - val_loss: 0.2037 - val_accuracy: 0.9583
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1202 - accuracy: 0.9808 - val_loss: 0.1651 - val_accuracy: 0.9674
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1216 - accuracy: 0.9800 - val_loss: 0.1879 - val_accuracy: 0.9632
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9810 - val_loss: 0.1838 - val_accuracy: 0.9633
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9803 - val_loss: 0.1777 - val_accuracy: 0.9650
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1214 - accuracy: 0.9805 - val_loss: 0.1659 - val_accuracy: 0.9694
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9813 - val_loss: 0.1635 - val_accuracy: 0.9684
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9808 - val_loss: 0.1681 - val_accuracy: 0.9676
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1153 - accuracy: 0.9818 - val_loss: 0.2425 - val_accuracy: 0.9465
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9796 - val_loss: 0.1737 - val_accuracy: 0.9647
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9807 - val_loss: 0.1838 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9801 - val_loss: 0.1758 - val_accuracy: 0.9667
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9804 - val_loss: 0.1853 - val_accuracy: 0.9624
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9805 - val_loss: 0.1799 - val_accuracy: 0.9659
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9803 - val_loss: 0.1801 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9808 - val_loss: 0.1619 - val_accuracy: 0.9703
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9802 - val_loss: 0.1763 - val_accuracy: 0.9652
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9805 - val_loss: 0.1697 - val_accuracy: 0.9673
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9813 - val_loss: 0.2071 - val_accuracy: 0.9555
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9804 - val_loss: 0.1685 - val_accuracy: 0.9679
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9805 - val_loss: 0.1801 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9809 - val_loss: 0.1789 - val_accuracy: 0.9646
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9806 - val_loss: 0.1822 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9811 - val_loss: 0.1761 - val_accuracy: 0.9665
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9808 - val_loss: 0.1708 - val_accuracy: 0.9675
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9808 - val_loss: 0.1753 - val_accuracy: 0.9659
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9802 - val_loss: 0.1815 - val_accuracy: 0.9640
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9816 - val_loss: 0.1867 - val_accuracy: 0.9637
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9809 - val_loss: 0.1794 - val_accuracy: 0.9641
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9745 - val_loss: 0.1540 - val_accuracy: 0.9689
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9790 - val_loss: 0.1534 - val_accuracy: 0.9697
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1151 - accuracy: 0.9792 - val_loss: 0.1782 - val_accuracy: 0.9629
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1138 - accuracy: 0.9793 - val_loss: 0.1852 - val_accuracy: 0.9591
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1132 - accuracy: 0.9797 - val_loss: 0.1686 - val_accuracy: 0.9685
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1126 - accuracy: 0.9792 - val_loss: 0.1461 - val_accuracy: 0.9703
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1102 - accuracy: 0.9803 - val_loss: 0.1645 - val_accuracy: 0.9665
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1102 - accuracy: 0.9799 - val_loss: 0.1560 - val_accuracy: 0.9707
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1115 - accuracy: 0.9798 - val_loss: 0.1775 - val_accuracy: 0.9622
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1088 - accuracy: 0.9802 - val_loss: 0.1519 - val_accuracy: 0.9706
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1081 - accuracy: 0.9811 - val_loss: 0.1722 - val_accuracy: 0.9642
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1060 - accuracy: 0.9808 - val_loss: 0.1633 - val_accuracy: 0.9676
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1076 - accuracy: 0.9811 - val_loss: 0.1658 - val_accuracy: 0.9666
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1086 - accuracy: 0.9801 - val_loss: 0.1597 - val_accuracy: 0.9686
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9818 - val_loss: 0.1711 - val_accuracy: 0.9649
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1050 - accuracy: 0.9811 - val_loss: 0.1659 - val_accuracy: 0.9652
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1072 - accuracy: 0.9808 - val_loss: 0.1723 - val_accuracy: 0.9636
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1055 - accuracy: 0.9812 - val_loss: 0.1535 - val_accuracy: 0.9683
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1029 - accuracy: 0.9820 - val_loss: 0.1666 - val_accuracy: 0.9663
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1039 - accuracy: 0.9815 - val_loss: 0.1738 - val_accuracy: 0.9630
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1074 - accuracy: 0.9805 - val_loss: 0.1649 - val_accuracy: 0.9664
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9815 - val_loss: 0.1573 - val_accuracy: 0.9693
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1041 - accuracy: 0.9816 - val_loss: 0.1700 - val_accuracy: 0.9651
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9811 - val_loss: 0.1654 - val_accuracy: 0.9675
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9816 - val_loss: 0.1709 - val_accuracy: 0.9636
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1050 - accuracy: 0.9813 - val_loss: 0.1545 - val_accuracy: 0.9712
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9819 - val_loss: 0.1776 - val_accuracy: 0.9638
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1058 - accuracy: 0.9811 - val_loss: 0.1567 - val_accuracy: 0.9670
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9817 - val_loss: 0.1644 - val_accuracy: 0.9660
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1030 - accuracy: 0.9821 - val_loss: 0.1997 - val_accuracy: 0.9577
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1071 - accuracy: 0.9804 - val_loss: 0.1656 - val_accuracy: 0.9674
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9811 - val_loss: 0.1561 - val_accuracy: 0.9692
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9811 - val_loss: 0.1532 - val_accuracy: 0.9693
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1058 - accuracy: 0.9804 - val_loss: 0.1733 - val_accuracy: 0.9659
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1030 - accuracy: 0.9817 - val_loss: 0.1485 - val_accuracy: 0.9703
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1041 - accuracy: 0.9807 - val_loss: 0.1556 - val_accuracy: 0.9672
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9814 - val_loss: 0.1767 - val_accuracy: 0.9628
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1032 - accuracy: 0.9817 - val_loss: 0.1708 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1035 - accuracy: 0.9814 - val_loss: 0.1540 - val_accuracy: 0.9695
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1039 - accuracy: 0.9813 - val_loss: 0.1619 - val_accuracy: 0.9672
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9814 - val_loss: 0.1623 - val_accuracy: 0.9658
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1043 - accuracy: 0.9812 - val_loss: 0.1593 - val_accuracy: 0.9677
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9812 - val_loss: 0.1680 - val_accuracy: 0.9667
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9811 - val_loss: 0.1674 - val_accuracy: 0.9654
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1030 - accuracy: 0.9821 - val_loss: 0.1710 - val_accuracy: 0.9648
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1043 - accuracy: 0.9813 - val_loss: 0.1798 - val_accuracy: 0.9626
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1041 - accuracy: 0.9815 - val_loss: 0.1604 - val_accuracy: 0.9673
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1036 - accuracy: 0.9811 - val_loss: 0.1685 - val_accuracy: 0.9649
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1036 - accuracy: 0.9817 - val_loss: 0.1583 - val_accuracy: 0.9683
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1053 - accuracy: 0.9813 - val_loss: 0.1680 - val_accuracy: 0.9652
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 3s 14ms/step - loss: 0.3376 - accuracy: 0.8644 - val_loss: 0.3164 - val_accuracy: 0.8685
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2828 - accuracy: 0.8747 - val_loss: 0.3200 - val_accuracy: 0.8629
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2746 - accuracy: 0.8759 - val_loss: 0.3120 - val_accuracy: 0.8654
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2700 - accuracy: 0.8768 - val_loss: 0.3056 - val_accuracy: 0.8670
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2667 - accuracy: 0.8778 - val_loss: 0.3121 - val_accuracy: 0.8655
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2647 - accuracy: 0.8778 - val_loss: 0.2978 - val_accuracy: 0.8738
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2610 - accuracy: 0.8791 - val_loss: 0.3033 - val_accuracy: 0.8714
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2617 - accuracy: 0.8791 - val_loss: 0.3104 - val_accuracy: 0.8706
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2587 - accuracy: 0.8795 - val_loss: 0.3041 - val_accuracy: 0.8710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2592 - accuracy: 0.8792 - val_loss: 0.3041 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2577 - accuracy: 0.8797 - val_loss: 0.3016 - val_accuracy: 0.8721
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2567 - accuracy: 0.8802 - val_loss: 0.3031 - val_accuracy: 0.8715
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2569 - accuracy: 0.8793 - val_loss: 0.2991 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2552 - accuracy: 0.8804 - val_loss: 0.3053 - val_accuracy: 0.8703
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2555 - accuracy: 0.8801 - val_loss: 0.3050 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2542 - accuracy: 0.8801 - val_loss: 0.3020 - val_accuracy: 0.8710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2561 - accuracy: 0.8793 - val_loss: 0.3124 - val_accuracy: 0.8717
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2552 - accuracy: 0.8803 - val_loss: 0.3100 - val_accuracy: 0.8700
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2532 - accuracy: 0.8810 - val_loss: 0.3040 - val_accuracy: 0.8719
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2539 - accuracy: 0.8801 - val_loss: 0.2981 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2544 - accuracy: 0.8806 - val_loss: 0.3034 - val_accuracy: 0.8708
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2534 - accuracy: 0.8805 - val_loss: 0.3048 - val_accuracy: 0.8715
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2550 - accuracy: 0.8794 - val_loss: 0.3017 - val_accuracy: 0.8723
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2535 - accuracy: 0.8808 - val_loss: 0.3022 - val_accuracy: 0.8719
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2544 - accuracy: 0.8802 - val_loss: 0.2998 - val_accuracy: 0.8724
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2544 - accuracy: 0.8797 - val_loss: 0.3079 - val_accuracy: 0.8707
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2538 - accuracy: 0.8804 - val_loss: 0.3048 - val_accuracy: 0.8701
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2537 - accuracy: 0.8804 - val_loss: 0.3034 - val_accuracy: 0.8713
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2532 - accuracy: 0.8806 - val_loss: 0.3079 - val_accuracy: 0.8725
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2531 - accuracy: 0.8811 - val_loss: 0.3145 - val_accuracy: 0.8689
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2529 - accuracy: 0.8802 - val_loss: 0.3071 - val_accuracy: 0.8701
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2540 - accuracy: 0.8804 - val_loss: 0.3083 - val_accuracy: 0.8721
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2521 - accuracy: 0.8801 - val_loss: 0.3104 - val_accuracy: 0.8702
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2521 - accuracy: 0.8805 - val_loss: 0.3056 - val_accuracy: 0.8710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2534 - accuracy: 0.8805 - val_loss: 0.3098 - val_accuracy: 0.8717
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2530 - accuracy: 0.8808 - val_loss: 0.3090 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2529 - accuracy: 0.8808 - val_loss: 0.3078 - val_accuracy: 0.8727
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2521 - accuracy: 0.8808 - val_loss: 0.2986 - val_accuracy: 0.8735
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2530 - accuracy: 0.8809 - val_loss: 0.3011 - val_accuracy: 0.8723
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2517 - accuracy: 0.8810 - val_loss: 0.3145 - val_accuracy: 0.8698
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2528 - accuracy: 0.8804 - val_loss: 0.3103 - val_accuracy: 0.8729
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2549 - accuracy: 0.8799 - val_loss: 0.3111 - val_accuracy: 0.8701
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8805 - val_loss: 0.3066 - val_accuracy: 0.8711
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2526 - accuracy: 0.8803 - val_loss: 0.2999 - val_accuracy: 0.8736
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2506 - accuracy: 0.8811 - val_loss: 0.2999 - val_accuracy: 0.8713
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2526 - accuracy: 0.8806 - val_loss: 0.3041 - val_accuracy: 0.8715
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2515 - accuracy: 0.8814 - val_loss: 0.3027 - val_accuracy: 0.8736
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2517 - accuracy: 0.8807 - val_loss: 0.3189 - val_accuracy: 0.8694
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2543 - accuracy: 0.8804 - val_loss: 0.3046 - val_accuracy: 0.8724
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2523 - accuracy: 0.8807 - val_loss: 0.3055 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2708 - accuracy: 0.8750 - val_loss: 0.3093 - val_accuracy: 0.8690
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2594 - accuracy: 0.8777 - val_loss: 0.2958 - val_accuracy: 0.8710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2578 - accuracy: 0.8785 - val_loss: 0.3025 - val_accuracy: 0.8690
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2576 - accuracy: 0.8785 - val_loss: 0.2971 - val_accuracy: 0.8700
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2547 - accuracy: 0.8786 - val_loss: 0.3055 - val_accuracy: 0.8698
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2564 - accuracy: 0.8784 - val_loss: 0.3059 - val_accuracy: 0.8692
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2561 - accuracy: 0.8783 - val_loss: 0.2958 - val_accuracy: 0.8721
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2539 - accuracy: 0.8793 - val_loss: 0.2962 - val_accuracy: 0.8723
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2536 - accuracy: 0.8790 - val_loss: 0.2947 - val_accuracy: 0.8720
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2545 - accuracy: 0.8786 - val_loss: 0.2905 - val_accuracy: 0.8729
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 4s 16ms/step - loss: 0.2542 - accuracy: 0.8784 - val_loss: 0.2963 - val_accuracy: 0.8722
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2535 - accuracy: 0.8792 - val_loss: 0.2910 - val_accuracy: 0.8725
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2529 - accuracy: 0.8791 - val_loss: 0.2960 - val_accuracy: 0.8713
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2519 - accuracy: 0.8793 - val_loss: 0.2981 - val_accuracy: 0.8717
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2533 - accuracy: 0.8793 - val_loss: 0.2949 - val_accuracy: 0.8726
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2533 - accuracy: 0.8789 - val_loss: 0.2991 - val_accuracy: 0.8715
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2525 - accuracy: 0.8788 - val_loss: 0.2980 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8795 - val_loss: 0.2986 - val_accuracy: 0.8722
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2520 - accuracy: 0.8798 - val_loss: 0.2928 - val_accuracy: 0.8726
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2521 - accuracy: 0.8794 - val_loss: 0.2978 - val_accuracy: 0.8714
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2527 - accuracy: 0.8796 - val_loss: 0.2951 - val_accuracy: 0.8720
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 4s 15ms/step - loss: 0.2520 - accuracy: 0.8796 - val_loss: 0.2864 - val_accuracy: 0.8740
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2518 - accuracy: 0.8793 - val_loss: 0.3002 - val_accuracy: 0.8713
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8792 - val_loss: 0.2924 - val_accuracy: 0.8715
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2523 - accuracy: 0.8793 - val_loss: 0.2976 - val_accuracy: 0.8712
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2512 - accuracy: 0.8799 - val_loss: 0.2936 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2521 - accuracy: 0.8795 - val_loss: 0.2920 - val_accuracy: 0.8735
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2523 - accuracy: 0.8794 - val_loss: 0.2919 - val_accuracy: 0.8724
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2524 - accuracy: 0.8787 - val_loss: 0.2948 - val_accuracy: 0.8717
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2513 - accuracy: 0.8789 - val_loss: 0.2894 - val_accuracy: 0.8722
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2526 - accuracy: 0.8793 - val_loss: 0.2949 - val_accuracy: 0.8722
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 4s 15ms/step - loss: 0.2522 - accuracy: 0.8789 - val_loss: 0.2901 - val_accuracy: 0.8725
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 12ms/step - loss: 0.2519 - accuracy: 0.8794 - val_loss: 0.2949 - val_accuracy: 0.8722
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2516 - accuracy: 0.8796 - val_loss: 0.2925 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2526 - accuracy: 0.8789 - val_loss: 0.2914 - val_accuracy: 0.8719
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8792 - val_loss: 0.2940 - val_accuracy: 0.8735
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2515 - accuracy: 0.8793 - val_loss: 0.2921 - val_accuracy: 0.8731
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2524 - accuracy: 0.8794 - val_loss: 0.2941 - val_accuracy: 0.8740
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 4s 15ms/step - loss: 0.2525 - accuracy: 0.8793 - val_loss: 0.2941 - val_accuracy: 0.8719
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2517 - accuracy: 0.8795 - val_loss: 0.2932 - val_accuracy: 0.8721
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2507 - accuracy: 0.8796 - val_loss: 0.2983 - val_accuracy: 0.8721
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8797 - val_loss: 0.2927 - val_accuracy: 0.8729
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2521 - accuracy: 0.8796 - val_loss: 0.2931 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2509 - accuracy: 0.8794 - val_loss: 0.2922 - val_accuracy: 0.8728
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2507 - accuracy: 0.8800 - val_loss: 0.2963 - val_accuracy: 0.8732
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8792 - val_loss: 0.2939 - val_accuracy: 0.8721
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8790 - val_loss: 0.2887 - val_accuracy: 0.8731
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2508 - accuracy: 0.8796 - val_loss: 0.2912 - val_accuracy: 0.8725
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2506 - accuracy: 0.8796 - val_loss: 0.2923 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8791 - val_loss: 0.2934 - val_accuracy: 0.8724
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2505 - accuracy: 0.8793 - val_loss: 0.2923 - val_accuracy: 0.8724
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8798 - val_loss: 0.2969 - val_accuracy: 0.8710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2520 - accuracy: 0.8787 - val_loss: 0.2963 - val_accuracy: 0.8729
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2498 - accuracy: 0.8797 - val_loss: 0.2954 - val_accuracy: 0.8723
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2523 - accuracy: 0.8792 - val_loss: 0.2935 - val_accuracy: 0.8728
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2515 - accuracy: 0.8794 - val_loss: 0.2900 - val_accuracy: 0.8731
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8794 - val_loss: 0.2913 - val_accuracy: 0.8734
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2515 - accuracy: 0.8790 - val_loss: 0.2904 - val_accuracy: 0.8724
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8791 - val_loss: 0.2935 - val_accuracy: 0.8733
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2496 - accuracy: 0.8799 - val_loss: 0.3006 - val_accuracy: 0.8712
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8792 - val_loss: 0.2954 - val_accuracy: 0.8719
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2500 - accuracy: 0.8792 - val_loss: 0.2959 - val_accuracy: 0.8728
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2526 - accuracy: 0.8789 - val_loss: 0.2982 - val_accuracy: 0.8711
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2509 - accuracy: 0.8797 - val_loss: 0.2934 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2514 - accuracy: 0.8796 - val_loss: 0.2993 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2516 - accuracy: 0.8796 - val_loss: 0.2956 - val_accuracy: 0.8711
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2508 - accuracy: 0.8796 - val_loss: 0.2956 - val_accuracy: 0.8722
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8794 - val_loss: 0.3064 - val_accuracy: 0.8701
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 4s 15ms/step - loss: 0.2513 - accuracy: 0.8792 - val_loss: 0.2921 - val_accuracy: 0.8720
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2507 - accuracy: 0.8798 - val_loss: 0.2969 - val_accuracy: 0.8722
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8787 - val_loss: 0.2913 - val_accuracy: 0.8730
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2507 - accuracy: 0.8794 - val_loss: 0.2924 - val_accuracy: 0.8728
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2509 - accuracy: 0.8793 - val_loss: 0.2951 - val_accuracy: 0.8730
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2500 - accuracy: 0.8793 - val_loss: 0.2927 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2513 - accuracy: 0.8790 - val_loss: 0.2940 - val_accuracy: 0.8720
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2501 - accuracy: 0.8794 - val_loss: 0.2989 - val_accuracy: 0.8704
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2502 - accuracy: 0.8798 - val_loss: 0.2937 - val_accuracy: 0.8717
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2506 - accuracy: 0.8790 - val_loss: 0.2961 - val_accuracy: 0.8721
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2512 - accuracy: 0.8796 - val_loss: 0.2953 - val_accuracy: 0.8713
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2513 - accuracy: 0.8797 - val_loss: 0.2959 - val_accuracy: 0.8717
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2503 - accuracy: 0.8800 - val_loss: 0.2932 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8792 - val_loss: 0.2963 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8792 - val_loss: 0.2930 - val_accuracy: 0.8721
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2505 - accuracy: 0.8796 - val_loss: 0.2981 - val_accuracy: 0.8725
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8797 - val_loss: 0.2926 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2512 - accuracy: 0.8791 - val_loss: 0.2925 - val_accuracy: 0.8724
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2502 - accuracy: 0.8794 - val_loss: 0.2947 - val_accuracy: 0.8727
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2500 - accuracy: 0.8799 - val_loss: 0.2939 - val_accuracy: 0.8718
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2505 - accuracy: 0.8795 - val_loss: 0.2905 - val_accuracy: 0.8724
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2491 - accuracy: 0.8795 - val_loss: 0.2951 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2509 - accuracy: 0.8790 - val_loss: 0.2999 - val_accuracy: 0.8713
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2506 - accuracy: 0.8793 - val_loss: 0.2997 - val_accuracy: 0.8705
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2512 - accuracy: 0.8790 - val_loss: 0.2913 - val_accuracy: 0.8722
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8793 - val_loss: 0.2917 - val_accuracy: 0.8733
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2498 - accuracy: 0.8794 - val_loss: 0.2888 - val_accuracy: 0.8727
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2503 - accuracy: 0.8796 - val_loss: 0.2921 - val_accuracy: 0.8723
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2505 - accuracy: 0.8793 - val_loss: 0.2980 - val_accuracy: 0.8716
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2502 - accuracy: 0.8790 - val_loss: 0.2933 - val_accuracy: 0.8729
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2498 - accuracy: 0.8792 - val_loss: 0.2953 - val_accuracy: 0.8720
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2499 - accuracy: 0.8791 - val_loss: 0.2960 - val_accuracy: 0.8714
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 5s 15ms/step - loss: 9.3065e-04 - accuracy: 0.9998 - val_loss: 0.0961 - val_accuracy: 0.9842
[ 0.         0.         0.        ...  0.3898531 -0.5158259 -0.       ]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5145e-04 - accuracy: 1.0000 - val_loss: 0.0933 - val_accuracy: 0.9853
[ 0.         0.         0.        ...  0.3905899 -0.5202818  0.       ]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0105e-04 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9847
[ 0.          0.          0.         ...  0.39188322 -0.5325534
  0.        ]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1370e-05 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9849
[ 0.          0.          0.         ...  0.39256585 -0.53411955
  0.        ]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9492e-05 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9852
[ 0.         0.         0.        ...  0.3930106 -0.5353843  0.       ]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 3s 14ms/step - loss: 9.3030e-05 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9846
[ 0.          0.          0.         ...  0.39384645 -0.5486798
  0.        ]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 3s 14ms/step - loss: 5.3987e-05 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 0.9838
[ 0.          0.          0.         ...  0.39495772 -0.5329883
  0.        ]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0273e-04 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9855
[ 0.          0.          0.         ...  0.3954649  -0.55066806
 -0.        ]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7853e-05 - accuracy: 1.0000 - val_loss: 0.0978 - val_accuracy: 0.9858
[ 0.          0.          0.         ...  0.40370035 -0.55238694
  0.        ]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 4s 15ms/step - loss: 2.0408e-05 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9859
[ 0.          0.          0.         ...  0.40298137 -0.5545618
 -0.        ]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5724e-05 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9855
[ 0.          0.          0.         ...  0.40389735 -0.5549969
  0.        ]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5162e-05 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9859
[ 0.          0.          0.         ...  0.40254235 -0.557172
  0.        ]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2032e-05 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9864
[ 0.          0.          0.         ...  0.4047065  -0.55779934
  0.        ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 3s 14ms/step - loss: 9.4705e-06 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9867
[ 0.          0.          0.         ...  0.40486833 -0.5586599
 -0.        ]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 3s 14ms/step - loss: 9.0066e-06 - accuracy: 1.0000 - val_loss: 0.0974 - val_accuracy: 0.9867
[ 0.          0.          0.         ...  0.40514544 -0.5611765
  0.        ]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 3s 14ms/step - loss: 7.1312e-06 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 0.9866
[ 0.          0.          0.         ...  0.40497485 -0.56159836
 -0.        ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 3s 14ms/step - loss: 7.0004e-06 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9866
[ 0.          0.          0.         ...  0.40563738 -0.56294185
  0.        ]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4492e-06 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9863
[ 0.          0.          0.         ...  0.40620402 -0.56633073
  0.        ]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6537e-06 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9860
[ 0.          0.          0.         ...  0.40628505 -0.5641691
  0.        ]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 3s 14ms/step - loss: 4.8383e-06 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9861
[ 0.          0.          0.         ...  0.40684065 -0.5670016
 -0.        ]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2661e-06 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9850
[ 0.          0.          0.         ...  0.4069241  -0.56900525
  0.        ]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.1341 - val_accuracy: 0.9818
[ 0.          0.          0.         ...  0.40220156 -0.5614891
 -0.        ]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 3s 14ms/step - loss: 9.0384e-04 - accuracy: 0.9997 - val_loss: 0.1113 - val_accuracy: 0.9837
[ 0.          0.          0.         ...  0.43992987 -0.58493453
 -0.        ]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6319e-04 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9841
[ 0.          0.          0.         ...  0.42477137 -0.58696556
 -0.        ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5259e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9845
[ 0.          0.          0.         ...  0.42555785 -0.5861252
 -0.        ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 3s 14ms/step - loss: 4.1701e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9850
[ 0.          0.          0.         ...  0.42829758 -0.58598495
 -0.        ]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 3s 15ms/step - loss: 2.9270e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9854
[ 0.         0.         0.        ...  0.4286086 -0.5854041 -0.       ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6889e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9854
[ 0.          0.          0.         ...  0.42776722 -0.5870525
  0.        ]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1721e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9853
[ 0.          0.          0.         ...  0.42749962 -0.58762914
 -0.        ]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5659e-05 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9855
[ 0.         0.         0.        ...  0.4256516 -0.5887176 -0.       ]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8327e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9852
[ 0.          0.          0.         ...  0.425942   -0.59087926
 -0.        ]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0313e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9856
[ 0.          0.          0.         ...  0.4334331  -0.59113204
 -0.        ]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 4s 15ms/step - loss: 1.5490e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9852
[ 0.          0.          0.         ...  0.43352455 -0.59114486
  0.        ]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 3s 14ms/step - loss: 8.1654e-06 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9850
[ 0.         0.         0.        ...  0.433654  -0.5916816  0.       ]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 3s 14ms/step - loss: 8.7518e-06 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9847
[ 0.          0.          0.         ...  0.43381184 -0.592908
  0.        ]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3490e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9848
[ 0.          0.          0.         ...  0.43437967 -0.59319997
 -0.        ]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 3s 14ms/step - loss: 5.7454e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9847
[ 0.          0.          0.         ...  0.43606937 -0.5939255
 -0.        ]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 3s 15ms/step - loss: 5.3795e-06 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9851
[ 0.         0.         0.        ...  0.4362031 -0.5946545  0.       ]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 3s 14ms/step - loss: 5.9587e-06 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9850
[ 0.          0.          0.         ...  0.43685165 -0.5951368
 -0.        ]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 4s 15ms/step - loss: 7.6090e-06 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9850
[ 0.         0.         0.        ...  0.4322403 -0.5967824  0.       ]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0790e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9850
[ 0.         0.         0.        ...  0.4322308 -0.5963704  0.       ]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2555e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9844
[ 0.          0.          0.         ...  0.45027718 -0.5971051
  0.        ]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1289 - val_accuracy: 0.9819
[ 0.         0.         0.        ...  0.4082035 -0.6209362 -0.       ]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 4s 15ms/step - loss: 9.4691e-04 - accuracy: 0.9997 - val_loss: 0.1201 - val_accuracy: 0.9824
[ 0.         0.         0.        ...  0.4171047 -0.6409388  0.       ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 3s 15ms/step - loss: 8.4172e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9842
[ 0.         0.         0.        ...  0.4198018 -0.648477   0.       ]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 3s 15ms/step - loss: 2.7153e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9845
[ 0.          0.          0.         ...  0.42114583 -0.6493258
 -0.        ]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 3s 15ms/step - loss: 1.7241e-05 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9847
[ 0.          0.          0.         ...  0.42167825 -0.64995426
 -0.        ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3804e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9849
[ 0.          0.          0.         ...  0.4220452  -0.65079767
 -0.        ]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4137e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9850
[ 0.         0.         0.        ...  0.4232686 -0.6508494 -0.       ]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1011e-05 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9849
[ 0.          0.          0.         ...  0.42367133 -0.6513359
  0.        ]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.0996 - val_accuracy: 0.9825
[ 0.          0.          0.         ...  0.46597597 -0.6151356
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 52/500
235/235 [==============================] - 3s 14ms/step - loss: 8.3775e-04 - accuracy: 0.9998 - val_loss: 0.0939 - val_accuracy: 0.9845
[ 0.          0.          0.         ...  0.45949364 -0.6158515
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 53/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5514e-04 - accuracy: 1.0000 - val_loss: 0.0923 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.45768076 -0.62133193
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 54/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3714e-04 - accuracy: 1.0000 - val_loss: 0.0907 - val_accuracy: 0.9849
[ 0.          0.          0.         ...  0.45995775 -0.6151077
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 55/500
235/235 [==============================] - 3s 14ms/step - loss: 7.4072e-05 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9856
[ 0.          0.          0.         ...  0.46029535 -0.6195626
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 56/500
235/235 [==============================] - 3s 15ms/step - loss: 7.7038e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9852
[ 0.          0.          0.         ...  0.45911187 -0.61793983
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 57/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2050e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9849
[ 0.          0.          0.         ...  0.45968363 -0.6211928
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 58/500
235/235 [==============================] - 3s 15ms/step - loss: 4.5488e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9849
[ 0.          0.          0.         ...  0.45955315 -0.6232606
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 59/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5487e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9850
[ 0.          0.          0.         ...  0.45922405 -0.6263509
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 60/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0614e-05 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9848
[ 0.          0.          0.         ...  0.46245384 -0.629474
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 61/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0954e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9849
[ 0.          0.          0.         ...  0.46376142 -0.63020724
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 62/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6236e-05 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9848
[ 0.         0.         0.        ...  0.46419   -0.6319635  0.       ]
Sparsity at: 0.6458602554470323
Epoch 63/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2754e-05 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.46525973 -0.6335181
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 64/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3744e-05 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9852
[ 0.          0.          0.         ...  0.46918952 -0.6317435
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 65/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1919e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.46811703 -0.6332109
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 66/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1026e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9854
[ 0.          0.          0.         ...  0.46809447 -0.6363593
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 67/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6383e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9854
[ 0.          0.          0.         ...  0.46822533 -0.63829225
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 68/500
235/235 [==============================] - 4s 15ms/step - loss: 1.4273e-05 - accuracy: 1.0000 - val_loss: 0.0935 - val_accuracy: 0.9851
[ 0.         0.         0.        ...  0.4686195 -0.638418   0.       ]
Sparsity at: 0.6458602554470323
Epoch 69/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1813e-05 - accuracy: 1.0000 - val_loss: 0.0933 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.46934253 -0.63940203
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 70/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1235e-05 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9854
[ 0.          0.          0.         ...  0.47278318 -0.64200133
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 71/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3956e-05 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9847
[ 0.          0.          0.         ...  0.47114527 -0.6465881
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 72/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0105e-05 - accuracy: 1.0000 - val_loss: 0.0950 - val_accuracy: 0.9848
[ 0.          0.          0.         ...  0.47417617 -0.6473242
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 73/500
235/235 [==============================] - 3s 15ms/step - loss: 8.0636e-06 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9849
[ 0.         0.         0.        ...  0.4743592 -0.6472893  0.       ]
Sparsity at: 0.6458602554470323
Epoch 74/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7326e-05 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9835
[ 0.         0.         0.        ...  0.4969626 -0.6483071 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 75/500
235/235 [==============================] - 3s 14ms/step - loss: 3.8910e-04 - accuracy: 0.9999 - val_loss: 0.1076 - val_accuracy: 0.9835
[ 0.          0.          0.         ...  0.50154227 -0.6594645
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 76/500
235/235 [==============================] - 3s 14ms/step - loss: 1.1671e-04 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9843
[ 0.          0.          0.         ...  0.4979294  -0.69436276
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 77/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2297e-05 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9852
[ 0.          0.          0.         ...  0.49719995 -0.68884367
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 78/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3438e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9854
[ 0.         0.         0.        ...  0.4970855 -0.6894904 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 79/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2293e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.49784008 -0.6892401
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 80/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9657e-05 - accuracy: 1.0000 - val_loss: 0.1031 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.49978006 -0.69144285
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 81/500
235/235 [==============================] - 3s 14ms/step - loss: 7.6235e-06 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.49989307 -0.69287497
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 82/500
235/235 [==============================] - 3s 14ms/step - loss: 6.4158e-06 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9852
[ 0.         0.         0.        ...  0.5002293 -0.6932928 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 83/500
235/235 [==============================] - 3s 14ms/step - loss: 6.5203e-06 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9854
[ 0.          0.          0.         ...  0.50026053 -0.6950041
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 84/500
235/235 [==============================] - 3s 14ms/step - loss: 5.7740e-06 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9852
[ 0.          0.          0.         ...  0.50018084 -0.6954591
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 85/500
235/235 [==============================] - 3s 14ms/step - loss: 4.9754e-06 - accuracy: 1.0000 - val_loss: 0.1039 - val_accuracy: 0.9852
[ 0.         0.         0.        ...  0.5000696 -0.6959325 -0.       ]
Sparsity at: 0.6458602554470323
Epoch 86/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6527e-06 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9852
[ 0.          0.          0.         ...  0.50016165 -0.6957323
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 87/500
235/235 [==============================] - 3s 14ms/step - loss: 5.9431e-06 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9852
[ 0.         0.         0.        ...  0.5012834 -0.6963597  0.       ]
Sparsity at: 0.6458602554470323
Epoch 88/500
235/235 [==============================] - 3s 14ms/step - loss: 7.3387e-05 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9842
[ 0.         0.         0.        ...  0.4997874 -0.6974817  0.       ]
Sparsity at: 0.6458602554470323
Epoch 89/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9232e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9847
[ 0.          0.          0.         ...  0.50163746 -0.7015947
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 90/500
235/235 [==============================] - 3s 14ms/step - loss: 7.9061e-06 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9854
[ 0.          0.          0.         ...  0.50159854 -0.7011329
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 91/500
235/235 [==============================] - 3s 14ms/step - loss: 7.7169e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9850
[ 0.          0.          0.         ...  0.50412345 -0.7056029
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 92/500
235/235 [==============================] - 3s 14ms/step - loss: 4.0730e-06 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9853
[ 0.          0.          0.         ...  0.50409067 -0.7064393
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 93/500
235/235 [==============================] - 3s 14ms/step - loss: 3.5732e-06 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.50450134 -0.70622724
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 94/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7936e-06 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.50526816 -0.70619774
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 95/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2695e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9852
[ 0.          0.          0.         ...  0.50399023 -0.70585406
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 96/500
235/235 [==============================] - 3s 14ms/step - loss: 2.2315e-06 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9854
[ 0.          0.          0.         ...  0.50428575 -0.7084808
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 97/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0315e-06 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9855
[ 0.          0.          0.         ...  0.5048268  -0.70761704
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 98/500
235/235 [==============================] - 3s 15ms/step - loss: 4.9889e-06 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9851
[ 0.          0.          0.         ...  0.50511247 -0.7122094
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 99/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0651e-06 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9850
[ 0.          0.          0.         ...  0.50402176 -0.71068704
 -0.        ]
Sparsity at: 0.6458602554470323
Epoch 100/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4412e-06 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9853
[ 0.          0.          0.         ...  0.50164115 -0.7095205
  0.        ]
Sparsity at: 0.6458602554470323
Epoch 101/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0086 - accuracy: 0.9972 - val_loss: 0.1061 - val_accuracy: 0.9825
[ 0.         0.         0.        ...  0.        -0.6716096 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 102/500
235/235 [==============================] - 3s 14ms/step - loss: 9.7591e-04 - accuracy: 0.9998 - val_loss: 0.1060 - val_accuracy: 0.9828
[ 0.         0.         0.        ...  0.        -0.6723734  0.       ]
Sparsity at: 0.7594515401953419
Epoch 103/500
235/235 [==============================] - 4s 15ms/step - loss: 2.8747e-04 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9825
[ 0.         0.         0.        ...  0.        -0.6713119  0.       ]
Sparsity at: 0.7594515401953419
Epoch 104/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9578e-04 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9826
[ 0.          0.          0.         ...  0.         -0.67197144
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 105/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6361e-04 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9825
[ 0.          0.          0.         ...  0.         -0.67481494
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 106/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9178e-04 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9832
[ 0.         0.         0.        ...  0.        -0.6761899  0.       ]
Sparsity at: 0.7594515401953419
Epoch 107/500
235/235 [==============================] - 3s 14ms/step - loss: 1.4322e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9828
[ 0.         0.         0.        ...  0.        -0.6750394  0.       ]
Sparsity at: 0.7594515401953419
Epoch 108/500
235/235 [==============================] - 3s 14ms/step - loss: 8.9606e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9830
[ 0.         0.         0.        ... -0.        -0.6754024  0.       ]
Sparsity at: 0.7594515401953419
Epoch 109/500
235/235 [==============================] - 3s 14ms/step - loss: 8.7648e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9827
[ 0.         0.         0.        ...  0.        -0.6736307  0.       ]
Sparsity at: 0.7594515401953419
Epoch 110/500
235/235 [==============================] - 3s 14ms/step - loss: 8.4442e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9828
[ 0.         0.         0.        ...  0.        -0.6733476  0.       ]
Sparsity at: 0.7594515401953419
Epoch 111/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0343e-04 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9828
[ 0.          0.          0.         ... -0.         -0.67449695
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 112/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0751e-05 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9826
[ 0.         0.         0.        ... -0.        -0.6736327  0.       ]
Sparsity at: 0.7594515401953419
Epoch 113/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4871e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9830
[ 0.         0.         0.        ... -0.        -0.6721374  0.       ]
Sparsity at: 0.7594515401953419
Epoch 114/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2220e-05 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9834
[ 0.          0.          0.         ... -0.         -0.66964775
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 115/500
235/235 [==============================] - 4s 15ms/step - loss: 4.5787e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9829
[ 0.         0.         0.        ...  0.        -0.6710673  0.       ]
Sparsity at: 0.7594515401953419
Epoch 116/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9035e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9838
[ 0.         0.         0.        ...  0.        -0.6726765  0.       ]
Sparsity at: 0.7594515401953419
Epoch 117/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2739e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9834
[ 0.         0.         0.        ...  0.        -0.6735285  0.       ]
Sparsity at: 0.7594515401953419
Epoch 118/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4427e-05 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9836
[ 0.         0.         0.        ...  0.        -0.6752823  0.       ]
Sparsity at: 0.7594515401953419
Epoch 119/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8867e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9838
[ 0.        0.        0.       ...  0.       -0.677676  0.      ]
Sparsity at: 0.7594515401953419
Epoch 120/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5994e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9831
[ 0.        0.        0.       ...  0.       -0.674391 -0.      ]
Sparsity at: 0.7594515401953419
Epoch 121/500
235/235 [==============================] - 3s 14ms/step - loss: 2.6212e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9831
[ 0.          0.          0.         ...  0.         -0.67842406
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 122/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5125e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9834
[ 0.         0.         0.        ... -0.        -0.6812905  0.       ]
Sparsity at: 0.7594515401953419
Epoch 123/500
235/235 [==============================] - 3s 14ms/step - loss: 1.9176e-05 - accuracy: 1.0000 - val_loss: 0.1094 - val_accuracy: 0.9839
[ 0.         0.         0.        ... -0.        -0.6828214  0.       ]
Sparsity at: 0.7594515401953419
Epoch 124/500
235/235 [==============================] - 3s 15ms/step - loss: 2.2365e-05 - accuracy: 1.0000 - val_loss: 0.1114 - val_accuracy: 0.9831
[ 0.          0.          0.         ... -0.         -0.68508834
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 125/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0145e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9838
[ 0.         0.         0.        ...  0.        -0.6870583 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 126/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3480e-05 - accuracy: 1.0000 - val_loss: 0.1121 - val_accuracy: 0.9835
[ 0.         0.         0.        ...  0.        -0.6871926  0.       ]
Sparsity at: 0.7594515401953419
Epoch 127/500
235/235 [==============================] - 4s 16ms/step - loss: 1.3140e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9836
[ 0.         0.         0.        ...  0.        -0.6906433 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 128/500
235/235 [==============================] - 4s 17ms/step - loss: 1.1276e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9839
[ 0.         0.         0.        ...  0.        -0.6931372  0.       ]
Sparsity at: 0.7594515401953419
Epoch 129/500
235/235 [==============================] - 4s 17ms/step - loss: 1.0294e-05 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9834
[ 0.         0.         0.        ...  0.        -0.6970223  0.       ]
Sparsity at: 0.7594515401953419
Epoch 130/500
235/235 [==============================] - 4s 17ms/step - loss: 8.1668e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9833
[ 0.          0.          0.         ...  0.         -0.69800645
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 131/500
235/235 [==============================] - 4s 15ms/step - loss: 7.9750e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9835
[ 0.          0.          0.         ... -0.         -0.69682264
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 132/500
235/235 [==============================] - 4s 15ms/step - loss: 6.8324e-06 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9836
[ 0.         0.         0.        ...  0.        -0.6987585  0.       ]
Sparsity at: 0.7594515401953419
Epoch 133/500
235/235 [==============================] - 4s 16ms/step - loss: 5.6998e-06 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9832
[ 0.          0.          0.         ...  0.         -0.69986844
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 134/500
235/235 [==============================] - 4s 16ms/step - loss: 6.0706e-06 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9834
[ 0.         0.         0.        ...  0.        -0.7011575  0.       ]
Sparsity at: 0.7594515401953419
Epoch 135/500
235/235 [==============================] - 4s 16ms/step - loss: 5.3144e-06 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9832
[ 0.          0.          0.         ...  0.         -0.70320445
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 136/500
235/235 [==============================] - 4s 15ms/step - loss: 6.4171e-06 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9836
[ 0.         0.         0.        ...  0.        -0.7045949  0.       ]
Sparsity at: 0.7594515401953419
Epoch 137/500
235/235 [==============================] - 4s 16ms/step - loss: 5.5977e-06 - accuracy: 1.0000 - val_loss: 0.1199 - val_accuracy: 0.9835
[ 0.          0.          0.         ...  0.         -0.70330656
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 138/500
235/235 [==============================] - 4s 16ms/step - loss: 1.6211e-04 - accuracy: 0.9999 - val_loss: 0.1342 - val_accuracy: 0.9821
[ 0.          0.          0.         ...  0.         -0.69383526
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 139/500
235/235 [==============================] - 4s 16ms/step - loss: 2.0013e-04 - accuracy: 0.9999 - val_loss: 0.1317 - val_accuracy: 0.9828
[ 0.          0.          0.         ...  0.         -0.69614774
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 140/500
235/235 [==============================] - 4s 16ms/step - loss: 3.1898e-04 - accuracy: 0.9999 - val_loss: 0.1262 - val_accuracy: 0.9825
[ 0.         0.         0.        ...  0.        -0.6919799  0.       ]
Sparsity at: 0.7594515401953419
Epoch 141/500
235/235 [==============================] - 4s 16ms/step - loss: 1.1368e-04 - accuracy: 1.0000 - val_loss: 0.1234 - val_accuracy: 0.9838
[ 0.         0.         0.        ...  0.        -0.6921864 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 142/500
235/235 [==============================] - 4s 16ms/step - loss: 1.8096e-05 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9838
[ 0.         0.         0.        ...  0.        -0.6934519  0.       ]
Sparsity at: 0.7594515401953419
Epoch 143/500
235/235 [==============================] - 4s 16ms/step - loss: 4.2352e-05 - accuracy: 1.0000 - val_loss: 0.1288 - val_accuracy: 0.9833
[ 0.         0.         0.        ... -0.        -0.6941465  0.       ]
Sparsity at: 0.7594515401953419
Epoch 144/500
235/235 [==============================] - 4s 16ms/step - loss: 4.3826e-05 - accuracy: 1.0000 - val_loss: 0.1232 - val_accuracy: 0.9839
[ 0.         0.         0.        ...  0.        -0.7012039  0.       ]
Sparsity at: 0.7594515401953419
Epoch 145/500
235/235 [==============================] - 4s 16ms/step - loss: 1.6384e-05 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9835
[ 0.         0.         0.        ... -0.        -0.7015288  0.       ]
Sparsity at: 0.7594515401953419
Epoch 146/500
235/235 [==============================] - 4s 15ms/step - loss: 1.7483e-04 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9829
[ 0.          0.          0.         ... -0.         -0.70105296
 -0.        ]
Sparsity at: 0.7594515401953419
Epoch 147/500
235/235 [==============================] - 5s 21ms/step - loss: 9.4821e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9832
[ 0.         0.         0.        ... -0.        -0.7016805  0.       ]
Sparsity at: 0.7594515401953419
Epoch 148/500
235/235 [==============================] - 4s 18ms/step - loss: 1.2213e-05 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9835
[ 0.          0.          0.         ... -0.         -0.70687497
  0.        ]
Sparsity at: 0.7594515401953419
Epoch 149/500
235/235 [==============================] - 4s 17ms/step - loss: 8.3476e-06 - accuracy: 1.0000 - val_loss: 0.1259 - val_accuracy: 0.9835
[ 0.         0.         0.        ... -0.        -0.7079566 -0.       ]
Sparsity at: 0.7594515401953419
Epoch 150/500
235/235 [==============================] - 4s 16ms/step - loss: 8.7716e-06 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9832
[ 0.         0.         0.        ...  0.        -0.7077201  0.       ]
Sparsity at: 0.7594515401953419
Epoch 151/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0236 - accuracy: 0.9929 - val_loss: 0.1132 - val_accuracy: 0.9802
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 152/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9989 - val_loss: 0.1126 - val_accuracy: 0.9815
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 153/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.1121 - val_accuracy: 0.9809
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 154/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1119 - val_accuracy: 0.9807
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 155/500
235/235 [==============================] - 3s 14ms/step - loss: 8.5730e-04 - accuracy: 0.9999 - val_loss: 0.1120 - val_accuracy: 0.9805
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 156/500
235/235 [==============================] - 3s 15ms/step - loss: 7.2073e-04 - accuracy: 0.9999 - val_loss: 0.1121 - val_accuracy: 0.9810
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 157/500
235/235 [==============================] - 3s 14ms/step - loss: 5.8185e-04 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9808
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 158/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0905e-04 - accuracy: 0.9999 - val_loss: 0.1127 - val_accuracy: 0.9817
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 159/500
235/235 [==============================] - 3s 14ms/step - loss: 4.6181e-04 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9812
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 160/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6286e-04 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9812
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 161/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2952e-04 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9811
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 162/500
235/235 [==============================] - 3s 14ms/step - loss: 2.8115e-04 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9813
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 163/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5485e-04 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9816
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 164/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5650e-04 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9815
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 165/500
235/235 [==============================] - 3s 15ms/step - loss: 2.1671e-04 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9816
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 166/500
235/235 [==============================] - 3s 14ms/step - loss: 1.7602e-04 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9818
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 167/500
235/235 [==============================] - 3s 14ms/step - loss: 1.6571e-04 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9816
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 168/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3040e-04 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9818
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 169/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3042e-04 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9817
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 170/500
235/235 [==============================] - 4s 15ms/step - loss: 1.1553e-04 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9821
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 171/500
235/235 [==============================] - 3s 15ms/step - loss: 1.2115e-04 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9820
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 172/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5756e-04 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9814
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 173/500
235/235 [==============================] - 4s 16ms/step - loss: 8.7537e-05 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9818
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 174/500
235/235 [==============================] - 3s 15ms/step - loss: 7.7734e-05 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9821
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 175/500
235/235 [==============================] - 3s 15ms/step - loss: 5.8022e-05 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9824
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 176/500
235/235 [==============================] - 3s 14ms/step - loss: 5.6830e-05 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9819
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 177/500
235/235 [==============================] - 3s 14ms/step - loss: 5.0371e-05 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9818
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 178/500
235/235 [==============================] - 3s 14ms/step - loss: 5.2323e-05 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9815
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 179/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3935e-04 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9821
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 180/500
235/235 [==============================] - 3s 14ms/step - loss: 5.5416e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9817
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 181/500
235/235 [==============================] - 4s 15ms/step - loss: 3.7010e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9823
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 182/500
235/235 [==============================] - 3s 15ms/step - loss: 2.8320e-05 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9822
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 183/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2234e-05 - accuracy: 1.0000 - val_loss: 0.1266 - val_accuracy: 0.9821
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 184/500
235/235 [==============================] - 3s 14ms/step - loss: 2.5283e-05 - accuracy: 1.0000 - val_loss: 0.1277 - val_accuracy: 0.9819
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 185/500
235/235 [==============================] - 3s 14ms/step - loss: 6.5624e-05 - accuracy: 1.0000 - val_loss: 0.1329 - val_accuracy: 0.9810
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 186/500
235/235 [==============================] - 3s 14ms/step - loss: 5.4727e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9822
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 187/500
235/235 [==============================] - 3s 14ms/step - loss: 8.6398e-05 - accuracy: 1.0000 - val_loss: 0.1331 - val_accuracy: 0.9817
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 188/500
235/235 [==============================] - 3s 14ms/step - loss: 9.8241e-05 - accuracy: 1.0000 - val_loss: 0.1342 - val_accuracy: 0.9822
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.8448196844477837
Epoch 189/500
235/235 [==============================] - 3s 14ms/step - loss: 4.7776e-05 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9823
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 190/500
235/235 [==============================] - 3s 14ms/step - loss: 3.2814e-05 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9815
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 191/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4437e-05 - accuracy: 1.0000 - val_loss: 0.1335 - val_accuracy: 0.9818
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 192/500
235/235 [==============================] - 3s 14ms/step - loss: 9.6971e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9815
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 193/500
235/235 [==============================] - 3s 14ms/step - loss: 4.3672e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9816
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.8448196844477837
Epoch 194/500
235/235 [==============================] - 3s 14ms/step - loss: 3.7415e-05 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9813
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 195/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3698e-05 - accuracy: 1.0000 - val_loss: 0.1365 - val_accuracy: 0.9819
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 196/500
235/235 [==============================] - 3s 15ms/step - loss: 1.3509e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9821
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 197/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3267e-05 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9821
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.8448196844477837
Epoch 198/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5826e-05 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9822
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 199/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0832e-05 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9822
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.8448196844477837
Epoch 200/500
235/235 [==============================] - 3s 15ms/step - loss: 1.1472e-05 - accuracy: 1.0000 - val_loss: 0.1401 - val_accuracy: 0.9823
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.8448196844477837
Epoch 201/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0623 - accuracy: 0.9835 - val_loss: 0.1405 - val_accuracy: 0.9742
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 202/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0190 - accuracy: 0.9942 - val_loss: 0.1293 - val_accuracy: 0.9748
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 203/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0116 - accuracy: 0.9965 - val_loss: 0.1252 - val_accuracy: 0.9754
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 204/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0079 - accuracy: 0.9978 - val_loss: 0.1238 - val_accuracy: 0.9759
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 205/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0059 - accuracy: 0.9987 - val_loss: 0.1245 - val_accuracy: 0.9755
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 206/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0048 - accuracy: 0.9987 - val_loss: 0.1225 - val_accuracy: 0.9761
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 207/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.1233 - val_accuracy: 0.9766
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 208/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.1228 - val_accuracy: 0.9766
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 209/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0027 - accuracy: 0.9996 - val_loss: 0.1225 - val_accuracy: 0.9763
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 210/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9997 - val_loss: 0.1246 - val_accuracy: 0.9765
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 211/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0020 - accuracy: 0.9998 - val_loss: 0.1243 - val_accuracy: 0.9775
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 212/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.1249 - val_accuracy: 0.9775
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 213/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1261 - val_accuracy: 0.9774
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 214/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1270 - val_accuracy: 0.9776
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 215/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1270 - val_accuracy: 0.9784
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 216/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1291 - val_accuracy: 0.9776
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 217/500
235/235 [==============================] - 3s 15ms/step - loss: 9.6531e-04 - accuracy: 0.9999 - val_loss: 0.1308 - val_accuracy: 0.9772
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 218/500
235/235 [==============================] - 3s 14ms/step - loss: 7.9615e-04 - accuracy: 1.0000 - val_loss: 0.1321 - val_accuracy: 0.9776
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 219/500
235/235 [==============================] - 3s 14ms/step - loss: 7.2509e-04 - accuracy: 1.0000 - val_loss: 0.1320 - val_accuracy: 0.9775
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 220/500
235/235 [==============================] - 3s 14ms/step - loss: 6.5214e-04 - accuracy: 0.9999 - val_loss: 0.1331 - val_accuracy: 0.9775
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 221/500
235/235 [==============================] - 3s 14ms/step - loss: 5.7805e-04 - accuracy: 1.0000 - val_loss: 0.1344 - val_accuracy: 0.9779
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 222/500
235/235 [==============================] - 3s 14ms/step - loss: 6.0168e-04 - accuracy: 0.9999 - val_loss: 0.1349 - val_accuracy: 0.9775
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 223/500
235/235 [==============================] - 3s 14ms/step - loss: 4.5303e-04 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9784
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 224/500
235/235 [==============================] - 3s 14ms/step - loss: 4.2693e-04 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9777
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 225/500
235/235 [==============================] - 3s 14ms/step - loss: 3.4728e-04 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9782
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 226/500
235/235 [==============================] - 3s 14ms/step - loss: 3.6580e-04 - accuracy: 1.0000 - val_loss: 0.1399 - val_accuracy: 0.9782
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 227/500
235/235 [==============================] - 3s 14ms/step - loss: 3.9179e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9783
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 228/500
235/235 [==============================] - 3s 14ms/step - loss: 3.1101e-04 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9775
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 229/500
235/235 [==============================] - 3s 14ms/step - loss: 2.7967e-04 - accuracy: 1.0000 - val_loss: 0.1430 - val_accuracy: 0.9784
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 230/500
235/235 [==============================] - 3s 14ms/step - loss: 2.3698e-04 - accuracy: 1.0000 - val_loss: 0.1448 - val_accuracy: 0.9788
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 231/500
235/235 [==============================] - 3s 14ms/step - loss: 2.4002e-04 - accuracy: 1.0000 - val_loss: 0.1460 - val_accuracy: 0.9786
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 232/500
235/235 [==============================] - 3s 14ms/step - loss: 2.1044e-04 - accuracy: 1.0000 - val_loss: 0.1453 - val_accuracy: 0.9778
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 233/500
235/235 [==============================] - 3s 15ms/step - loss: 2.2428e-04 - accuracy: 1.0000 - val_loss: 0.1492 - val_accuracy: 0.9778
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 234/500
235/235 [==============================] - 3s 14ms/step - loss: 3.0708e-04 - accuracy: 0.9999 - val_loss: 0.1482 - val_accuracy: 0.9783
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 235/500
235/235 [==============================] - 3s 14ms/step - loss: 2.0479e-04 - accuracy: 1.0000 - val_loss: 0.1477 - val_accuracy: 0.9783
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 236/500
235/235 [==============================] - 3s 15ms/step - loss: 1.6461e-04 - accuracy: 1.0000 - val_loss: 0.1507 - val_accuracy: 0.9789
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 237/500
235/235 [==============================] - 3s 14ms/step - loss: 1.3638e-04 - accuracy: 1.0000 - val_loss: 0.1516 - val_accuracy: 0.9784
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 238/500
235/235 [==============================] - 3s 14ms/step - loss: 1.8562e-04 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9777
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 239/500
235/235 [==============================] - 3s 14ms/step - loss: 1.5996e-04 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9782
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 240/500
235/235 [==============================] - 4s 15ms/step - loss: 1.2188e-04 - accuracy: 1.0000 - val_loss: 0.1558 - val_accuracy: 0.9780
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 241/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0143e-04 - accuracy: 1.0000 - val_loss: 0.1561 - val_accuracy: 0.9786
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 242/500
235/235 [==============================] - 4s 15ms/step - loss: 7.5947e-05 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9784
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9059804658151765
Epoch 243/500
235/235 [==============================] - 3s 14ms/step - loss: 8.2570e-05 - accuracy: 1.0000 - val_loss: 0.1581 - val_accuracy: 0.9784
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 244/500
235/235 [==============================] - 3s 14ms/step - loss: 9.0700e-05 - accuracy: 1.0000 - val_loss: 0.1577 - val_accuracy: 0.9783
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9059804658151765
Epoch 245/500
235/235 [==============================] - 3s 15ms/step - loss: 8.6183e-05 - accuracy: 1.0000 - val_loss: 0.1579 - val_accuracy: 0.9786
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 246/500
235/235 [==============================] - 3s 14ms/step - loss: 1.2061e-04 - accuracy: 1.0000 - val_loss: 0.1618 - val_accuracy: 0.9785
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 247/500
235/235 [==============================] - 3s 14ms/step - loss: 6.2780e-05 - accuracy: 1.0000 - val_loss: 0.1612 - val_accuracy: 0.9785
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 248/500
235/235 [==============================] - 3s 14ms/step - loss: 6.3127e-05 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9782
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059804658151765
Epoch 249/500
235/235 [==============================] - 3s 14ms/step - loss: 7.5904e-05 - accuracy: 1.0000 - val_loss: 0.1647 - val_accuracy: 0.9783
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9059804658151765
Epoch 250/500
235/235 [==============================] - 3s 15ms/step - loss: 7.3875e-05 - accuracy: 1.0000 - val_loss: 0.1646 - val_accuracy: 0.9781
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059804658151765
Epoch 251/500
235/235 [==============================] - 3s 15ms/step - loss: 0.2209 - accuracy: 0.9476 - val_loss: 0.2066 - val_accuracy: 0.9546
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 252/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0985 - accuracy: 0.9707 - val_loss: 0.1778 - val_accuracy: 0.9598
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 253/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0772 - accuracy: 0.9765 - val_loss: 0.1644 - val_accuracy: 0.9615
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 254/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0669 - accuracy: 0.9794 - val_loss: 0.1559 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 255/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0588 - accuracy: 0.9817 - val_loss: 0.1492 - val_accuracy: 0.9636
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 256/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0520 - accuracy: 0.9836 - val_loss: 0.1445 - val_accuracy: 0.9651
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 257/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0482 - accuracy: 0.9849 - val_loss: 0.1407 - val_accuracy: 0.9664
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 258/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0446 - accuracy: 0.9859 - val_loss: 0.1380 - val_accuracy: 0.9665
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 259/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0407 - accuracy: 0.9871 - val_loss: 0.1360 - val_accuracy: 0.9669
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 260/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0385 - accuracy: 0.9880 - val_loss: 0.1339 - val_accuracy: 0.9676
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 261/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0361 - accuracy: 0.9887 - val_loss: 0.1328 - val_accuracy: 0.9676
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 262/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0349 - accuracy: 0.9890 - val_loss: 0.1326 - val_accuracy: 0.9673
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 263/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0315 - accuracy: 0.9901 - val_loss: 0.1319 - val_accuracy: 0.9676
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 264/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0307 - accuracy: 0.9908 - val_loss: 0.1320 - val_accuracy: 0.9680
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 265/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0290 - accuracy: 0.9910 - val_loss: 0.1327 - val_accuracy: 0.9679
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 266/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0271 - accuracy: 0.9917 - val_loss: 0.1328 - val_accuracy: 0.9685
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 267/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0257 - accuracy: 0.9922 - val_loss: 0.1329 - val_accuracy: 0.9685
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 268/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0249 - accuracy: 0.9924 - val_loss: 0.1331 - val_accuracy: 0.9685
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 269/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0236 - accuracy: 0.9930 - val_loss: 0.1330 - val_accuracy: 0.9683
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 270/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0226 - accuracy: 0.9930 - val_loss: 0.1339 - val_accuracy: 0.9683
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 271/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0219 - accuracy: 0.9936 - val_loss: 0.1334 - val_accuracy: 0.9680
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 272/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0205 - accuracy: 0.9940 - val_loss: 0.1346 - val_accuracy: 0.9688
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 273/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0192 - accuracy: 0.9944 - val_loss: 0.1356 - val_accuracy: 0.9684
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 274/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0187 - accuracy: 0.9946 - val_loss: 0.1364 - val_accuracy: 0.9691
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 275/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0175 - accuracy: 0.9948 - val_loss: 0.1366 - val_accuracy: 0.9684
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 276/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0169 - accuracy: 0.9954 - val_loss: 0.1372 - val_accuracy: 0.9680
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 277/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0162 - accuracy: 0.9954 - val_loss: 0.1392 - val_accuracy: 0.9685
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 278/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0158 - accuracy: 0.9955 - val_loss: 0.1400 - val_accuracy: 0.9684
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 279/500
235/235 [==============================] - 4s 15ms/step - loss: 0.0140 - accuracy: 0.9964 - val_loss: 0.1394 - val_accuracy: 0.9690
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 280/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0138 - accuracy: 0.9964 - val_loss: 0.1404 - val_accuracy: 0.9690
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 281/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0135 - accuracy: 0.9963 - val_loss: 0.1426 - val_accuracy: 0.9689
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 282/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0128 - accuracy: 0.9967 - val_loss: 0.1424 - val_accuracy: 0.9684
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 283/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0119 - accuracy: 0.9969 - val_loss: 0.1443 - val_accuracy: 0.9688
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 284/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0116 - accuracy: 0.9970 - val_loss: 0.1460 - val_accuracy: 0.9691
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 285/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9974 - val_loss: 0.1456 - val_accuracy: 0.9693
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469872276483847
Epoch 286/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9972 - val_loss: 0.1468 - val_accuracy: 0.9691
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 287/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0101 - accuracy: 0.9976 - val_loss: 0.1467 - val_accuracy: 0.9688
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 288/500
235/235 [==============================] - 3s 15ms/step - loss: 0.0102 - accuracy: 0.9972 - val_loss: 0.1497 - val_accuracy: 0.9687
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 289/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0092 - accuracy: 0.9981 - val_loss: 0.1506 - val_accuracy: 0.9685
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 290/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0089 - accuracy: 0.9979 - val_loss: 0.1520 - val_accuracy: 0.9687
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 291/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9982 - val_loss: 0.1540 - val_accuracy: 0.9687
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 292/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0080 - accuracy: 0.9983 - val_loss: 0.1537 - val_accuracy: 0.9692
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 293/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0080 - accuracy: 0.9984 - val_loss: 0.1549 - val_accuracy: 0.9693
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 294/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0076 - accuracy: 0.9983 - val_loss: 0.1559 - val_accuracy: 0.9684
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 295/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0071 - accuracy: 0.9985 - val_loss: 0.1587 - val_accuracy: 0.9697
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 296/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0066 - accuracy: 0.9988 - val_loss: 0.1590 - val_accuracy: 0.9697
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 297/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0065 - accuracy: 0.9986 - val_loss: 0.1614 - val_accuracy: 0.9696
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469872276483847
Epoch 298/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0061 - accuracy: 0.9988 - val_loss: 0.1625 - val_accuracy: 0.9691
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 299/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0057 - accuracy: 0.9990 - val_loss: 0.1646 - val_accuracy: 0.9689
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 300/500
235/235 [==============================] - 3s 14ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 0.1653 - val_accuracy: 0.9688
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469872276483847
Epoch 301/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5164 - accuracy: 0.8768 - val_loss: 0.3727 - val_accuracy: 0.9041
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9718557475582269
Epoch 302/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2957 - accuracy: 0.9171 - val_loss: 0.3096 - val_accuracy: 0.9169
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 303/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2532 - accuracy: 0.9267 - val_loss: 0.2797 - val_accuracy: 0.9248
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 304/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2309 - accuracy: 0.9312 - val_loss: 0.2618 - val_accuracy: 0.9279
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 305/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2155 - accuracy: 0.9353 - val_loss: 0.2493 - val_accuracy: 0.9297
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 306/500
235/235 [==============================] - 3s 14ms/step - loss: 0.2039 - accuracy: 0.9383 - val_loss: 0.2399 - val_accuracy: 0.9329
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 307/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1953 - accuracy: 0.9402 - val_loss: 0.2318 - val_accuracy: 0.9343
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 308/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1889 - accuracy: 0.9421 - val_loss: 0.2259 - val_accuracy: 0.9364
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 309/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1827 - accuracy: 0.9434 - val_loss: 0.2210 - val_accuracy: 0.9377
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 310/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1781 - accuracy: 0.9443 - val_loss: 0.2164 - val_accuracy: 0.9395
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 311/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1741 - accuracy: 0.9462 - val_loss: 0.2121 - val_accuracy: 0.9402
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 312/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1691 - accuracy: 0.9482 - val_loss: 0.2089 - val_accuracy: 0.9410
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 313/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1659 - accuracy: 0.9485 - val_loss: 0.2061 - val_accuracy: 0.9417
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 314/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1628 - accuracy: 0.9495 - val_loss: 0.2036 - val_accuracy: 0.9427
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 315/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1590 - accuracy: 0.9505 - val_loss: 0.2012 - val_accuracy: 0.9438
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 316/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1571 - accuracy: 0.9517 - val_loss: 0.1993 - val_accuracy: 0.9445
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 317/500
235/235 [==============================] - 4s 16ms/step - loss: 0.1548 - accuracy: 0.9521 - val_loss: 0.1978 - val_accuracy: 0.9448
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 318/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1519 - accuracy: 0.9523 - val_loss: 0.1966 - val_accuracy: 0.9450
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 319/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9539 - val_loss: 0.1951 - val_accuracy: 0.9452
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 320/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1479 - accuracy: 0.9540 - val_loss: 0.1934 - val_accuracy: 0.9456
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 321/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1450 - accuracy: 0.9552 - val_loss: 0.1928 - val_accuracy: 0.9461
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 322/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1444 - accuracy: 0.9553 - val_loss: 0.1915 - val_accuracy: 0.9464
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 323/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9560 - val_loss: 0.1903 - val_accuracy: 0.9461
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 324/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9561 - val_loss: 0.1894 - val_accuracy: 0.9467
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 325/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9565 - val_loss: 0.1884 - val_accuracy: 0.9466
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 326/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9568 - val_loss: 0.1874 - val_accuracy: 0.9464
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 327/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9582 - val_loss: 0.1868 - val_accuracy: 0.9458
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 328/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9580 - val_loss: 0.1860 - val_accuracy: 0.9464
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 329/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9584 - val_loss: 0.1852 - val_accuracy: 0.9464
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 330/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9585 - val_loss: 0.1842 - val_accuracy: 0.9462
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 331/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9588 - val_loss: 0.1836 - val_accuracy: 0.9469
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 332/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1313 - accuracy: 0.9595 - val_loss: 0.1834 - val_accuracy: 0.9471
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 333/500
235/235 [==============================] - 3s 15ms/step - loss: 0.1302 - accuracy: 0.9601 - val_loss: 0.1824 - val_accuracy: 0.9472
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 334/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9599 - val_loss: 0.1823 - val_accuracy: 0.9472
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 335/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9599 - val_loss: 0.1817 - val_accuracy: 0.9476
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 336/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1273 - accuracy: 0.9610 - val_loss: 0.1811 - val_accuracy: 0.9475
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 337/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9608 - val_loss: 0.1806 - val_accuracy: 0.9479
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 338/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.1802 - val_accuracy: 0.9484
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 339/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9616 - val_loss: 0.1799 - val_accuracy: 0.9492
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 340/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9618 - val_loss: 0.1793 - val_accuracy: 0.9488
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 341/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9617 - val_loss: 0.1790 - val_accuracy: 0.9496
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 342/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9620 - val_loss: 0.1790 - val_accuracy: 0.9494
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 343/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9616 - val_loss: 0.1787 - val_accuracy: 0.9498
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 344/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9627 - val_loss: 0.1779 - val_accuracy: 0.9502
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 345/500
235/235 [==============================] - 4s 15ms/step - loss: 0.1211 - accuracy: 0.9625 - val_loss: 0.1779 - val_accuracy: 0.9500
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 346/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9633 - val_loss: 0.1776 - val_accuracy: 0.9507
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 347/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9633 - val_loss: 0.1775 - val_accuracy: 0.9507
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 348/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9636 - val_loss: 0.1775 - val_accuracy: 0.9506
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 349/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9638 - val_loss: 0.1768 - val_accuracy: 0.9508
[ 0.  0.  0. ...  0.  0. -0.]
Sparsity at: 0.9718557475582269
Epoch 350/500
235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9638 - val_loss: 0.1769 - val_accuracy: 0.9511
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9718557475582269
Epoch 351/500
235/235 [==============================] - 3s 14ms/step - loss: 0.7794 - accuracy: 0.7436 - val_loss: 0.6255 - val_accuracy: 0.7861
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 352/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5830 - accuracy: 0.8131 - val_loss: 0.5424 - val_accuracy: 0.8350
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 353/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5435 - accuracy: 0.8322 - val_loss: 0.5166 - val_accuracy: 0.8435
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 354/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5245 - accuracy: 0.8393 - val_loss: 0.5008 - val_accuracy: 0.8482
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 355/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5131 - accuracy: 0.8425 - val_loss: 0.4896 - val_accuracy: 0.8509
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 356/500
235/235 [==============================] - 3s 14ms/step - loss: 0.5048 - accuracy: 0.8464 - val_loss: 0.4824 - val_accuracy: 0.8532
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 357/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4978 - accuracy: 0.8489 - val_loss: 0.4766 - val_accuracy: 0.8549
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 358/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4937 - accuracy: 0.8499 - val_loss: 0.4727 - val_accuracy: 0.8572
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 359/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4879 - accuracy: 0.8521 - val_loss: 0.4686 - val_accuracy: 0.8586
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 360/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4859 - accuracy: 0.8522 - val_loss: 0.4656 - val_accuracy: 0.8583
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 361/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4818 - accuracy: 0.8539 - val_loss: 0.4632 - val_accuracy: 0.8589
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 362/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4787 - accuracy: 0.8548 - val_loss: 0.4600 - val_accuracy: 0.8603
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 363/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4753 - accuracy: 0.8560 - val_loss: 0.4585 - val_accuracy: 0.8613
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 364/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4734 - accuracy: 0.8569 - val_loss: 0.4570 - val_accuracy: 0.8616
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 365/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4717 - accuracy: 0.8577 - val_loss: 0.4553 - val_accuracy: 0.8615
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 366/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4702 - accuracy: 0.8570 - val_loss: 0.4540 - val_accuracy: 0.8627
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 367/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4673 - accuracy: 0.8576 - val_loss: 0.4526 - val_accuracy: 0.8628
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 368/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4668 - accuracy: 0.8588 - val_loss: 0.4519 - val_accuracy: 0.8625
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 369/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4648 - accuracy: 0.8597 - val_loss: 0.4507 - val_accuracy: 0.8636
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 370/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4629 - accuracy: 0.8597 - val_loss: 0.4497 - val_accuracy: 0.8636
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 371/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4620 - accuracy: 0.8597 - val_loss: 0.4483 - val_accuracy: 0.8645
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 372/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4604 - accuracy: 0.8602 - val_loss: 0.4472 - val_accuracy: 0.8650
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 373/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4592 - accuracy: 0.8608 - val_loss: 0.4464 - val_accuracy: 0.8656
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 374/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4581 - accuracy: 0.8609 - val_loss: 0.4461 - val_accuracy: 0.8654
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 375/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4566 - accuracy: 0.8622 - val_loss: 0.4454 - val_accuracy: 0.8653
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 376/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4557 - accuracy: 0.8625 - val_loss: 0.4449 - val_accuracy: 0.8656
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 377/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4549 - accuracy: 0.8622 - val_loss: 0.4441 - val_accuracy: 0.8655
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 378/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4539 - accuracy: 0.8619 - val_loss: 0.4436 - val_accuracy: 0.8654
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 379/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4525 - accuracy: 0.8628 - val_loss: 0.4427 - val_accuracy: 0.8665
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 380/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4522 - accuracy: 0.8627 - val_loss: 0.4421 - val_accuracy: 0.8667
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 381/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4513 - accuracy: 0.8634 - val_loss: 0.4418 - val_accuracy: 0.8671
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 382/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4505 - accuracy: 0.8636 - val_loss: 0.4417 - val_accuracy: 0.8667
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 383/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4489 - accuracy: 0.8644 - val_loss: 0.4406 - val_accuracy: 0.8667
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 384/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4481 - accuracy: 0.8640 - val_loss: 0.4407 - val_accuracy: 0.8687
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 385/500
235/235 [==============================] - 3s 15ms/step - loss: 0.4475 - accuracy: 0.8642 - val_loss: 0.4403 - val_accuracy: 0.8689
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 386/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4471 - accuracy: 0.8642 - val_loss: 0.4401 - val_accuracy: 0.8680
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 387/500
235/235 [==============================] - 4s 15ms/step - loss: 0.4457 - accuracy: 0.8655 - val_loss: 0.4400 - val_accuracy: 0.8683
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 388/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4454 - accuracy: 0.8643 - val_loss: 0.4395 - val_accuracy: 0.8684
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 389/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4452 - accuracy: 0.8651 - val_loss: 0.4394 - val_accuracy: 0.8681
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 390/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4450 - accuracy: 0.8651 - val_loss: 0.4392 - val_accuracy: 0.8686
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 391/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4443 - accuracy: 0.8651 - val_loss: 0.4390 - val_accuracy: 0.8687
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 392/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4443 - accuracy: 0.8660 - val_loss: 0.4384 - val_accuracy: 0.8693
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 393/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4429 - accuracy: 0.8662 - val_loss: 0.4379 - val_accuracy: 0.8689
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 394/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4428 - accuracy: 0.8661 - val_loss: 0.4385 - val_accuracy: 0.8688
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9846243425995492
Epoch 395/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4427 - accuracy: 0.8665 - val_loss: 0.4379 - val_accuracy: 0.8686
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 396/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4424 - accuracy: 0.8655 - val_loss: 0.4378 - val_accuracy: 0.8685
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 397/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4424 - accuracy: 0.8663 - val_loss: 0.4378 - val_accuracy: 0.8689
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 398/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4414 - accuracy: 0.8668 - val_loss: 0.4372 - val_accuracy: 0.8691
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 399/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4407 - accuracy: 0.8670 - val_loss: 0.4371 - val_accuracy: 0.8692
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 400/500
235/235 [==============================] - 3s 14ms/step - loss: 0.4403 - accuracy: 0.8665 - val_loss: 0.4376 - val_accuracy: 0.8693
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9846243425995492
Epoch 401/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0932 - accuracy: 0.6444 - val_loss: 1.0447 - val_accuracy: 0.6236
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 402/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0273 - accuracy: 0.6478 - val_loss: 1.0126 - val_accuracy: 0.6531
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 403/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0194 - accuracy: 0.6477 - val_loss: 1.0073 - val_accuracy: 0.6527
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 404/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0164 - accuracy: 0.6484 - val_loss: 1.0054 - val_accuracy: 0.6536
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 405/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0145 - accuracy: 0.6482 - val_loss: 1.0046 - val_accuracy: 0.6534
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 406/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0138 - accuracy: 0.6489 - val_loss: 1.0034 - val_accuracy: 0.6536
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 407/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0118 - accuracy: 0.6490 - val_loss: 1.0015 - val_accuracy: 0.6554
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 408/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0117 - accuracy: 0.6494 - val_loss: 1.0008 - val_accuracy: 0.6543
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 409/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0090 - accuracy: 0.6496 - val_loss: 1.0017 - val_accuracy: 0.6549
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 410/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0088 - accuracy: 0.6496 - val_loss: 0.9998 - val_accuracy: 0.6554
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 411/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0067 - accuracy: 0.6505 - val_loss: 0.9982 - val_accuracy: 0.6554
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 412/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0061 - accuracy: 0.6502 - val_loss: 0.9971 - val_accuracy: 0.6555
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 413/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0044 - accuracy: 0.6500 - val_loss: 0.9961 - val_accuracy: 0.6556
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 414/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0050 - accuracy: 0.6503 - val_loss: 0.9949 - val_accuracy: 0.6553
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 415/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0041 - accuracy: 0.6499 - val_loss: 0.9947 - val_accuracy: 0.6555
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 416/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0025 - accuracy: 0.6512 - val_loss: 0.9940 - val_accuracy: 0.6565
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 417/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0017 - accuracy: 0.6505 - val_loss: 0.9932 - val_accuracy: 0.6555
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 418/500
235/235 [==============================] - 3s 15ms/step - loss: 1.0019 - accuracy: 0.6514 - val_loss: 0.9926 - val_accuracy: 0.6561
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 419/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0014 - accuracy: 0.6514 - val_loss: 0.9920 - val_accuracy: 0.6572
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 420/500
235/235 [==============================] - 3s 14ms/step - loss: 1.0012 - accuracy: 0.6518 - val_loss: 0.9917 - val_accuracy: 0.6562
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 421/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9995 - accuracy: 0.6519 - val_loss: 0.9914 - val_accuracy: 0.6571
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 422/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9995 - accuracy: 0.6511 - val_loss: 0.9909 - val_accuracy: 0.6564
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 423/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9995 - accuracy: 0.6519 - val_loss: 0.9903 - val_accuracy: 0.6565
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 424/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9986 - accuracy: 0.6522 - val_loss: 0.9899 - val_accuracy: 0.6560
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 425/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9981 - accuracy: 0.6519 - val_loss: 0.9895 - val_accuracy: 0.6554
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 426/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9983 - accuracy: 0.6521 - val_loss: 0.9888 - val_accuracy: 0.6558
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 427/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9967 - accuracy: 0.6524 - val_loss: 0.9885 - val_accuracy: 0.6552
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 428/500
235/235 [==============================] - 3s 15ms/step - loss: 0.9963 - accuracy: 0.6526 - val_loss: 0.9882 - val_accuracy: 0.6553
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 429/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9954 - accuracy: 0.6527 - val_loss: 0.9883 - val_accuracy: 0.6549
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 430/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9952 - accuracy: 0.6527 - val_loss: 0.9876 - val_accuracy: 0.6556
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 431/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9956 - accuracy: 0.6530 - val_loss: 0.9877 - val_accuracy: 0.6556
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 432/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9950 - accuracy: 0.6536 - val_loss: 0.9884 - val_accuracy: 0.6556
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 433/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9944 - accuracy: 0.6533 - val_loss: 0.9879 - val_accuracy: 0.6559
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 434/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9941 - accuracy: 0.6526 - val_loss: 0.9871 - val_accuracy: 0.6559
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 435/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9942 - accuracy: 0.6538 - val_loss: 0.9873 - val_accuracy: 0.6562
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 436/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9948 - accuracy: 0.6536 - val_loss: 0.9876 - val_accuracy: 0.6563
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 437/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9948 - accuracy: 0.6532 - val_loss: 0.9874 - val_accuracy: 0.6561
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 438/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9929 - accuracy: 0.6541 - val_loss: 0.9877 - val_accuracy: 0.6559
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 439/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9930 - accuracy: 0.6535 - val_loss: 0.9878 - val_accuracy: 0.6568
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 440/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9937 - accuracy: 0.6540 - val_loss: 0.9874 - val_accuracy: 0.6565
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 441/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9932 - accuracy: 0.6542 - val_loss: 0.9862 - val_accuracy: 0.6568
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 442/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9922 - accuracy: 0.6547 - val_loss: 0.9864 - val_accuracy: 0.6567
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 443/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9924 - accuracy: 0.6546 - val_loss: 0.9859 - val_accuracy: 0.6573
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 444/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9917 - accuracy: 0.6545 - val_loss: 0.9864 - val_accuracy: 0.6562
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 445/500
235/235 [==============================] - 3s 15ms/step - loss: 0.9920 - accuracy: 0.6540 - val_loss: 0.9855 - val_accuracy: 0.6580
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 446/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9915 - accuracy: 0.6543 - val_loss: 0.9862 - val_accuracy: 0.6563
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 447/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9925 - accuracy: 0.6540 - val_loss: 0.9848 - val_accuracy: 0.6576
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 448/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9910 - accuracy: 0.6544 - val_loss: 0.9850 - val_accuracy: 0.6576
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 449/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9905 - accuracy: 0.6544 - val_loss: 0.9843 - val_accuracy: 0.6580
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 450/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9905 - accuracy: 0.6536 - val_loss: 0.9860 - val_accuracy: 0.6569
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 451/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9910 - accuracy: 0.6539 - val_loss: 0.9847 - val_accuracy: 0.6576
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 452/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9902 - accuracy: 0.6524 - val_loss: 0.9837 - val_accuracy: 0.6577
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 453/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9898 - accuracy: 0.6538 - val_loss: 0.9832 - val_accuracy: 0.6573
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 454/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9896 - accuracy: 0.6546 - val_loss: 0.9822 - val_accuracy: 0.6587
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 455/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9898 - accuracy: 0.6537 - val_loss: 0.9828 - val_accuracy: 0.6586
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 456/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9898 - accuracy: 0.6522 - val_loss: 0.9822 - val_accuracy: 0.6590
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 457/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9891 - accuracy: 0.6527 - val_loss: 0.9819 - val_accuracy: 0.6590
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 458/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9892 - accuracy: 0.6525 - val_loss: 0.9820 - val_accuracy: 0.6581
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 459/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9888 - accuracy: 0.6526 - val_loss: 0.9809 - val_accuracy: 0.6582
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 460/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9877 - accuracy: 0.6526 - val_loss: 0.9811 - val_accuracy: 0.6587
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 461/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9889 - accuracy: 0.6525 - val_loss: 0.9812 - val_accuracy: 0.6587
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 462/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9885 - accuracy: 0.6514 - val_loss: 0.9810 - val_accuracy: 0.6577
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 463/500
235/235 [==============================] - 3s 15ms/step - loss: 0.9894 - accuracy: 0.6524 - val_loss: 0.9810 - val_accuracy: 0.6584
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 464/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9884 - accuracy: 0.6526 - val_loss: 0.9811 - val_accuracy: 0.6583
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 465/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9886 - accuracy: 0.6519 - val_loss: 0.9801 - val_accuracy: 0.6598
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 466/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9874 - accuracy: 0.6523 - val_loss: 0.9798 - val_accuracy: 0.6594
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 467/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9874 - accuracy: 0.6528 - val_loss: 0.9803 - val_accuracy: 0.6594
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 468/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9874 - accuracy: 0.6529 - val_loss: 0.9794 - val_accuracy: 0.6596
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 469/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9876 - accuracy: 0.6523 - val_loss: 0.9806 - val_accuracy: 0.6585
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 470/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9873 - accuracy: 0.6521 - val_loss: 0.9800 - val_accuracy: 0.6589
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 471/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9864 - accuracy: 0.6527 - val_loss: 0.9798 - val_accuracy: 0.6587
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 472/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9872 - accuracy: 0.6512 - val_loss: 0.9795 - val_accuracy: 0.6595
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 473/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9865 - accuracy: 0.6521 - val_loss: 0.9801 - val_accuracy: 0.6590
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 474/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9862 - accuracy: 0.6524 - val_loss: 0.9794 - val_accuracy: 0.6584
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 475/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9867 - accuracy: 0.6520 - val_loss: 0.9799 - val_accuracy: 0.6591
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 476/500
235/235 [==============================] - 4s 16ms/step - loss: 0.9864 - accuracy: 0.6531 - val_loss: 0.9799 - val_accuracy: 0.6583
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 477/500
235/235 [==============================] - 4s 15ms/step - loss: 0.9870 - accuracy: 0.6525 - val_loss: 0.9803 - val_accuracy: 0.6583
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 478/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9868 - accuracy: 0.6523 - val_loss: 0.9792 - val_accuracy: 0.6590
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 479/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9867 - accuracy: 0.6531 - val_loss: 0.9796 - val_accuracy: 0.6592
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 480/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9864 - accuracy: 0.6531 - val_loss: 0.9793 - val_accuracy: 0.6585
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 481/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9867 - accuracy: 0.6519 - val_loss: 0.9793 - val_accuracy: 0.6585
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 482/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9866 - accuracy: 0.6525 - val_loss: 0.9800 - val_accuracy: 0.6594
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 483/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9864 - accuracy: 0.6524 - val_loss: 0.9799 - val_accuracy: 0.6591
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 484/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9869 - accuracy: 0.6514 - val_loss: 0.9790 - val_accuracy: 0.6590
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 485/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9863 - accuracy: 0.6525 - val_loss: 0.9793 - val_accuracy: 0.6586
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 486/500
235/235 [==============================] - 4s 16ms/step - loss: 0.9862 - accuracy: 0.6526 - val_loss: 0.9788 - val_accuracy: 0.6594
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 487/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9861 - accuracy: 0.6527 - val_loss: 0.9792 - val_accuracy: 0.6589
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 488/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9856 - accuracy: 0.6529 - val_loss: 0.9792 - val_accuracy: 0.6589
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 489/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9861 - accuracy: 0.6528 - val_loss: 0.9791 - val_accuracy: 0.6595
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 490/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9862 - accuracy: 0.6526 - val_loss: 0.9788 - val_accuracy: 0.6593
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 491/500
235/235 [==============================] - 3s 15ms/step - loss: 0.9870 - accuracy: 0.6523 - val_loss: 0.9800 - val_accuracy: 0.6593
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 492/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9862 - accuracy: 0.6537 - val_loss: 0.9789 - val_accuracy: 0.6593
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 493/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9862 - accuracy: 0.6521 - val_loss: 0.9778 - val_accuracy: 0.6596
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 494/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9856 - accuracy: 0.6532 - val_loss: 0.9791 - val_accuracy: 0.6589
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 495/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9854 - accuracy: 0.6528 - val_loss: 0.9786 - val_accuracy: 0.6595
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 496/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9865 - accuracy: 0.6523 - val_loss: 0.9795 - val_accuracy: 0.6596
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893275732531931
Epoch 497/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9861 - accuracy: 0.6519 - val_loss: 0.9785 - val_accuracy: 0.6595
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 498/500
235/235 [==============================] - 3s 15ms/step - loss: 0.9874 - accuracy: 0.6520 - val_loss: 0.9788 - val_accuracy: 0.6585
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 499/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9858 - accuracy: 0.6528 - val_loss: 0.9784 - val_accuracy: 0.6598
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 500/500
235/235 [==============================] - 3s 14ms/step - loss: 0.9852 - accuracy: 0.6523 - val_loss: 0.9792 - val_accuracy: 0.6592
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893275732531931
Epoch 1/500
235/235 [==============================] - 3s 9ms/step - loss: 0.8513 - accuracy: 0.9007 - val_loss: 0.8256 - val_accuracy: 0.9034
[0.         0.         0.         ... 0.15788244 0.         0.14081757]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8415 - accuracy: 0.9017 - val_loss: 0.8244 - val_accuracy: 0.9042
[0.         0.         0.         ... 0.16220704 0.         0.13526146]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8404 - accuracy: 0.9020 - val_loss: 0.8236 - val_accuracy: 0.9044
[ 0.          0.          0.         ...  0.16280665 -0.
  0.13400939]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9018 - val_loss: 0.8226 - val_accuracy: 0.9049
[0.         0.         0.         ... 0.16244288 0.         0.13373683]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9019 - val_loss: 0.8230 - val_accuracy: 0.9046
[0.         0.         0.         ... 0.16208158 0.         0.13432963]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8229 - val_accuracy: 0.9049
[ 0.          0.          0.         ...  0.16135864 -0.
  0.13471374]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9018 - val_loss: 0.8230 - val_accuracy: 0.9047
[0.         0.         0.         ... 0.16059455 0.         0.13496135]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9020 - val_loss: 0.8226 - val_accuracy: 0.9046
[ 0.          0.          0.         ...  0.16012819 -0.
  0.13517989]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8387 - accuracy: 0.9016 - val_loss: 0.8224 - val_accuracy: 0.9048
[0.         0.         0.         ... 0.16003999 0.         0.13517018]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9020 - val_loss: 0.8230 - val_accuracy: 0.9046
[0.         0.         0.         ... 0.15942231 0.         0.13536632]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8389 - accuracy: 0.9019 - val_loss: 0.8232 - val_accuracy: 0.9047
[0.         0.         0.         ... 0.1592013  0.         0.13542442]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8387 - accuracy: 0.9021 - val_loss: 0.8228 - val_accuracy: 0.9047
[0.         0.         0.         ... 0.15921855 0.         0.13534898]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8387 - accuracy: 0.9020 - val_loss: 0.8228 - val_accuracy: 0.9051
[ 0.          0.          0.         ...  0.15913102 -0.
  0.1354428 ]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8386 - accuracy: 0.9020 - val_loss: 0.8222 - val_accuracy: 0.9046
[ 0.          0.          0.         ...  0.15884258 -0.
  0.13563946]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8387 - accuracy: 0.9023 - val_loss: 0.8223 - val_accuracy: 0.9047
[0.         0.         0.         ... 0.15885976 0.         0.13555853]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9019 - val_loss: 0.8225 - val_accuracy: 0.9046
[0.         0.         0.         ... 0.15848298 0.         0.13537033]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9021 - val_loss: 0.8222 - val_accuracy: 0.9047
[0.         0.         0.         ... 0.15849675 0.         0.13540642]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9018 - val_loss: 0.8222 - val_accuracy: 0.9047
[0.         0.         0.         ... 0.15827875 0.         0.13552135]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9020 - val_loss: 0.8223 - val_accuracy: 0.9049
[0.         0.         0.         ... 0.15845625 0.         0.13551594]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9020 - val_loss: 0.8220 - val_accuracy: 0.9049
[0.         0.         0.         ... 0.1583716  0.         0.13615865]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9020 - val_loss: 0.8218 - val_accuracy: 0.9043
[0.         0.         0.         ... 0.15836339 0.         0.13612577]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9020 - val_loss: 0.8224 - val_accuracy: 0.9046
[0.         0.         0.         ... 0.15808155 0.         0.13636185]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8382 - accuracy: 0.9021 - val_loss: 0.8226 - val_accuracy: 0.9052
[0.         0.         0.         ... 0.15804927 0.         0.13609779]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9020 - val_loss: 0.8215 - val_accuracy: 0.9046
[ 0.          0.          0.         ...  0.158039   -0.
  0.13599962]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9021 - val_loss: 0.8222 - val_accuracy: 0.9052
[0.         0.         0.         ... 0.15787165 0.         0.1357117 ]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9021 - val_loss: 0.8224 - val_accuracy: 0.9047
[ 0.          0.          0.         ...  0.15788928 -0.
  0.13574402]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9018 - val_loss: 0.8212 - val_accuracy: 0.9050
[0.         0.         0.         ... 0.15784638 0.         0.1357661 ]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9023 - val_loss: 0.8218 - val_accuracy: 0.9049
[0.         0.         0.         ... 0.15775436 0.         0.13552889]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9019 - val_loss: 0.8217 - val_accuracy: 0.9049
[0.         0.         0.         ... 0.15762708 0.         0.13496895]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9020 - val_loss: 0.8223 - val_accuracy: 0.9047
[0.         0.         0.         ... 0.15731747 0.         0.13471764]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9020 - val_loss: 0.8222 - val_accuracy: 0.9047
[ 0.          0.          0.         ...  0.15731616 -0.
  0.13463095]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9022 - val_loss: 0.8223 - val_accuracy: 0.9048
[0.         0.         0.         ... 0.15722875 0.         0.13452779]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9021 - val_loss: 0.8224 - val_accuracy: 0.9048
[0.         0.         0.         ... 0.15719621 0.         0.13406663]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9018 - val_loss: 0.8220 - val_accuracy: 0.9041
[0.         0.         0.         ... 0.15694052 0.         0.1337055 ]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9017 - val_loss: 0.8225 - val_accuracy: 0.9049
[0.         0.         0.         ... 0.15687607 0.         0.1334594 ]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9021 - val_loss: 0.8224 - val_accuracy: 0.9048
[0.         0.         0.         ... 0.15664308 0.         0.13329028]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9021 - val_loss: 0.8225 - val_accuracy: 0.9045
[ 0.         0.         0.        ...  0.1565845 -0.         0.1331554]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8379 - accuracy: 0.9021 - val_loss: 0.8222 - val_accuracy: 0.9043
[0.         0.         0.         ... 0.15663806 0.         0.13260451]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9022 - val_loss: 0.8230 - val_accuracy: 0.9050
[ 0.          0.          0.         ...  0.15649739 -0.
  0.13243528]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8382 - accuracy: 0.9018 - val_loss: 0.8223 - val_accuracy: 0.9043
[ 0.          0.          0.         ...  0.15614049 -0.
  0.13218908]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8379 - accuracy: 0.9019 - val_loss: 0.8224 - val_accuracy: 0.9043
[0.         0.         0.         ... 0.15599233 0.         0.1317357 ]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8378 - accuracy: 0.9019 - val_loss: 0.8221 - val_accuracy: 0.9047
[0.         0.         0.         ... 0.15586543 0.         0.13152066]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9015 - val_loss: 0.8222 - val_accuracy: 0.9041
[0.         0.         0.         ... 0.1557162  0.         0.13125826]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8380 - accuracy: 0.9020 - val_loss: 0.8224 - val_accuracy: 0.9048
[0.         0.         0.         ... 0.15577984 0.         0.1311198 ]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8380 - accuracy: 0.9020 - val_loss: 0.8225 - val_accuracy: 0.9049
[0.         0.         0.         ... 0.15565813 0.         0.13085525]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8382 - accuracy: 0.9020 - val_loss: 0.8223 - val_accuracy: 0.9043
[0.         0.         0.         ... 0.15530625 0.         0.13077942]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9022 - val_loss: 0.8209 - val_accuracy: 0.9045
[0.         0.         0.         ... 0.15526164 0.         0.1305427 ]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8378 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9045
[0.         0.         0.         ... 0.1548658  0.         0.13041286]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8379 - accuracy: 0.9020 - val_loss: 0.8222 - val_accuracy: 0.9045
[ 0.          0.          0.         ...  0.15488583 -0.
  0.13032916]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8378 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9040
[ 0.          0.          0.         ...  0.15472884 -0.
  0.1303137 ]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8639 - accuracy: 0.9017 - val_loss: 0.8408 - val_accuracy: 0.9073
[0.         0.         0.         ... 0.13868643 0.         0.13391747]
Sparsity at: 0.6457718615879828
Epoch 52/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9028 - val_loss: 0.8397 - val_accuracy: 0.9070
[0.         0.         0.         ... 0.12898542 0.         0.13097095]
Sparsity at: 0.6457718615879828
Epoch 53/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9029 - val_loss: 0.8392 - val_accuracy: 0.9074
[0.         0.         0.         ... 0.12436873 0.         0.12697442]
Sparsity at: 0.6457718615879828
Epoch 54/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9029 - val_loss: 0.8390 - val_accuracy: 0.9077
[0.         0.         0.         ... 0.12196597 0.         0.12345385]
Sparsity at: 0.6457718615879828
Epoch 55/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9030 - val_loss: 0.8388 - val_accuracy: 0.9079
[ 0.          0.          0.         ...  0.12046788 -0.
  0.12060314]
Sparsity at: 0.6457718615879828
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9032 - val_loss: 0.8385 - val_accuracy: 0.9080
[0.         0.         0.         ... 0.11946367 0.         0.11828665]
Sparsity at: 0.6457718615879828
Epoch 57/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8571 - accuracy: 0.9033 - val_loss: 0.8387 - val_accuracy: 0.9082
[ 0.          0.          0.         ...  0.11877161 -0.
  0.11631727]
Sparsity at: 0.6457718615879828
Epoch 58/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8572 - accuracy: 0.9031 - val_loss: 0.8386 - val_accuracy: 0.9083
[0.         0.         0.         ... 0.11838406 0.         0.11472747]
Sparsity at: 0.6457718615879828
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8571 - accuracy: 0.9036 - val_loss: 0.8387 - val_accuracy: 0.9082
[0.         0.         0.         ... 0.11800242 0.         0.11338073]
Sparsity at: 0.6457718615879828
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8570 - accuracy: 0.9034 - val_loss: 0.8385 - val_accuracy: 0.9082
[0.         0.         0.         ... 0.11756724 0.         0.11241508]
Sparsity at: 0.6457718615879828
Epoch 61/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9031 - val_loss: 0.8385 - val_accuracy: 0.9083
[0.         0.         0.         ... 0.117554   0.         0.11156002]
Sparsity at: 0.6457718615879828
Epoch 62/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8570 - accuracy: 0.9031 - val_loss: 0.8386 - val_accuracy: 0.9082
[0.         0.         0.         ... 0.1173941  0.         0.11054305]
Sparsity at: 0.6457718615879828
Epoch 63/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8570 - accuracy: 0.9032 - val_loss: 0.8384 - val_accuracy: 0.9083
[ 0.          0.          0.         ...  0.11711521 -0.
  0.11003958]
Sparsity at: 0.6457718615879828
Epoch 64/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9032 - val_loss: 0.8384 - val_accuracy: 0.9084
[0.         0.         0.         ... 0.11699417 0.         0.1093235 ]
Sparsity at: 0.6457718615879828
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9031 - val_loss: 0.8381 - val_accuracy: 0.9078
[0.         0.         0.         ... 0.11691038 0.         0.10889148]
Sparsity at: 0.6457718615879828
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9029 - val_loss: 0.8381 - val_accuracy: 0.9078
[0.         0.         0.         ... 0.11658615 0.         0.10839706]
Sparsity at: 0.6457718615879828
Epoch 67/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9033 - val_loss: 0.8385 - val_accuracy: 0.9082
[0.         0.         0.         ... 0.11661546 0.         0.1080367 ]
Sparsity at: 0.6457718615879828
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9031 - val_loss: 0.8384 - val_accuracy: 0.9087
[0.         0.         0.         ... 0.1165145  0.         0.10772243]
Sparsity at: 0.6457718615879828
Epoch 69/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9034 - val_loss: 0.8382 - val_accuracy: 0.9079
[0.         0.         0.         ... 0.11651203 0.         0.10758363]
Sparsity at: 0.6457718615879828
Epoch 70/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9032 - val_loss: 0.8384 - val_accuracy: 0.9081
[0.         0.         0.         ... 0.11649998 0.         0.10697347]
Sparsity at: 0.6457718615879828
Epoch 71/500
235/235 [==============================] - 2s 8ms/step - loss: 0.8568 - accuracy: 0.9033 - val_loss: 0.8384 - val_accuracy: 0.9081
[0.         0.         0.         ... 0.11632838 0.         0.10688186]
Sparsity at: 0.6457718615879828
Epoch 72/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9032 - val_loss: 0.8382 - val_accuracy: 0.9083
[0.         0.         0.         ... 0.11620827 0.         0.10663418]
Sparsity at: 0.6457718615879828
Epoch 73/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9032 - val_loss: 0.8384 - val_accuracy: 0.9085
[0.         0.         0.         ... 0.11600775 0.         0.10657653]
Sparsity at: 0.6457718615879828
Epoch 74/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9035 - val_loss: 0.8383 - val_accuracy: 0.9079
[0.         0.         0.         ... 0.11608796 0.         0.10634568]
Sparsity at: 0.6457718615879828
Epoch 75/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9030 - val_loss: 0.8386 - val_accuracy: 0.9092
[0.         0.         0.         ... 0.11618339 0.         0.1061451 ]
Sparsity at: 0.6457718615879828
Epoch 76/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8384 - val_accuracy: 0.9086
[0.         0.         0.         ... 0.11608247 0.         0.10617648]
Sparsity at: 0.6457718615879828
Epoch 77/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9032 - val_loss: 0.8381 - val_accuracy: 0.9083
[0.         0.         0.         ... 0.11605187 0.         0.10620727]
Sparsity at: 0.6457718615879828
Epoch 78/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9031 - val_loss: 0.8385 - val_accuracy: 0.9090
[ 0.         0.         0.        ...  0.1157997 -0.         0.1060439]
Sparsity at: 0.6457718615879828
Epoch 79/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9029 - val_loss: 0.8385 - val_accuracy: 0.9084
[0.         0.         0.         ... 0.11595087 0.         0.10599548]
Sparsity at: 0.6457718615879828
Epoch 80/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9030 - val_loss: 0.8382 - val_accuracy: 0.9085
[0.         0.         0.         ... 0.11594436 0.         0.10598035]
Sparsity at: 0.6457718615879828
Epoch 81/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9030 - val_loss: 0.8385 - val_accuracy: 0.9087
[ 0.          0.          0.         ...  0.1158568  -0.
  0.10581444]
Sparsity at: 0.6457718615879828
Epoch 82/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9029 - val_loss: 0.8383 - val_accuracy: 0.9085
[ 0.          0.          0.         ...  0.11588375 -0.
  0.10575263]
Sparsity at: 0.6457718615879828
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8384 - val_accuracy: 0.9086
[0.         0.         0.         ... 0.11580482 0.         0.10548916]
Sparsity at: 0.6457718615879828
Epoch 84/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9030 - val_loss: 0.8381 - val_accuracy: 0.9086
[0.         0.         0.         ... 0.11591887 0.         0.10546894]
Sparsity at: 0.6457718615879828
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9030 - val_loss: 0.8384 - val_accuracy: 0.9080
[0.         0.         0.         ... 0.11551363 0.         0.10553424]
Sparsity at: 0.6457718615879828
Epoch 86/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8379 - val_accuracy: 0.9085
[0.         0.         0.         ... 0.11585085 0.         0.10542686]
Sparsity at: 0.6457718615879828
Epoch 87/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8565 - accuracy: 0.9032 - val_loss: 0.8379 - val_accuracy: 0.9080
[ 0.          0.          0.         ...  0.11561348 -0.
  0.10540969]
Sparsity at: 0.6457718615879828
Epoch 88/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8385 - val_accuracy: 0.9084
[0.         0.         0.         ... 0.11544598 0.         0.10519272]
Sparsity at: 0.6457718615879828
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8385 - val_accuracy: 0.9084
[0.         0.         0.         ... 0.11551893 0.         0.10559369]
Sparsity at: 0.6457718615879828
Epoch 90/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9031 - val_loss: 0.8386 - val_accuracy: 0.9091
[0.         0.         0.         ... 0.11554836 0.         0.10530648]
Sparsity at: 0.6457718615879828
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9032 - val_loss: 0.8381 - val_accuracy: 0.9083
[0.         0.         0.         ... 0.11572839 0.         0.10527035]
Sparsity at: 0.6457718615879828
Epoch 92/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9028 - val_loss: 0.8380 - val_accuracy: 0.9081
[0.         0.         0.         ... 0.11558791 0.         0.10552419]
Sparsity at: 0.6457718615879828
Epoch 93/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8565 - accuracy: 0.9033 - val_loss: 0.8381 - val_accuracy: 0.9083
[ 0.          0.          0.         ...  0.11559746 -0.
  0.10545988]
Sparsity at: 0.6457718615879828
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9028 - val_loss: 0.8384 - val_accuracy: 0.9086
[ 0.          0.          0.         ...  0.1156354  -0.
  0.10536512]
Sparsity at: 0.6457718615879828
Epoch 95/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9031 - val_loss: 0.8382 - val_accuracy: 0.9088
[0.         0.         0.         ... 0.11572543 0.         0.10520094]
Sparsity at: 0.6457718615879828
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9032 - val_loss: 0.8386 - val_accuracy: 0.9083
[0.        0.        0.        ... 0.1154372 0.        0.1053072]
Sparsity at: 0.6457718615879828
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9030 - val_loss: 0.8384 - val_accuracy: 0.9088
[ 0.          0.          0.         ...  0.1155977  -0.
  0.10523844]
Sparsity at: 0.6457718615879828
Epoch 98/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9031 - val_loss: 0.8382 - val_accuracy: 0.9084
[ 0.          0.          0.         ...  0.11549519 -0.
  0.10525094]
Sparsity at: 0.6457718615879828
Epoch 99/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9031 - val_loss: 0.8384 - val_accuracy: 0.9086
[0.         0.         0.         ... 0.11548864 0.         0.10507528]
Sparsity at: 0.6457718615879828
Epoch 100/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9028 - val_loss: 0.8383 - val_accuracy: 0.9086
[0.         0.         0.         ... 0.11525078 0.         0.1050217 ]
Sparsity at: 0.6457718615879828
Epoch 101/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9045 - accuracy: 0.9006 - val_loss: 0.8844 - val_accuracy: 0.9032
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 102/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8967 - accuracy: 0.9023 - val_loss: 0.8828 - val_accuracy: 0.9034
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 103/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8959 - accuracy: 0.9026 - val_loss: 0.8825 - val_accuracy: 0.9037
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 104/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.9025 - val_loss: 0.8823 - val_accuracy: 0.9035
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8953 - accuracy: 0.9028 - val_loss: 0.8817 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8951 - accuracy: 0.9032 - val_loss: 0.8818 - val_accuracy: 0.9045
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8950 - accuracy: 0.9026 - val_loss: 0.8816 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 108/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8949 - accuracy: 0.9028 - val_loss: 0.8818 - val_accuracy: 0.9042
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 109/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8948 - accuracy: 0.9026 - val_loss: 0.8817 - val_accuracy: 0.9042
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8948 - accuracy: 0.9028 - val_loss: 0.8815 - val_accuracy: 0.9040
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8947 - accuracy: 0.9028 - val_loss: 0.8816 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 112/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8946 - accuracy: 0.9028 - val_loss: 0.8815 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 113/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8947 - accuracy: 0.9025 - val_loss: 0.8814 - val_accuracy: 0.9045
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 114/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9026 - val_loss: 0.8813 - val_accuracy: 0.9045
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 115/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9025 - val_loss: 0.8818 - val_accuracy: 0.9041
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9027 - val_loss: 0.8812 - val_accuracy: 0.9045
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 117/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9029 - val_loss: 0.8812 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 118/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9030 - val_loss: 0.8810 - val_accuracy: 0.9045
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9027 - val_loss: 0.8811 - val_accuracy: 0.9040
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9027 - val_loss: 0.8811 - val_accuracy: 0.9038
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 121/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9027 - val_loss: 0.8811 - val_accuracy: 0.9046
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 122/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8813 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 123/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9028 - val_loss: 0.8813 - val_accuracy: 0.9042
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 124/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9041
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 125/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 126/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9031 - val_loss: 0.8809 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 127/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9032 - val_loss: 0.8814 - val_accuracy: 0.9042
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 129/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8810 - val_accuracy: 0.9046
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9029 - val_loss: 0.8811 - val_accuracy: 0.9046
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9027 - val_loss: 0.8811 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 132/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9027 - val_loss: 0.8810 - val_accuracy: 0.9045
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 133/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8813 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 134/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 135/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9029 - val_loss: 0.8813 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 136/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 137/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9029 - val_loss: 0.8811 - val_accuracy: 0.9047
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 138/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9048
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 139/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9030 - val_loss: 0.8810 - val_accuracy: 0.9045
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 140/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8809 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 141/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9030 - val_loss: 0.8810 - val_accuracy: 0.9047
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9028 - val_loss: 0.8809 - val_accuracy: 0.9043
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 143/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9029 - val_loss: 0.8809 - val_accuracy: 0.9045
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 144/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9042
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 146/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8815 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 147/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9029 - val_loss: 0.8810 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 148/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9031 - val_loss: 0.8808 - val_accuracy: 0.9047
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9029 - val_loss: 0.8810 - val_accuracy: 0.9047
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 150/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8811 - val_accuracy: 0.9044
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.7594051770386266
Epoch 151/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9944 - accuracy: 0.8967 - val_loss: 0.9566 - val_accuracy: 0.9038
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 152/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9693 - accuracy: 0.9014 - val_loss: 0.9535 - val_accuracy: 0.9037
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 153/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9670 - accuracy: 0.9020 - val_loss: 0.9521 - val_accuracy: 0.9038
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 154/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9658 - accuracy: 0.9018 - val_loss: 0.9513 - val_accuracy: 0.9036
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 155/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9650 - accuracy: 0.9017 - val_loss: 0.9509 - val_accuracy: 0.9033
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 156/500
235/235 [==============================] - 2s 10ms/step - loss: 0.9644 - accuracy: 0.9017 - val_loss: 0.9504 - val_accuracy: 0.9035
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9640 - accuracy: 0.9014 - val_loss: 0.9500 - val_accuracy: 0.9040
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 158/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9635 - accuracy: 0.9016 - val_loss: 0.9496 - val_accuracy: 0.9037
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 159/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9631 - accuracy: 0.9018 - val_loss: 0.9495 - val_accuracy: 0.9037
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9629 - accuracy: 0.9018 - val_loss: 0.9492 - val_accuracy: 0.9039
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9628 - accuracy: 0.9017 - val_loss: 0.9491 - val_accuracy: 0.9036
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9626 - accuracy: 0.9016 - val_loss: 0.9492 - val_accuracy: 0.9035
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9624 - accuracy: 0.9016 - val_loss: 0.9490 - val_accuracy: 0.9035
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 164/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9623 - accuracy: 0.9015 - val_loss: 0.9487 - val_accuracy: 0.9035
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 165/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9621 - accuracy: 0.9016 - val_loss: 0.9488 - val_accuracy: 0.9033
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9620 - accuracy: 0.9017 - val_loss: 0.9490 - val_accuracy: 0.9033
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9620 - accuracy: 0.9015 - val_loss: 0.9485 - val_accuracy: 0.9035
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 168/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9619 - accuracy: 0.9017 - val_loss: 0.9484 - val_accuracy: 0.9033
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9618 - accuracy: 0.9014 - val_loss: 0.9483 - val_accuracy: 0.9033
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9617 - accuracy: 0.9015 - val_loss: 0.9483 - val_accuracy: 0.9034
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9617 - accuracy: 0.9015 - val_loss: 0.9481 - val_accuracy: 0.9035
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 172/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9616 - accuracy: 0.9014 - val_loss: 0.9485 - val_accuracy: 0.9035
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9615 - accuracy: 0.9016 - val_loss: 0.9484 - val_accuracy: 0.9030
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9615 - accuracy: 0.9015 - val_loss: 0.9483 - val_accuracy: 0.9037
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9615 - accuracy: 0.9017 - val_loss: 0.9483 - val_accuracy: 0.9036
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 176/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9614 - accuracy: 0.9014 - val_loss: 0.9479 - val_accuracy: 0.9034
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9614 - accuracy: 0.9012 - val_loss: 0.9483 - val_accuracy: 0.9030
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9615 - accuracy: 0.9014 - val_loss: 0.9480 - val_accuracy: 0.9031
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9483 - val_accuracy: 0.9037
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 180/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9614 - accuracy: 0.9015 - val_loss: 0.9481 - val_accuracy: 0.9030
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 181/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9480 - val_accuracy: 0.9032
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 182/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9017 - val_loss: 0.9478 - val_accuracy: 0.9032
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 183/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9015 - val_loss: 0.9482 - val_accuracy: 0.9031
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 184/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9479 - val_accuracy: 0.9031
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 185/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9016 - val_loss: 0.9482 - val_accuracy: 0.9033
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 186/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9479 - val_accuracy: 0.9031
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 187/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9013 - val_loss: 0.9481 - val_accuracy: 0.9031
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9015 - val_loss: 0.9481 - val_accuracy: 0.9033
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 189/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9014 - val_loss: 0.9479 - val_accuracy: 0.9029
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 190/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9016 - val_loss: 0.9480 - val_accuracy: 0.9028
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 191/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9476 - val_accuracy: 0.9029
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 192/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9013 - val_loss: 0.9479 - val_accuracy: 0.9028
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9017 - val_loss: 0.9479 - val_accuracy: 0.9028
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 194/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9014 - val_loss: 0.9479 - val_accuracy: 0.9032
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 195/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9013 - val_loss: 0.9481 - val_accuracy: 0.9032
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9611 - accuracy: 0.9014 - val_loss: 0.9481 - val_accuracy: 0.9031
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 197/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9015 - val_loss: 0.9478 - val_accuracy: 0.9032
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9014 - val_loss: 0.9477 - val_accuracy: 0.9029
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 199/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9016 - val_loss: 0.9480 - val_accuracy: 0.9033
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9013 - val_loss: 0.9480 - val_accuracy: 0.9026
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.8448061963519313
Epoch 201/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0908 - accuracy: 0.8862 - val_loss: 1.0428 - val_accuracy: 0.8967
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 202/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0568 - accuracy: 0.8956 - val_loss: 1.0376 - val_accuracy: 0.8990
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 203/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0535 - accuracy: 0.8967 - val_loss: 1.0361 - val_accuracy: 0.8994
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 204/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0523 - accuracy: 0.8968 - val_loss: 1.0353 - val_accuracy: 0.9002
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0516 - accuracy: 0.8970 - val_loss: 1.0348 - val_accuracy: 0.9003
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0512 - accuracy: 0.8972 - val_loss: 1.0344 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 207/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0509 - accuracy: 0.8972 - val_loss: 1.0343 - val_accuracy: 0.9005
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0506 - accuracy: 0.8972 - val_loss: 1.0340 - val_accuracy: 0.9006
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 209/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0504 - accuracy: 0.8974 - val_loss: 1.0339 - val_accuracy: 0.9005
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0502 - accuracy: 0.8976 - val_loss: 1.0337 - val_accuracy: 0.9006
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 211/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0501 - accuracy: 0.8976 - val_loss: 1.0336 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 212/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0500 - accuracy: 0.8978 - val_loss: 1.0335 - val_accuracy: 0.9005
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 213/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0500 - accuracy: 0.8977 - val_loss: 1.0334 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 214/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0499 - accuracy: 0.8979 - val_loss: 1.0333 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 215/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0498 - accuracy: 0.8979 - val_loss: 1.0332 - val_accuracy: 0.9011
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 216/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0497 - accuracy: 0.8978 - val_loss: 1.0331 - val_accuracy: 0.9008
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 217/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0497 - accuracy: 0.8980 - val_loss: 1.0330 - val_accuracy: 0.9011
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 218/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0497 - accuracy: 0.8977 - val_loss: 1.0331 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 219/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8977 - val_loss: 1.0330 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 220/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8979 - val_loss: 1.0331 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 221/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8979 - val_loss: 1.0330 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 222/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8980 - val_loss: 1.0329 - val_accuracy: 0.9011
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 223/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8979 - val_loss: 1.0330 - val_accuracy: 0.9008
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 224/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8980 - val_loss: 1.0329 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 225/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 226/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 227/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8978 - val_loss: 1.0330 - val_accuracy: 0.9007
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 228/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0330 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 229/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 230/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9008
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 231/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9008
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 232/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 233/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9008
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 234/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9006
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 235/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 236/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 237/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0327 - val_accuracy: 0.9007
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 238/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8977 - val_loss: 1.0328 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 239/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0328 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 240/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 241/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8977 - val_loss: 1.0329 - val_accuracy: 0.9007
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 242/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 243/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8976 - val_loss: 1.0328 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 244/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8980 - val_loss: 1.0328 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 245/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 246/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9009
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 247/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8980 - val_loss: 1.0327 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 248/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0328 - val_accuracy: 0.9008
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 249/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 250/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8978 - val_loss: 1.0328 - val_accuracy: 0.9010
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059649946351931
Epoch 251/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1677 - accuracy: 0.8843 - val_loss: 1.1221 - val_accuracy: 0.8967
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9469890021459227
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1374 - accuracy: 0.8920 - val_loss: 1.1163 - val_accuracy: 0.8970
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 253/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1336 - accuracy: 0.8921 - val_loss: 1.1141 - val_accuracy: 0.8968
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 254/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1317 - accuracy: 0.8923 - val_loss: 1.1127 - val_accuracy: 0.8966
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1306 - accuracy: 0.8921 - val_loss: 1.1119 - val_accuracy: 0.8964
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 256/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1299 - accuracy: 0.8923 - val_loss: 1.1115 - val_accuracy: 0.8963
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 257/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1295 - accuracy: 0.8921 - val_loss: 1.1110 - val_accuracy: 0.8966
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 258/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1291 - accuracy: 0.8921 - val_loss: 1.1108 - val_accuracy: 0.8965
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1288 - accuracy: 0.8921 - val_loss: 1.1106 - val_accuracy: 0.8966
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1286 - accuracy: 0.8922 - val_loss: 1.1105 - val_accuracy: 0.8966
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 261/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1285 - accuracy: 0.8921 - val_loss: 1.1103 - val_accuracy: 0.8970
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 262/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1283 - accuracy: 0.8924 - val_loss: 1.1102 - val_accuracy: 0.8972
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1282 - accuracy: 0.8923 - val_loss: 1.1101 - val_accuracy: 0.8972
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 264/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1282 - accuracy: 0.8924 - val_loss: 1.1101 - val_accuracy: 0.8970
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 265/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1281 - accuracy: 0.8925 - val_loss: 1.1099 - val_accuracy: 0.8970
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1281 - accuracy: 0.8926 - val_loss: 1.1099 - val_accuracy: 0.8973
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 267/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1280 - accuracy: 0.8926 - val_loss: 1.1098 - val_accuracy: 0.8973
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1279 - accuracy: 0.8927 - val_loss: 1.1097 - val_accuracy: 0.8970
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1279 - accuracy: 0.8926 - val_loss: 1.1098 - val_accuracy: 0.8972
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1279 - accuracy: 0.8925 - val_loss: 1.1097 - val_accuracy: 0.8973
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1279 - accuracy: 0.8926 - val_loss: 1.1097 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 272/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1098 - val_accuracy: 0.8969
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 273/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1097 - val_accuracy: 0.8973
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 274/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1097 - val_accuracy: 0.8971
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 275/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8926 - val_loss: 1.1096 - val_accuracy: 0.8974
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 276/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8928 - val_loss: 1.1096 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8973
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 279/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8926 - val_loss: 1.1097 - val_accuracy: 0.8973
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 282/500
235/235 [==============================] - 3s 11ms/step - loss: 1.1278 - accuracy: 0.8926 - val_loss: 1.1096 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8927 - val_loss: 1.1095 - val_accuracy: 0.8978
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 284/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8926 - val_loss: 1.1096 - val_accuracy: 0.8972
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8972
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 286/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1097 - val_accuracy: 0.8971
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 287/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1097 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 288/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1095 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1096 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 290/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 291/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1097 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 292/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 293/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1097 - val_accuracy: 0.8971
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 294/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 295/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1096 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 297/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8930 - val_loss: 1.1096 - val_accuracy: 0.8973
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 298/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8975
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 299/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8977
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 300/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8926 - val_loss: 1.1096 - val_accuracy: 0.8976
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9469890021459227
Epoch 301/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2805 - accuracy: 0.8674 - val_loss: 1.2165 - val_accuracy: 0.8816
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716503487124464
Epoch 302/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2289 - accuracy: 0.8792 - val_loss: 1.2112 - val_accuracy: 0.8823
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 303/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2265 - accuracy: 0.8797 - val_loss: 1.2100 - val_accuracy: 0.8831
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 304/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2257 - accuracy: 0.8801 - val_loss: 1.2094 - val_accuracy: 0.8838
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716503487124464
Epoch 305/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2251 - accuracy: 0.8802 - val_loss: 1.2090 - val_accuracy: 0.8841
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 306/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2248 - accuracy: 0.8804 - val_loss: 1.2087 - val_accuracy: 0.8841
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716503487124464
Epoch 307/500
235/235 [==============================] - 2s 10ms/step - loss: 1.2245 - accuracy: 0.8803 - val_loss: 1.2084 - val_accuracy: 0.8840
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9716503487124464
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2242 - accuracy: 0.8801 - val_loss: 1.2082 - val_accuracy: 0.8836
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 309/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2240 - accuracy: 0.8799 - val_loss: 1.2080 - val_accuracy: 0.8836
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 310/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2238 - accuracy: 0.8800 - val_loss: 1.2077 - val_accuracy: 0.8838
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 311/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2234 - accuracy: 0.8800 - val_loss: 1.2071 - val_accuracy: 0.8839
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 312/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2230 - accuracy: 0.8802 - val_loss: 1.2066 - val_accuracy: 0.8842
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 313/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2227 - accuracy: 0.8804 - val_loss: 1.2063 - val_accuracy: 0.8846
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 314/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2224 - accuracy: 0.8806 - val_loss: 1.2061 - val_accuracy: 0.8848
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 315/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2223 - accuracy: 0.8805 - val_loss: 1.2059 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 316/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2222 - accuracy: 0.8805 - val_loss: 1.2058 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 317/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2221 - accuracy: 0.8806 - val_loss: 1.2057 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 318/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2221 - accuracy: 0.8805 - val_loss: 1.2056 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 319/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2220 - accuracy: 0.8806 - val_loss: 1.2056 - val_accuracy: 0.8851
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 320/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2220 - accuracy: 0.8804 - val_loss: 1.2055 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 321/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2220 - accuracy: 0.8806 - val_loss: 1.2055 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 322/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2055 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 323/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2055 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 324/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2055 - val_accuracy: 0.8853
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 325/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2055 - val_accuracy: 0.8854
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 326/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8807 - val_loss: 1.2055 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 327/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8807 - val_loss: 1.2054 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8807 - val_loss: 1.2054 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 330/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 332/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8852
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8803 - val_loss: 1.2054 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 334/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8846
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 335/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2053 - val_accuracy: 0.8849
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 336/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8851
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 337/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8849
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2053 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8846
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 341/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8846
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 342/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2053 - val_accuracy: 0.8847
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 343/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8847
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 344/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8847
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 345/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8849
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2053 - val_accuracy: 0.8846
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 347/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2053 - val_accuracy: 0.8849
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2053 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8804 - val_loss: 1.2053 - val_accuracy: 0.8851
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8850
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9716503487124464
Epoch 351/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5134 - accuracy: 0.7019 - val_loss: 1.4537 - val_accuracy: 0.7037
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 352/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4632 - accuracy: 0.7056 - val_loss: 1.4420 - val_accuracy: 0.7029
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 353/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4576 - accuracy: 0.7054 - val_loss: 1.4395 - val_accuracy: 0.7032
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9844923551502146
Epoch 354/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4562 - accuracy: 0.7055 - val_loss: 1.4385 - val_accuracy: 0.7030
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9844923551502146
Epoch 355/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4556 - accuracy: 0.7054 - val_loss: 1.4381 - val_accuracy: 0.7034
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 356/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4552 - accuracy: 0.7053 - val_loss: 1.4378 - val_accuracy: 0.7035
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 357/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4550 - accuracy: 0.7055 - val_loss: 1.4376 - val_accuracy: 0.7259
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4549 - accuracy: 0.7054 - val_loss: 1.4374 - val_accuracy: 0.7259
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 359/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4548 - accuracy: 0.7056 - val_loss: 1.4373 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 360/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4547 - accuracy: 0.7055 - val_loss: 1.4372 - val_accuracy: 0.7257
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4546 - accuracy: 0.7053 - val_loss: 1.4372 - val_accuracy: 0.7258
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 362/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4546 - accuracy: 0.7053 - val_loss: 1.4371 - val_accuracy: 0.7257
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4371 - val_accuracy: 0.7259
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 364/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4371 - val_accuracy: 0.7262
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 365/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4371 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 366/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 371/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7259
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 372/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 373/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 374/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 375/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 376/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 377/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 378/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 379/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 380/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7259
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 381/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 382/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 385/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 387/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7262
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 388/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7262
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 390/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 391/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 392/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 393/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9844923551502146
Epoch 394/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 395/500
235/235 [==============================] - 2s 10ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 396/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7262
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 397/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 398/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 399/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7259
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 400/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7056 - val_loss: 1.4370 - val_accuracy: 0.7260
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9844923551502146
Epoch 401/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6849 - accuracy: 0.5622 - val_loss: 1.6380 - val_accuracy: 0.5701
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9893374463519313
Epoch 402/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6470 - accuracy: 0.5576 - val_loss: 1.6270 - val_accuracy: 0.5670
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 403/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6416 - accuracy: 0.5526 - val_loss: 1.6246 - val_accuracy: 0.5587
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 404/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6403 - accuracy: 0.5508 - val_loss: 1.6239 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 405/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6398 - accuracy: 0.5508 - val_loss: 1.6236 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 406/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6396 - accuracy: 0.5509 - val_loss: 1.6234 - val_accuracy: 0.5555
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 407/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6394 - accuracy: 0.5507 - val_loss: 1.6233 - val_accuracy: 0.5555
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 408/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6393 - accuracy: 0.5508 - val_loss: 1.6232 - val_accuracy: 0.5556
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 409/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6392 - accuracy: 0.5509 - val_loss: 1.6231 - val_accuracy: 0.5558
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 410/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6391 - accuracy: 0.5510 - val_loss: 1.6230 - val_accuracy: 0.5561
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 411/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6390 - accuracy: 0.5509 - val_loss: 1.6230 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 412/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6390 - accuracy: 0.5511 - val_loss: 1.6230 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 413/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5513 - val_loss: 1.6229 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 414/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5513 - val_loss: 1.6229 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 415/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5512 - val_loss: 1.6229 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 416/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5512 - val_loss: 1.6229 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 417/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5507 - val_loss: 1.6228 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 418/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 419/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5514 - val_loss: 1.6228 - val_accuracy: 0.5560
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 420/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 421/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 422/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 423/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 424/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 425/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 426/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 427/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 428/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6227 - val_accuracy: 0.5568
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 429/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 430/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 431/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5508 - val_loss: 1.6228 - val_accuracy: 0.5562
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 432/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5508 - val_loss: 1.6228 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5515 - val_loss: 1.6228 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 435/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 436/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 437/500
235/235 [==============================] - 2s 10ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 438/500
235/235 [==============================] - 2s 10ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5562
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 439/500
235/235 [==============================] - 2s 10ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5562
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 440/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5509 - val_loss: 1.6227 - val_accuracy: 0.5562
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 441/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 442/500
235/235 [==============================] - 3s 11ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5562
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 443/500
235/235 [==============================] - 2s 10ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5508 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5568
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 447/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 448/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5509 - val_loss: 1.6227 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 449/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5514 - val_loss: 1.6227 - val_accuracy: 0.5562
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 450/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 451/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5509 - val_loss: 1.6227 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 452/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6227 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 453/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5508 - val_loss: 1.6227 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 454/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 455/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 456/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 457/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5515 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6228 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 460/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5562
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 461/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6227 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 462/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 463/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6227 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 464/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 465/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 466/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 467/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 468/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 469/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 470/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5568
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 471/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 472/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6227 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 473/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 475/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5509 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 476/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 477/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 478/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 479/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 480/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 481/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 482/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 483/500
235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 485/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 486/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6387 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5568
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 487/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 488/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5568
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 489/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6227 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 490/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5566
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 491/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 492/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5514 - val_loss: 1.6227 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 493/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6227 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 495/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6227 - val_accuracy: 0.5563
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5567
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 499/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5564
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 500/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5565
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 1/500
235/235 [==============================] - 3s 9ms/step - loss: 0.0023 - accuracy: 0.9991 - val_loss: 0.2456 - val_accuracy: 0.9727
[ 0.          0.          0.         ...  0.         -1.0181915
  0.88523495]
Sparsity at: 0.5
Epoch 2/500
235/235 [==============================] - 2s 8ms/step - loss: 9.5415e-04 - accuracy: 0.9997 - val_loss: 0.2419 - val_accuracy: 0.9735
[ 0.          0.          0.         ...  0.         -1.0231206
  0.88134694]
Sparsity at: 0.5
Epoch 3/500
235/235 [==============================] - 2s 9ms/step - loss: 3.8316e-04 - accuracy: 0.9999 - val_loss: 0.2335 - val_accuracy: 0.9736
[ 0.         0.         0.        ... -0.        -1.0226798  0.8862854]
Sparsity at: 0.5
Epoch 4/500
235/235 [==============================] - 2s 9ms/step - loss: 9.2704e-05 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9739
[ 0.          0.          0.         ...  0.         -1.0206683
  0.88623744]
Sparsity at: 0.5
Epoch 5/500
235/235 [==============================] - 2s 9ms/step - loss: 2.0538e-05 - accuracy: 1.0000 - val_loss: 0.2296 - val_accuracy: 0.9739
[ 0.          0.          0.         ...  0.         -1.0203844
  0.88652563]
Sparsity at: 0.5
Epoch 6/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4560e-05 - accuracy: 1.0000 - val_loss: 0.2290 - val_accuracy: 0.9737
[ 0.          0.          0.         ...  0.         -1.0202798
  0.88662416]
Sparsity at: 0.5
Epoch 7/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2441e-05 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9735
[ 0.          0.          0.         ...  0.         -1.0202796
  0.88675714]
Sparsity at: 0.5
Epoch 8/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0955e-05 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9736
[ 0.         0.         0.        ...  0.        -1.0203437  0.8869064]
Sparsity at: 0.5
Epoch 9/500
235/235 [==============================] - 2s 9ms/step - loss: 9.8052e-06 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9735
[ 0.          0.          0.         ...  0.         -1.0204452
  0.88705945]
Sparsity at: 0.5
Epoch 10/500
235/235 [==============================] - 2s 9ms/step - loss: 8.8728e-06 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9736
[ 0.         0.         0.        ...  0.        -1.0205829  0.8872201]
Sparsity at: 0.5
Epoch 11/500
235/235 [==============================] - 2s 8ms/step - loss: 8.0879e-06 - accuracy: 1.0000 - val_loss: 0.2278 - val_accuracy: 0.9735
[ 0.         0.         0.        ...  0.        -1.0207484  0.8873786]
Sparsity at: 0.5
Epoch 12/500
235/235 [==============================] - 2s 9ms/step - loss: 7.4125e-06 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 0.9736
[ 0.          0.          0.         ...  0.         -1.0209476
  0.88753873]
Sparsity at: 0.5
Epoch 13/500
235/235 [==============================] - 2s 8ms/step - loss: 6.8175e-06 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9738
[ 0.          0.          0.         ...  0.         -1.0211667
  0.88770187]
Sparsity at: 0.5
Epoch 14/500
235/235 [==============================] - 2s 9ms/step - loss: 6.2891e-06 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9738
[ 0.         0.         0.        ...  0.        -1.0214062  0.8878677]
Sparsity at: 0.5
Epoch 15/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8141e-06 - accuracy: 1.0000 - val_loss: 0.2273 - val_accuracy: 0.9738
[ 0.          0.          0.         ...  0.         -1.0216669
  0.88803166]
Sparsity at: 0.5
Epoch 16/500
235/235 [==============================] - 2s 9ms/step - loss: 5.3842e-06 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9739
[ 0.         0.         0.        ...  0.        -1.0219483  0.888198 ]
Sparsity at: 0.5
Epoch 17/500
235/235 [==============================] - 2s 9ms/step - loss: 4.9949e-06 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9739
[ 0.          0.          0.         ...  0.         -1.0222465
  0.88837135]
Sparsity at: 0.5
Epoch 18/500
235/235 [==============================] - 2s 9ms/step - loss: 4.6368e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9740
[ 0.          0.          0.         ...  0.         -1.0225662
  0.88855237]
Sparsity at: 0.5
Epoch 19/500
235/235 [==============================] - 2s 9ms/step - loss: 4.3109e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9740
[ 0.         0.         0.        ...  0.        -1.0229051  0.8887399]
Sparsity at: 0.5
Epoch 20/500
235/235 [==============================] - 2s 8ms/step - loss: 4.0075e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9742
[ 0.         0.         0.        ...  0.        -1.0232621  0.8889321]
Sparsity at: 0.5
Epoch 21/500
235/235 [==============================] - 2s 8ms/step - loss: 3.7290e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9742
[ 0.         0.         0.        ...  0.        -1.0236423  0.8891367]
Sparsity at: 0.5
Epoch 22/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4699e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9743
[ 0.         0.         0.        ...  0.        -1.0240476  0.8893552]
Sparsity at: 0.5
Epoch 23/500
235/235 [==============================] - 2s 9ms/step - loss: 3.2312e-06 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9743
[ 0.          0.          0.         ...  0.         -1.0244749
  0.88957274]
Sparsity at: 0.5
Epoch 24/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0097e-06 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9745
[ 0.         0.         0.        ...  0.        -1.0249312  0.889815 ]
Sparsity at: 0.5
Epoch 25/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8032e-06 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9746
[ 0.         0.         0.        ...  0.        -1.0254152  0.8900664]
Sparsity at: 0.5
Epoch 26/500
235/235 [==============================] - 2s 9ms/step - loss: 2.6122e-06 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9746
[ 0.          0.          0.         ...  0.         -1.0259303
  0.89034384]
Sparsity at: 0.5
Epoch 27/500
235/235 [==============================] - 2s 9ms/step - loss: 2.4325e-06 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9747
[ 0.         0.         0.        ...  0.        -1.0264808  0.8906357]
Sparsity at: 0.5
Epoch 28/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2656e-06 - accuracy: 1.0000 - val_loss: 0.2277 - val_accuracy: 0.9746
[ 0.         0.         0.        ...  0.        -1.0270671  0.8909461]
Sparsity at: 0.5
Epoch 29/500
235/235 [==============================] - 2s 9ms/step - loss: 2.1105e-06 - accuracy: 1.0000 - val_loss: 0.2278 - val_accuracy: 0.9746
[ 0.         0.         0.        ...  0.        -1.0276937  0.8912819]
Sparsity at: 0.5
Epoch 30/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9665e-06 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9747
[ 0.         0.         0.        ...  0.        -1.0283669  0.8916432]
Sparsity at: 0.5
Epoch 31/500
235/235 [==============================] - 2s 9ms/step - loss: 1.8303e-06 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9747
[ 0.          0.          0.         ...  0.         -1.0290956
  0.89201945]
Sparsity at: 0.5
Epoch 32/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7044e-06 - accuracy: 1.0000 - val_loss: 0.2286 - val_accuracy: 0.9747
[ 0.         0.         0.        ...  0.        -1.0298685  0.8924315]
Sparsity at: 0.5
Epoch 33/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5860e-06 - accuracy: 1.0000 - val_loss: 0.2288 - val_accuracy: 0.9748
[ 0.         0.         0.        ...  0.        -1.0306965  0.8928702]
Sparsity at: 0.5
Epoch 34/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4755e-06 - accuracy: 1.0000 - val_loss: 0.2291 - val_accuracy: 0.9748
[ 0.          0.          0.         ...  0.         -1.0315812
  0.89333075]
Sparsity at: 0.5
Epoch 35/500
235/235 [==============================] - 2s 10ms/step - loss: 1.3728e-06 - accuracy: 1.0000 - val_loss: 0.2295 - val_accuracy: 0.9749
[ 0.         0.         0.        ...  0.        -1.0325135  0.8938218]
Sparsity at: 0.5
Epoch 36/500
235/235 [==============================] - 2s 10ms/step - loss: 1.2766e-06 - accuracy: 1.0000 - val_loss: 0.2298 - val_accuracy: 0.9749
[ 0.          0.          0.         ...  0.         -1.0335137
  0.89435744]
Sparsity at: 0.5
Epoch 37/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1870e-06 - accuracy: 1.0000 - val_loss: 0.2302 - val_accuracy: 0.9749
[ 0.          0.          0.         ...  0.         -1.034576
  0.89492154]
Sparsity at: 0.5
Epoch 38/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1028e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9748
[ 0.         0.         0.        ...  0.        -1.035703   0.8955361]
Sparsity at: 0.5
Epoch 39/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0243e-06 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9748
[ 0.          0.          0.         ...  0.         -1.0369009
  0.89618874]
Sparsity at: 0.5
Epoch 40/500
235/235 [==============================] - 2s 9ms/step - loss: 9.5076e-07 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9747
[ 0.          0.          0.         ...  0.         -1.0381527
  0.89689505]
Sparsity at: 0.5
Epoch 41/500
235/235 [==============================] - 2s 9ms/step - loss: 8.8185e-07 - accuracy: 1.0000 - val_loss: 0.2320 - val_accuracy: 0.9749
[ 0.         0.         0.        ...  0.        -1.039488   0.8976448]
Sparsity at: 0.5
Epoch 42/500
235/235 [==============================] - 2s 9ms/step - loss: 8.1843e-07 - accuracy: 1.0000 - val_loss: 0.2325 - val_accuracy: 0.9749
[ 0.          0.          0.         ...  0.         -1.0408934
  0.89844894]
Sparsity at: 0.5
Epoch 43/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5870e-07 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 0.9748
[ 0.          0.          0.         ...  0.         -1.0423669
  0.89930505]
Sparsity at: 0.5
Epoch 44/500
235/235 [==============================] - 2s 9ms/step - loss: 7.0271e-07 - accuracy: 1.0000 - val_loss: 0.2335 - val_accuracy: 0.9748
[ 0.          0.          0.         ...  0.         -1.0439173
  0.90020955]
Sparsity at: 0.5
Epoch 45/500
235/235 [==============================] - 2s 9ms/step - loss: 6.5091e-07 - accuracy: 1.0000 - val_loss: 0.2341 - val_accuracy: 0.9749
[ 0.          0.          0.         ...  0.         -1.0455459
  0.90117425]
Sparsity at: 0.5
Epoch 46/500
235/235 [==============================] - 2s 10ms/step - loss: 6.0229e-07 - accuracy: 1.0000 - val_loss: 0.2346 - val_accuracy: 0.9750
[ 0.          0.          0.         ...  0.         -1.0472581
  0.90217096]
Sparsity at: 0.5
Epoch 47/500
235/235 [==============================] - 2s 9ms/step - loss: 5.5735e-07 - accuracy: 1.0000 - val_loss: 0.2352 - val_accuracy: 0.9749
[ 0.          0.          0.         ...  0.         -1.0490443
  0.90325636]
Sparsity at: 0.5
Epoch 48/500
235/235 [==============================] - 2s 10ms/step - loss: 5.1540e-07 - accuracy: 1.0000 - val_loss: 0.2358 - val_accuracy: 0.9749
[ 0.         0.         0.        ...  0.        -1.0509125  0.9043943]
Sparsity at: 0.5
Epoch 49/500
235/235 [==============================] - 2s 10ms/step - loss: 4.7614e-07 - accuracy: 1.0000 - val_loss: 0.2365 - val_accuracy: 0.9750
[ 0.         0.         0.        ...  0.        -1.0528693  0.9055982]
Sparsity at: 0.5
Epoch 50/500
235/235 [==============================] - 2s 10ms/step - loss: 4.3982e-07 - accuracy: 1.0000 - val_loss: 0.2371 - val_accuracy: 0.9751
[ 0.          0.          0.         ...  0.         -1.0549086
  0.90686244]
Sparsity at: 0.5
Epoch 51/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0183 - accuracy: 0.9945 - val_loss: 0.1800 - val_accuracy: 0.9736
[ 0.         0.         0.        ... -0.        -1.0044947  0.8258815]
Sparsity at: 0.6458724517167382
Epoch 52/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1746 - val_accuracy: 0.9744
[ 0.         0.         0.        ... -0.        -1.0078081  0.8313586]
Sparsity at: 0.6458724517167382
Epoch 53/500
235/235 [==============================] - 2s 9ms/step - loss: 6.2155e-04 - accuracy: 0.9999 - val_loss: 0.1727 - val_accuracy: 0.9747
[ 0.          0.          0.         ... -0.         -1.0144657
  0.83406806]
Sparsity at: 0.6458724517167382
Epoch 54/500
235/235 [==============================] - 2s 8ms/step - loss: 2.8440e-04 - accuracy: 1.0000 - val_loss: 0.1720 - val_accuracy: 0.9751
[ 0.          0.          0.         ... -0.         -1.0159569
  0.83422184]
Sparsity at: 0.6458724517167382
Epoch 55/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9494e-04 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9746
[ 0.         0.         0.        ... -0.        -1.0175234  0.8357566]
Sparsity at: 0.6458724517167382
Epoch 56/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6738e-04 - accuracy: 1.0000 - val_loss: 0.1726 - val_accuracy: 0.9748
[ 0.          0.          0.         ... -0.         -1.0191909
  0.83756787]
Sparsity at: 0.6458724517167382
Epoch 57/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4860e-04 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9746
[ 0.          0.          0.         ... -0.         -1.020996
  0.83942026]
Sparsity at: 0.6458724517167382
Epoch 58/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3377e-04 - accuracy: 1.0000 - val_loss: 0.1736 - val_accuracy: 0.9743
[ 0.         0.         0.        ... -0.        -1.0229108  0.8414131]
Sparsity at: 0.6458724517167382
Epoch 59/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2145e-04 - accuracy: 1.0000 - val_loss: 0.1741 - val_accuracy: 0.9742
[ 0.          0.          0.         ... -0.         -1.0249248
  0.84347373]
Sparsity at: 0.6458724517167382
Epoch 60/500
235/235 [==============================] - 2s 9ms/step - loss: 1.1089e-04 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9739
[ 0.         0.         0.        ... -0.        -1.0270368  0.8456093]
Sparsity at: 0.6458724517167382
Epoch 61/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0156e-04 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9740
[ 0.          0.          0.         ... -0.         -1.0292325
  0.84786755]
Sparsity at: 0.6458724517167382
Epoch 62/500
235/235 [==============================] - 2s 8ms/step - loss: 9.3209e-05 - accuracy: 1.0000 - val_loss: 0.1757 - val_accuracy: 0.9740
[ 0.         0.         0.        ... -0.        -1.0315295  0.8502343]
Sparsity at: 0.6458724517167382
Epoch 63/500
235/235 [==============================] - 2s 8ms/step - loss: 8.5712e-05 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 0.9740
[ 0.         0.         0.        ... -0.        -1.0339124  0.8526186]
Sparsity at: 0.6458724517167382
Epoch 64/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8945e-05 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9741
[ 0.         0.         0.        ... -0.        -1.0363885  0.8552584]
Sparsity at: 0.6458724517167382
Epoch 65/500
235/235 [==============================] - 2s 9ms/step - loss: 7.2818e-05 - accuracy: 1.0000 - val_loss: 0.1775 - val_accuracy: 0.9742
[ 0.         0.         0.        ... -0.        -1.0389671  0.8580301]
Sparsity at: 0.6458724517167382
Epoch 66/500
235/235 [==============================] - 2s 9ms/step - loss: 6.7124e-05 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9743
[ 0.          0.          0.         ... -0.         -1.0416406
  0.86078215]
Sparsity at: 0.6458724517167382
Epoch 67/500
235/235 [==============================] - 2s 9ms/step - loss: 6.1960e-05 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9744
[ 0.         0.         0.        ... -0.        -1.0444332  0.8637285]
Sparsity at: 0.6458724517167382
Epoch 68/500
235/235 [==============================] - 2s 9ms/step - loss: 5.7130e-05 - accuracy: 1.0000 - val_loss: 0.1794 - val_accuracy: 0.9743
[ 0.         0.         0.        ... -0.        -1.0473466  0.8668981]
Sparsity at: 0.6458724517167382
Epoch 69/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2735e-05 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9744
[ 0.         0.         0.        ... -0.        -1.0503454  0.8702127]
Sparsity at: 0.6458724517167382
Epoch 70/500
235/235 [==============================] - 2s 8ms/step - loss: 4.8635e-05 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9746
[ 0.          0.          0.         ... -0.         -1.0534655
  0.87350273]
Sparsity at: 0.6458724517167382
Epoch 71/500
235/235 [==============================] - 2s 9ms/step - loss: 4.4853e-05 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9748
[ 0.          0.          0.         ... -0.         -1.0566862
  0.87715125]
Sparsity at: 0.6458724517167382
Epoch 72/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1314e-05 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9747
[ 0.          0.          0.         ... -0.         -1.0600276
  0.88086885]
Sparsity at: 0.6458724517167382
Epoch 73/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8053e-05 - accuracy: 1.0000 - val_loss: 0.1830 - val_accuracy: 0.9746
[ 0.         0.         0.        ... -0.        -1.0634466  0.8847622]
Sparsity at: 0.6458724517167382
Epoch 74/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5020e-05 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9748
[ 0.          0.          0.         ... -0.         -1.067005
  0.88867897]
Sparsity at: 0.6458724517167382
Epoch 75/500
235/235 [==============================] - 2s 8ms/step - loss: 3.2209e-05 - accuracy: 1.0000 - val_loss: 0.1845 - val_accuracy: 0.9749
[ 0.          0.          0.         ... -0.         -1.0706282
  0.89282113]
Sparsity at: 0.6458724517167382
Epoch 76/500
235/235 [==============================] - 2s 8ms/step - loss: 2.9621e-05 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9748
[ 0.          0.          0.         ... -0.         -1.0743974
  0.89704335]
Sparsity at: 0.6458724517167382
Epoch 77/500
235/235 [==============================] - 2s 8ms/step - loss: 2.7216e-05 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.9750
[ 0.         0.         0.        ... -0.        -1.0782732  0.9014282]
Sparsity at: 0.6458724517167382
Epoch 78/500
235/235 [==============================] - 2s 8ms/step - loss: 2.4975e-05 - accuracy: 1.0000 - val_loss: 0.1871 - val_accuracy: 0.9750
[ 0.          0.          0.         ... -0.         -1.0822431
  0.90574473]
Sparsity at: 0.6458724517167382
Epoch 79/500
235/235 [==============================] - 2s 8ms/step - loss: 2.2893e-05 - accuracy: 1.0000 - val_loss: 0.1880 - val_accuracy: 0.9751
[ 0.          0.          0.         ... -0.         -1.0863028
  0.91035235]
Sparsity at: 0.6458724517167382
Epoch 80/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0955e-05 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9751
[ 0.         0.         0.        ... -0.        -1.09045    0.9150701]
Sparsity at: 0.6458724517167382
Epoch 81/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9207e-05 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9754
[ 0.          0.          0.         ... -0.         -1.0947087
  0.91975933]
Sparsity at: 0.6458724517167382
Epoch 82/500
235/235 [==============================] - 2s 9ms/step - loss: 1.7570e-05 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9755
[ 0.          0.          0.         ... -0.         -1.099098
  0.92468375]
Sparsity at: 0.6458724517167382
Epoch 83/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6058e-05 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9754
[ 0.          0.          0.         ...  0.         -1.1035762
  0.92977226]
Sparsity at: 0.6458724517167382
Epoch 84/500
235/235 [==============================] - 2s 8ms/step - loss: 1.4661e-05 - accuracy: 1.0000 - val_loss: 0.1927 - val_accuracy: 0.9753
[ 0.          0.          0.         ... -0.         -1.1081384
  0.93481517]
Sparsity at: 0.6458724517167382
Epoch 85/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3369e-05 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9755
[ 0.         0.         0.        ... -0.        -1.1128359  0.9401547]
Sparsity at: 0.6458724517167382
Epoch 86/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2189e-05 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9754
[ 0.         0.         0.        ... -0.        -1.1176136  0.9453849]
Sparsity at: 0.6458724517167382
Epoch 87/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1112e-05 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9755
[ 0.          0.          0.         ... -0.         -1.1224889
  0.95076984]
Sparsity at: 0.6458724517167382
Epoch 88/500
235/235 [==============================] - 2s 9ms/step - loss: 1.0102e-05 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9755
[ 0.          0.          0.         ...  0.         -1.127452
  0.95645326]
Sparsity at: 0.6458724517167382
Epoch 89/500
235/235 [==============================] - 2s 9ms/step - loss: 9.1733e-06 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9756
[ 0.         0.         0.        ... -0.        -1.132506   0.9621127]
Sparsity at: 0.6458724517167382
Epoch 90/500
235/235 [==============================] - 2s 9ms/step - loss: 8.3432e-06 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9757
[ 0.         0.         0.        ... -0.        -1.1377124  0.968016 ]
Sparsity at: 0.6458724517167382
Epoch 91/500
235/235 [==============================] - 2s 9ms/step - loss: 7.5675e-06 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9759
[ 0.         0.         0.        ...  0.        -1.1429793  0.973983 ]
Sparsity at: 0.6458724517167382
Epoch 92/500
235/235 [==============================] - 2s 9ms/step - loss: 6.8538e-06 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9759
[ 0.          0.          0.         ...  0.         -1.1483622
  0.97986007]
Sparsity at: 0.6458724517167382
Epoch 93/500
235/235 [==============================] - 2s 9ms/step - loss: 6.2062e-06 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9760
[ 0.          0.          0.         ... -0.         -1.1538146
  0.98587465]
Sparsity at: 0.6458724517167382
Epoch 94/500
235/235 [==============================] - 2s 9ms/step - loss: 5.6216e-06 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9761
[ 0.          0.          0.         ... -0.         -1.1594172
  0.99216473]
Sparsity at: 0.6458724517167382
Epoch 95/500
235/235 [==============================] - 2s 8ms/step - loss: 5.0818e-06 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9762
[ 0.         0.         0.        ... -0.        -1.1650869  0.9984474]
Sparsity at: 0.6458724517167382
Epoch 96/500
235/235 [==============================] - 2s 9ms/step - loss: 4.5906e-06 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9762
[ 0.         0.         0.        ...  0.        -1.1708474  1.0047092]
Sparsity at: 0.6458724517167382
Epoch 97/500
235/235 [==============================] - 2s 9ms/step - loss: 4.1432e-06 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9761
[ 0.         0.         0.        ...  0.        -1.1766921  1.0110048]
Sparsity at: 0.6458724517167382
Epoch 98/500
235/235 [==============================] - 2s 9ms/step - loss: 3.7402e-06 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9762
[ 0.         0.         0.        ...  0.        -1.18261    1.0173999]
Sparsity at: 0.6458724517167382
Epoch 99/500
235/235 [==============================] - 2s 8ms/step - loss: 3.3735e-06 - accuracy: 1.0000 - val_loss: 0.2108 - val_accuracy: 0.9761
[ 0.         0.         0.        ...  0.        -1.1885993  1.0237564]
Sparsity at: 0.6458724517167382
Epoch 100/500
235/235 [==============================] - 2s 8ms/step - loss: 3.0358e-06 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9761
[ 0.         0.         0.        ... -0.        -1.1947235  1.0302945]
Sparsity at: 0.6458724517167382
Epoch 101/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0421 - accuracy: 0.9871 - val_loss: 0.1704 - val_accuracy: 0.9702
[ 0.         0.         0.        ...  0.        -1.1724231  0.9685865]
Sparsity at: 0.759438707081545
Epoch 102/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0109 - accuracy: 0.9962 - val_loss: 0.1671 - val_accuracy: 0.9709
[ 0.         0.         0.        ...  0.        -1.1719555  0.9674918]
Sparsity at: 0.759438707081545
Epoch 103/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.1663 - val_accuracy: 0.9713
[ 0.         0.         0.        ...  0.        -1.1666331  0.9704507]
Sparsity at: 0.759438707081545
Epoch 104/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.1645 - val_accuracy: 0.9720
[ 0.         0.         0.        ...  0.        -1.1633383  0.9720621]
Sparsity at: 0.759438707081545
Epoch 105/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0032 - accuracy: 0.9997 - val_loss: 0.1641 - val_accuracy: 0.9719
[ 0.         0.         0.        ...  0.        -1.1623425  0.9759455]
Sparsity at: 0.759438707081545
Epoch 106/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.1643 - val_accuracy: 0.9720
[ 0.          0.          0.         ...  0.         -1.1621644
  0.98052573]
Sparsity at: 0.759438707081545
Epoch 107/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 0.9999 - val_loss: 0.1646 - val_accuracy: 0.9720
[ 0.         0.         0.        ...  0.        -1.1630228  0.9851188]
Sparsity at: 0.759438707081545
Epoch 108/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9722
[ 0.         0.         0.        ...  0.        -1.1645243  0.9898876]
Sparsity at: 0.759438707081545
Epoch 109/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1655 - val_accuracy: 0.9724
[ 0.          0.          0.         ...  0.         -1.166787
  0.99414617]
Sparsity at: 0.759438707081545
Epoch 110/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1661 - val_accuracy: 0.9723
[ 0.          0.          0.         ...  0.         -1.1695281
  0.99826455]
Sparsity at: 0.759438707081545
Epoch 111/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1667 - val_accuracy: 0.9721
[ 0.         0.         0.        ...  0.        -1.1724857  1.002295 ]
Sparsity at: 0.759438707081545
Epoch 112/500
235/235 [==============================] - 2s 9ms/step - loss: 9.7824e-04 - accuracy: 1.0000 - val_loss: 0.1675 - val_accuracy: 0.9721
[ 0.         0.         0.        ...  0.        -1.1757934  1.0063   ]
Sparsity at: 0.759438707081545
Epoch 113/500
235/235 [==============================] - 2s 9ms/step - loss: 8.7433e-04 - accuracy: 1.0000 - val_loss: 0.1683 - val_accuracy: 0.9718
[ 0.         0.         0.        ...  0.        -1.1792421  1.0103333]
Sparsity at: 0.759438707081545
Epoch 114/500
235/235 [==============================] - 2s 8ms/step - loss: 7.8218e-04 - accuracy: 1.0000 - val_loss: 0.1692 - val_accuracy: 0.9720
[ 0.         0.         0.        ...  0.        -1.182873   1.0146369]
Sparsity at: 0.759438707081545
Epoch 115/500
235/235 [==============================] - 2s 8ms/step - loss: 7.0314e-04 - accuracy: 1.0000 - val_loss: 0.1701 - val_accuracy: 0.9724
[ 0.         0.         0.        ...  0.        -1.1867957  1.0192901]
Sparsity at: 0.759438707081545
Epoch 116/500
235/235 [==============================] - 2s 9ms/step - loss: 6.3504e-04 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9723
[ 0.         0.         0.        ...  0.        -1.190718   1.0241282]
Sparsity at: 0.759438707081545
Epoch 117/500
235/235 [==============================] - 2s 8ms/step - loss: 5.7357e-04 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9722
[ 0.         0.         0.        ...  0.        -1.1949095  1.0292276]
Sparsity at: 0.759438707081545
Epoch 118/500
235/235 [==============================] - 2s 8ms/step - loss: 5.1924e-04 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9723
[ 0.         0.         0.        ...  0.        -1.1993282  1.03442  ]
Sparsity at: 0.759438707081545
Epoch 119/500
235/235 [==============================] - 2s 9ms/step - loss: 4.7045e-04 - accuracy: 1.0000 - val_loss: 0.1742 - val_accuracy: 0.9724
[ 0.         0.         0.        ...  0.        -1.2040023  1.0398817]
Sparsity at: 0.759438707081545
Epoch 120/500
235/235 [==============================] - 2s 9ms/step - loss: 4.2618e-04 - accuracy: 1.0000 - val_loss: 0.1753 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2088557  1.0455652]
Sparsity at: 0.759438707081545
Epoch 121/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8669e-04 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9724
[ 0.         0.         0.        ...  0.        -1.21384    1.0516487]
Sparsity at: 0.759438707081545
Epoch 122/500
235/235 [==============================] - 2s 8ms/step - loss: 3.5113e-04 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9724
[ 0.         0.         0.        ...  0.        -1.2191674  1.0578794]
Sparsity at: 0.759438707081545
Epoch 123/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1877e-04 - accuracy: 1.0000 - val_loss: 0.1788 - val_accuracy: 0.9724
[ 0.         0.         0.        ...  0.        -1.2246436  1.0643942]
Sparsity at: 0.759438707081545
Epoch 124/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8929e-04 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2302443  1.0710657]
Sparsity at: 0.759438707081545
Epoch 125/500
235/235 [==============================] - 2s 8ms/step - loss: 2.6259e-04 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2361274  1.0782167]
Sparsity at: 0.759438707081545
Epoch 126/500
235/235 [==============================] - 2s 8ms/step - loss: 2.3885e-04 - accuracy: 1.0000 - val_loss: 0.1827 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2421038  1.0854352]
Sparsity at: 0.759438707081545
Epoch 127/500
235/235 [==============================] - 2s 8ms/step - loss: 2.1638e-04 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9727
[ 0.         0.         0.        ...  0.        -1.2484224  1.0930289]
Sparsity at: 0.759438707081545
Epoch 128/500
235/235 [==============================] - 2s 9ms/step - loss: 1.9629e-04 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2548465  1.1004783]
Sparsity at: 0.759438707081545
Epoch 129/500
235/235 [==============================] - 2s 8ms/step - loss: 1.7807e-04 - accuracy: 1.0000 - val_loss: 0.1868 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2614312  1.1085973]
Sparsity at: 0.759438707081545
Epoch 130/500
235/235 [==============================] - 2s 9ms/step - loss: 1.6126e-04 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2681916  1.116809 ]
Sparsity at: 0.759438707081545
Epoch 131/500
235/235 [==============================] - 2s 9ms/step - loss: 1.4616e-04 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2751759  1.1250877]
Sparsity at: 0.759438707081545
Epoch 132/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3241e-04 - accuracy: 1.0000 - val_loss: 0.1913 - val_accuracy: 0.9725
[ 0.         0.         0.        ...  0.        -1.2822132  1.1334398]
Sparsity at: 0.759438707081545
Epoch 133/500
235/235 [==============================] - 2s 8ms/step - loss: 1.1988e-04 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9727
[ 0.         0.         0.        ...  0.        -1.2894782  1.142117 ]
Sparsity at: 0.759438707081545
Epoch 134/500
235/235 [==============================] - 2s 8ms/step - loss: 1.0835e-04 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9729
[ 0.         0.         0.        ...  0.        -1.2968236  1.150963 ]
Sparsity at: 0.759438707081545
Epoch 135/500
235/235 [==============================] - 2s 8ms/step - loss: 9.7970e-05 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9729
[ 0.         0.         0.        ...  0.        -1.304301   1.1598463]
Sparsity at: 0.759438707081545
Epoch 136/500
235/235 [==============================] - 2s 8ms/step - loss: 8.8480e-05 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9730
[ 0.         0.         0.        ...  0.        -1.311966   1.1689874]
Sparsity at: 0.759438707081545
Epoch 137/500
235/235 [==============================] - 2s 9ms/step - loss: 8.0059e-05 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9729
[ 0.         0.         0.        ...  0.        -1.3196768  1.1780622]
Sparsity at: 0.759438707081545
Epoch 138/500
235/235 [==============================] - 2s 8ms/step - loss: 7.2113e-05 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9728
[ 0.         0.         0.        ...  0.        -1.3274646  1.1875334]
Sparsity at: 0.759438707081545
Epoch 139/500
235/235 [==============================] - 2s 8ms/step - loss: 6.5082e-05 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9730
[ 0.         0.         0.        ...  0.        -1.3355262  1.1969401]
Sparsity at: 0.759438707081545
Epoch 140/500
235/235 [==============================] - 2s 8ms/step - loss: 5.8765e-05 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9730
[ 0.         0.         0.        ...  0.        -1.3436683  1.2065746]
Sparsity at: 0.759438707081545
Epoch 141/500
235/235 [==============================] - 2s 8ms/step - loss: 5.2940e-05 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9730
[ 0.         0.         0.        ...  0.        -1.3517257  1.2162542]
Sparsity at: 0.759438707081545
Epoch 142/500
235/235 [==============================] - 2s 9ms/step - loss: 4.7707e-05 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9730
[ 0.         0.         0.        ...  0.        -1.360137   1.2257247]
Sparsity at: 0.759438707081545
Epoch 143/500
235/235 [==============================] - 2s 8ms/step - loss: 4.2987e-05 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9732
[ 0.         0.         0.        ...  0.        -1.3685989  1.2358656]
Sparsity at: 0.759438707081545
Epoch 144/500
235/235 [==============================] - 2s 8ms/step - loss: 3.8692e-05 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9733
[ 0.         0.         0.        ...  0.        -1.3769926  1.2454377]
Sparsity at: 0.759438707081545
Epoch 145/500
235/235 [==============================] - 2s 9ms/step - loss: 3.4797e-05 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9733
[ 0.         0.         0.        ...  0.        -1.3856144  1.2556477]
Sparsity at: 0.759438707081545
Epoch 146/500
235/235 [==============================] - 2s 8ms/step - loss: 3.1304e-05 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9730
[ 0.         0.         0.        ...  0.        -1.3941742  1.265625 ]
Sparsity at: 0.759438707081545
Epoch 147/500
235/235 [==============================] - 2s 9ms/step - loss: 2.8109e-05 - accuracy: 1.0000 - val_loss: 0.2180 - val_accuracy: 0.9731
[ 0.         0.         0.        ...  0.        -1.4029249  1.2756073]
Sparsity at: 0.759438707081545
Epoch 148/500
235/235 [==============================] - 2s 8ms/step - loss: 2.5272e-05 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9729
[ 0.         0.         0.        ...  0.        -1.4117149  1.2860515]
Sparsity at: 0.759438707081545
Epoch 149/500
235/235 [==============================] - 2s 9ms/step - loss: 2.2729e-05 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9729
[ 0.         0.         0.        ...  0.        -1.420476   1.2957963]
Sparsity at: 0.759438707081545
Epoch 150/500
235/235 [==============================] - 2s 8ms/step - loss: 2.0410e-05 - accuracy: 1.0000 - val_loss: 0.2239 - val_accuracy: 0.9729
[ 0.         0.         0.        ...  0.        -1.429346   1.3061253]
Sparsity at: 0.759438707081545
Epoch 151/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0964 - accuracy: 0.9744 - val_loss: 0.1952 - val_accuracy: 0.9661
[ 0.         0.         0.        ...  0.        -1.2941321  0.       ]
Sparsity at: 0.8448229613733905
Epoch 152/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0401 - accuracy: 0.9872 - val_loss: 0.1885 - val_accuracy: 0.9672
[ 0.         0.         0.        ...  0.        -1.2721828  0.       ]
Sparsity at: 0.8448229613733905
Epoch 153/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0295 - accuracy: 0.9903 - val_loss: 0.1842 - val_accuracy: 0.9684
[ 0.         0.         0.        ...  0.        -1.2564533  0.       ]
Sparsity at: 0.8448229613733905
Epoch 154/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0239 - accuracy: 0.9920 - val_loss: 0.1808 - val_accuracy: 0.9692
[ 0.        0.        0.       ...  0.       -1.244729  0.      ]
Sparsity at: 0.8448229613733905
Epoch 155/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0203 - accuracy: 0.9932 - val_loss: 0.1786 - val_accuracy: 0.9700
[ 0.         0.         0.        ...  0.        -1.2358537  0.       ]
Sparsity at: 0.8448229613733905
Epoch 156/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0177 - accuracy: 0.9943 - val_loss: 0.1768 - val_accuracy: 0.9700
[ 0.         0.         0.        ...  0.        -1.2291857  0.       ]
Sparsity at: 0.8448229613733905
Epoch 157/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0157 - accuracy: 0.9950 - val_loss: 0.1755 - val_accuracy: 0.9701
[ 0.         0.         0.        ...  0.        -1.2241826  0.       ]
Sparsity at: 0.8448229613733905
Epoch 158/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0141 - accuracy: 0.9958 - val_loss: 0.1744 - val_accuracy: 0.9706
[ 0.         0.         0.        ...  0.        -1.2205951  0.       ]
Sparsity at: 0.8448229613733905
Epoch 159/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0127 - accuracy: 0.9964 - val_loss: 0.1739 - val_accuracy: 0.9704
[ 0.         0.         0.        ...  0.        -1.2179171  0.       ]
Sparsity at: 0.8448229613733905
Epoch 160/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0116 - accuracy: 0.9968 - val_loss: 0.1734 - val_accuracy: 0.9706
[ 0.         0.         0.        ...  0.        -1.2157714  0.       ]
Sparsity at: 0.8448229613733905
Epoch 161/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0106 - accuracy: 0.9973 - val_loss: 0.1731 - val_accuracy: 0.9706
[ 0.        0.        0.       ...  0.       -1.214289  0.      ]
Sparsity at: 0.8448229613733905
Epoch 162/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0098 - accuracy: 0.9976 - val_loss: 0.1728 - val_accuracy: 0.9710
[ 0.         0.         0.        ...  0.        -1.2130989  0.       ]
Sparsity at: 0.8448229613733905
Epoch 163/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0090 - accuracy: 0.9980 - val_loss: 0.1728 - val_accuracy: 0.9712
[ 0.         0.         0.        ...  0.        -1.2123059  0.       ]
Sparsity at: 0.8448229613733905
Epoch 164/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0083 - accuracy: 0.9982 - val_loss: 0.1730 - val_accuracy: 0.9709
[ 0.         0.         0.        ... -0.        -1.2114054  0.       ]
Sparsity at: 0.8448229613733905
Epoch 165/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0077 - accuracy: 0.9985 - val_loss: 0.1731 - val_accuracy: 0.9708
[ 0.         0.         0.        ...  0.        -1.2114593  0.       ]
Sparsity at: 0.8448229613733905
Epoch 166/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0071 - accuracy: 0.9987 - val_loss: 0.1736 - val_accuracy: 0.9709
[ 0.         0.         0.        ...  0.        -1.2115622  0.       ]
Sparsity at: 0.8448229613733905
Epoch 167/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0066 - accuracy: 0.9991 - val_loss: 0.1741 - val_accuracy: 0.9706
[ 0.         0.         0.        ...  0.        -1.2119647  0.       ]
Sparsity at: 0.8448229613733905
Epoch 168/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0062 - accuracy: 0.9992 - val_loss: 0.1745 - val_accuracy: 0.9704
[ 0.         0.         0.        ... -0.        -1.2125804  0.       ]
Sparsity at: 0.8448229613733905
Epoch 169/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0057 - accuracy: 0.9994 - val_loss: 0.1752 - val_accuracy: 0.9705
[ 0.         0.         0.        ...  0.        -1.2135597  0.       ]
Sparsity at: 0.8448229613733905
Epoch 170/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0054 - accuracy: 0.9995 - val_loss: 0.1756 - val_accuracy: 0.9703
[ 0.         0.         0.        ...  0.        -1.2143645  0.       ]
Sparsity at: 0.8448229613733905
Epoch 171/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0050 - accuracy: 0.9996 - val_loss: 0.1763 - val_accuracy: 0.9703
[ 0.         0.         0.        ... -0.        -1.2157342  0.       ]
Sparsity at: 0.8448229613733905
Epoch 172/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9997 - val_loss: 0.1771 - val_accuracy: 0.9702
[ 0.         0.         0.        ... -0.        -1.2175868  0.       ]
Sparsity at: 0.8448229613733905
Epoch 173/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0044 - accuracy: 0.9997 - val_loss: 0.1778 - val_accuracy: 0.9703
[ 0.        0.        0.       ... -0.       -1.219726  0.      ]
Sparsity at: 0.8448229613733905
Epoch 174/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0041 - accuracy: 0.9998 - val_loss: 0.1789 - val_accuracy: 0.9702
[ 0.         0.         0.        ... -0.        -1.2223516  0.       ]
Sparsity at: 0.8448229613733905
Epoch 175/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0039 - accuracy: 0.9998 - val_loss: 0.1797 - val_accuracy: 0.9701
[ 0.         0.         0.        ... -0.        -1.2254808  0.       ]
Sparsity at: 0.8448229613733905
Epoch 176/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0036 - accuracy: 0.9999 - val_loss: 0.1808 - val_accuracy: 0.9705
[ 0.         0.         0.        ...  0.        -1.2287586  0.       ]
Sparsity at: 0.8448229613733905
Epoch 177/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0034 - accuracy: 0.9999 - val_loss: 0.1815 - val_accuracy: 0.9705
[ 0.         0.         0.        ... -0.        -1.2325125  0.       ]
Sparsity at: 0.8448229613733905
Epoch 178/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0032 - accuracy: 0.9999 - val_loss: 0.1827 - val_accuracy: 0.9703
[ 0.         0.         0.        ... -0.        -1.2364184  0.       ]
Sparsity at: 0.8448229613733905
Epoch 179/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9706
[ 0.        0.        0.       ... -0.       -1.240545  0.      ]
Sparsity at: 0.8448229613733905
Epoch 180/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.9706
[ 0.         0.         0.        ... -0.        -1.2451948  0.       ]
Sparsity at: 0.8448229613733905
Epoch 181/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.9708
[ 0.        0.        0.       ... -0.       -1.250121  0.      ]
Sparsity at: 0.8448229613733905
Epoch 182/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9709
[ 0.        0.        0.       ... -0.       -1.255553  0.      ]
Sparsity at: 0.8448229613733905
Epoch 183/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.1880 - val_accuracy: 0.9708
[ 0.         0.         0.        ... -0.        -1.2609473  0.       ]
Sparsity at: 0.8448229613733905
Epoch 184/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1893 - val_accuracy: 0.9709
[ 0.         0.         0.        ... -0.        -1.2667714  0.       ]
Sparsity at: 0.8448229613733905
Epoch 185/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9709
[ 0.         0.         0.        ... -0.        -1.2725538  0.       ]
Sparsity at: 0.8448229613733905
Epoch 186/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9710
[ 0.         0.         0.        ... -0.        -1.2783074  0.       ]
Sparsity at: 0.8448229613733905
Epoch 187/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9708
[ 0.         0.         0.        ... -0.        -1.2848704  0.       ]
Sparsity at: 0.8448229613733905
Epoch 188/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9708
[ 0.         0.         0.        ... -0.        -1.2913005  0.       ]
Sparsity at: 0.8448229613733905
Epoch 189/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9707
[ 0.         0.         0.        ... -0.        -1.2981095  0.       ]
Sparsity at: 0.8448229613733905
Epoch 190/500
235/235 [==============================] - 2s 10ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9706
[ 0.         0.         0.        ... -0.        -1.3049219  0.       ]
Sparsity at: 0.8448229613733905
Epoch 191/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9706
[ 0.         0.         0.        ... -0.        -1.3121951  0.       ]
Sparsity at: 0.8448229613733905
Epoch 192/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9705
[ 0.         0.         0.        ... -0.        -1.3192942  0.       ]
Sparsity at: 0.8448229613733905
Epoch 193/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9705
[ 0.         0.         0.        ... -0.        -1.3271637  0.       ]
Sparsity at: 0.8448229613733905
Epoch 194/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9706
[ 0.         0.         0.        ...  0.        -1.3346442  0.       ]
Sparsity at: 0.8448229613733905
Epoch 195/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9706
[ 0.       0.       0.      ... -0.      -1.34241  0.     ]
Sparsity at: 0.8448229613733905
Epoch 196/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9704
[ 0.         0.         0.        ...  0.        -1.3502862  0.       ]
Sparsity at: 0.8448229613733905
Epoch 197/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9704
[ 0.         0.         0.        ...  0.        -1.3579656  0.       ]
Sparsity at: 0.8448229613733905
Epoch 198/500
235/235 [==============================] - 2s 9ms/step - loss: 9.5605e-04 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9705
[ 0.         0.         0.        ... -0.        -1.3659614  0.       ]
Sparsity at: 0.8448229613733905
Epoch 199/500
235/235 [==============================] - 2s 9ms/step - loss: 8.9668e-04 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9706
[ 0.         0.         0.        ... -0.        -1.3740524  0.       ]
Sparsity at: 0.8448229613733905
Epoch 200/500
235/235 [==============================] - 2s 9ms/step - loss: 8.4497e-04 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9706
[ 0.         0.         0.        ...  0.        -1.3822023  0.       ]
Sparsity at: 0.8448229613733905
Epoch 201/500
235/235 [==============================] - 2s 8ms/step - loss: 0.2007 - accuracy: 0.9465 - val_loss: 0.2152 - val_accuracy: 0.9520
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 202/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1068 - accuracy: 0.9666 - val_loss: 0.1938 - val_accuracy: 0.9568
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 203/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0896 - accuracy: 0.9712 - val_loss: 0.1838 - val_accuracy: 0.9583
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 204/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0802 - accuracy: 0.9736 - val_loss: 0.1778 - val_accuracy: 0.9587
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 205/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0739 - accuracy: 0.9753 - val_loss: 0.1737 - val_accuracy: 0.9597
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 206/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0692 - accuracy: 0.9768 - val_loss: 0.1706 - val_accuracy: 0.9602
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 207/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0655 - accuracy: 0.9783 - val_loss: 0.1683 - val_accuracy: 0.9611
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 208/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0624 - accuracy: 0.9789 - val_loss: 0.1665 - val_accuracy: 0.9619
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 209/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0598 - accuracy: 0.9800 - val_loss: 0.1651 - val_accuracy: 0.9619
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 210/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0577 - accuracy: 0.9807 - val_loss: 0.1639 - val_accuracy: 0.9626
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 211/500
235/235 [==============================] - 2s 9ms/step - loss: 0.0557 - accuracy: 0.9812 - val_loss: 0.1629 - val_accuracy: 0.9627
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 212/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0541 - accuracy: 0.9818 - val_loss: 0.1620 - val_accuracy: 0.9624
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 213/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0525 - accuracy: 0.9823 - val_loss: 0.1613 - val_accuracy: 0.9628
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 214/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0512 - accuracy: 0.9829 - val_loss: 0.1608 - val_accuracy: 0.9628
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 215/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0499 - accuracy: 0.9834 - val_loss: 0.1603 - val_accuracy: 0.9628
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 216/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0488 - accuracy: 0.9837 - val_loss: 0.1599 - val_accuracy: 0.9631
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 217/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0478 - accuracy: 0.9842 - val_loss: 0.1595 - val_accuracy: 0.9632
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 218/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0469 - accuracy: 0.9846 - val_loss: 0.1592 - val_accuracy: 0.9631
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 219/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0459 - accuracy: 0.9849 - val_loss: 0.1590 - val_accuracy: 0.9630
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 220/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0451 - accuracy: 0.9851 - val_loss: 0.1588 - val_accuracy: 0.9631
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 221/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0444 - accuracy: 0.9854 - val_loss: 0.1587 - val_accuracy: 0.9633
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 222/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0437 - accuracy: 0.9856 - val_loss: 0.1586 - val_accuracy: 0.9638
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 223/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0430 - accuracy: 0.9859 - val_loss: 0.1586 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 224/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0423 - accuracy: 0.9861 - val_loss: 0.1586 - val_accuracy: 0.9638
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 225/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0417 - accuracy: 0.9862 - val_loss: 0.1585 - val_accuracy: 0.9634
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 226/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0411 - accuracy: 0.9865 - val_loss: 0.1585 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 227/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0406 - accuracy: 0.9868 - val_loss: 0.1587 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 228/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0401 - accuracy: 0.9871 - val_loss: 0.1587 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 229/500
235/235 [==============================] - 2s 8ms/step - loss: 0.0396 - accuracy: 0.9873 - val_loss: 0.1589 - val_accuracy: 0.9636
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 230/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0391 - accuracy: 0.9876 - val_loss: 0.1590 - val_accuracy: 0.9634
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 231/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0386 - accuracy: 0.9877 - val_loss: 0.1592 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 232/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0382 - accuracy: 0.9879 - val_loss: 0.1594 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 233/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0378 - accuracy: 0.9880 - val_loss: 0.1596 - val_accuracy: 0.9637
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 234/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0374 - accuracy: 0.9883 - val_loss: 0.1598 - val_accuracy: 0.9635
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 235/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0370 - accuracy: 0.9885 - val_loss: 0.1600 - val_accuracy: 0.9633
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 236/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0366 - accuracy: 0.9886 - val_loss: 0.1603 - val_accuracy: 0.9636
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 237/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0363 - accuracy: 0.9887 - val_loss: 0.1605 - val_accuracy: 0.9636
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 238/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0359 - accuracy: 0.9888 - val_loss: 0.1608 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 239/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0356 - accuracy: 0.9891 - val_loss: 0.1611 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 240/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0352 - accuracy: 0.9892 - val_loss: 0.1613 - val_accuracy: 0.9639
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9059985246781116
Epoch 241/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0349 - accuracy: 0.9894 - val_loss: 0.1617 - val_accuracy: 0.9640
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 242/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0346 - accuracy: 0.9894 - val_loss: 0.1620 - val_accuracy: 0.9642
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 243/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0343 - accuracy: 0.9896 - val_loss: 0.1623 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 244/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0340 - accuracy: 0.9897 - val_loss: 0.1627 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 245/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0337 - accuracy: 0.9898 - val_loss: 0.1630 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 246/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0334 - accuracy: 0.9899 - val_loss: 0.1634 - val_accuracy: 0.9643
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 247/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0331 - accuracy: 0.9901 - val_loss: 0.1637 - val_accuracy: 0.9641
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 248/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0329 - accuracy: 0.9902 - val_loss: 0.1641 - val_accuracy: 0.9642
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 249/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0326 - accuracy: 0.9903 - val_loss: 0.1645 - val_accuracy: 0.9642
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 250/500
235/235 [==============================] - 2s 7ms/step - loss: 0.0324 - accuracy: 0.9904 - val_loss: 0.1648 - val_accuracy: 0.9644
[ 0.  0.  0. ...  0. -0. -0.]
Sparsity at: 0.9059985246781116
Epoch 251/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4951 - accuracy: 0.8515 - val_loss: 0.3678 - val_accuracy: 0.8900
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9469890021459227
Epoch 252/500
235/235 [==============================] - 2s 9ms/step - loss: 0.3094 - accuracy: 0.9025 - val_loss: 0.3142 - val_accuracy: 0.9058
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 253/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2702 - accuracy: 0.9153 - val_loss: 0.2883 - val_accuracy: 0.9150
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 254/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2489 - accuracy: 0.9221 - val_loss: 0.2725 - val_accuracy: 0.9195
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 255/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2352 - accuracy: 0.9266 - val_loss: 0.2615 - val_accuracy: 0.9228
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 256/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2252 - accuracy: 0.9297 - val_loss: 0.2532 - val_accuracy: 0.9256
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 257/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2176 - accuracy: 0.9323 - val_loss: 0.2468 - val_accuracy: 0.9276
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 258/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2114 - accuracy: 0.9346 - val_loss: 0.2414 - val_accuracy: 0.9289
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 259/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2063 - accuracy: 0.9359 - val_loss: 0.2370 - val_accuracy: 0.9304
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 260/500
235/235 [==============================] - 2s 9ms/step - loss: 0.2020 - accuracy: 0.9371 - val_loss: 0.2333 - val_accuracy: 0.9315
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 261/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1983 - accuracy: 0.9384 - val_loss: 0.2301 - val_accuracy: 0.9330
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 262/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1950 - accuracy: 0.9393 - val_loss: 0.2273 - val_accuracy: 0.9340
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 263/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1921 - accuracy: 0.9400 - val_loss: 0.2247 - val_accuracy: 0.9347
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 264/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1895 - accuracy: 0.9408 - val_loss: 0.2225 - val_accuracy: 0.9359
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 265/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1872 - accuracy: 0.9417 - val_loss: 0.2205 - val_accuracy: 0.9361
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 266/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1851 - accuracy: 0.9425 - val_loss: 0.2186 - val_accuracy: 0.9362
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 267/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1832 - accuracy: 0.9432 - val_loss: 0.2169 - val_accuracy: 0.9369
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 268/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1814 - accuracy: 0.9437 - val_loss: 0.2154 - val_accuracy: 0.9367
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 269/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1798 - accuracy: 0.9441 - val_loss: 0.2140 - val_accuracy: 0.9372
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 270/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1783 - accuracy: 0.9445 - val_loss: 0.2127 - val_accuracy: 0.9375
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 271/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1769 - accuracy: 0.9448 - val_loss: 0.2114 - val_accuracy: 0.9379
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 272/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1756 - accuracy: 0.9453 - val_loss: 0.2103 - val_accuracy: 0.9378
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 273/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1743 - accuracy: 0.9455 - val_loss: 0.2092 - val_accuracy: 0.9383
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 274/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1732 - accuracy: 0.9461 - val_loss: 0.2082 - val_accuracy: 0.9387
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 275/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1721 - accuracy: 0.9466 - val_loss: 0.2073 - val_accuracy: 0.9392
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 276/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1711 - accuracy: 0.9468 - val_loss: 0.2063 - val_accuracy: 0.9400
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 277/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1701 - accuracy: 0.9469 - val_loss: 0.2055 - val_accuracy: 0.9402
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 278/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1692 - accuracy: 0.9470 - val_loss: 0.2047 - val_accuracy: 0.9406
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 279/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1684 - accuracy: 0.9473 - val_loss: 0.2039 - val_accuracy: 0.9408
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 280/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1676 - accuracy: 0.9475 - val_loss: 0.2032 - val_accuracy: 0.9409
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 281/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1668 - accuracy: 0.9479 - val_loss: 0.2025 - val_accuracy: 0.9412
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 282/500
235/235 [==============================] - 2s 10ms/step - loss: 0.1660 - accuracy: 0.9479 - val_loss: 0.2018 - val_accuracy: 0.9415
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 283/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1653 - accuracy: 0.9482 - val_loss: 0.2012 - val_accuracy: 0.9422
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 284/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1646 - accuracy: 0.9486 - val_loss: 0.2006 - val_accuracy: 0.9425
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 285/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1640 - accuracy: 0.9487 - val_loss: 0.2001 - val_accuracy: 0.9425
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 286/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1634 - accuracy: 0.9486 - val_loss: 0.1995 - val_accuracy: 0.9427
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 287/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1628 - accuracy: 0.9490 - val_loss: 0.1990 - val_accuracy: 0.9433
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 288/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1622 - accuracy: 0.9491 - val_loss: 0.1985 - val_accuracy: 0.9436
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 289/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1616 - accuracy: 0.9492 - val_loss: 0.1980 - val_accuracy: 0.9436
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 290/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1610 - accuracy: 0.9496 - val_loss: 0.1976 - val_accuracy: 0.9435
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 291/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1605 - accuracy: 0.9496 - val_loss: 0.1971 - val_accuracy: 0.9437
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 292/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1600 - accuracy: 0.9498 - val_loss: 0.1967 - val_accuracy: 0.9441
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 293/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1595 - accuracy: 0.9500 - val_loss: 0.1963 - val_accuracy: 0.9444
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 294/500
235/235 [==============================] - 2s 8ms/step - loss: 0.1590 - accuracy: 0.9502 - val_loss: 0.1958 - val_accuracy: 0.9442
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 295/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1585 - accuracy: 0.9503 - val_loss: 0.1955 - val_accuracy: 0.9443
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 296/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1581 - accuracy: 0.9504 - val_loss: 0.1951 - val_accuracy: 0.9440
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 297/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1576 - accuracy: 0.9506 - val_loss: 0.1947 - val_accuracy: 0.9443
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 298/500
235/235 [==============================] - 2s 10ms/step - loss: 0.1572 - accuracy: 0.9507 - val_loss: 0.1943 - val_accuracy: 0.9445
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 299/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1568 - accuracy: 0.9508 - val_loss: 0.1940 - val_accuracy: 0.9447
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 300/500
235/235 [==============================] - 2s 9ms/step - loss: 0.1564 - accuracy: 0.9510 - val_loss: 0.1937 - val_accuracy: 0.9450
[0. 0. 0. ... 0. 0. 0.]
Sparsity at: 0.9469890021459227
Epoch 301/500
235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.7437 - val_loss: 0.6718 - val_accuracy: 0.7878
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 302/500
235/235 [==============================] - 2s 9ms/step - loss: 0.6515 - accuracy: 0.7914 - val_loss: 0.6309 - val_accuracy: 0.8007
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 303/500
235/235 [==============================] - 2s 9ms/step - loss: 0.6219 - accuracy: 0.8023 - val_loss: 0.6093 - val_accuracy: 0.8091
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 304/500
235/235 [==============================] - 2s 9ms/step - loss: 0.6035 - accuracy: 0.8079 - val_loss: 0.5937 - val_accuracy: 0.8158
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 305/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5895 - accuracy: 0.8123 - val_loss: 0.5815 - val_accuracy: 0.8194
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 306/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5780 - accuracy: 0.8160 - val_loss: 0.5716 - val_accuracy: 0.8223
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 307/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5683 - accuracy: 0.8192 - val_loss: 0.5636 - val_accuracy: 0.8238
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 308/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5603 - accuracy: 0.8212 - val_loss: 0.5569 - val_accuracy: 0.8245
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 309/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5533 - accuracy: 0.8227 - val_loss: 0.5510 - val_accuracy: 0.8264
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 310/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5466 - accuracy: 0.8247 - val_loss: 0.5455 - val_accuracy: 0.8283
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 311/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5402 - accuracy: 0.8269 - val_loss: 0.5405 - val_accuracy: 0.8297
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 312/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5342 - accuracy: 0.8290 - val_loss: 0.5361 - val_accuracy: 0.8302
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 313/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5289 - accuracy: 0.8303 - val_loss: 0.5321 - val_accuracy: 0.8316
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 314/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5245 - accuracy: 0.8309 - val_loss: 0.5287 - val_accuracy: 0.8338
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 315/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5208 - accuracy: 0.8320 - val_loss: 0.5256 - val_accuracy: 0.8353
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 316/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5174 - accuracy: 0.8338 - val_loss: 0.5227 - val_accuracy: 0.8365
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 317/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5141 - accuracy: 0.8349 - val_loss: 0.5198 - val_accuracy: 0.8371
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 318/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5112 - accuracy: 0.8360 - val_loss: 0.5171 - val_accuracy: 0.8383
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 319/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5085 - accuracy: 0.8371 - val_loss: 0.5144 - val_accuracy: 0.8391
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 320/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5061 - accuracy: 0.8376 - val_loss: 0.5119 - val_accuracy: 0.8403
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 321/500
235/235 [==============================] - 2s 8ms/step - loss: 0.5037 - accuracy: 0.8386 - val_loss: 0.5095 - val_accuracy: 0.8409
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 322/500
235/235 [==============================] - 2s 9ms/step - loss: 0.5016 - accuracy: 0.8393 - val_loss: 0.5073 - val_accuracy: 0.8414
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 323/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4995 - accuracy: 0.8401 - val_loss: 0.5053 - val_accuracy: 0.8415
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 324/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4977 - accuracy: 0.8410 - val_loss: 0.5035 - val_accuracy: 0.8421
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 325/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4959 - accuracy: 0.8416 - val_loss: 0.5018 - val_accuracy: 0.8426
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 326/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4943 - accuracy: 0.8420 - val_loss: 0.5004 - val_accuracy: 0.8439
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 327/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4929 - accuracy: 0.8429 - val_loss: 0.4990 - val_accuracy: 0.8444
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 328/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4915 - accuracy: 0.8431 - val_loss: 0.4978 - val_accuracy: 0.8450
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 329/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4904 - accuracy: 0.8437 - val_loss: 0.4967 - val_accuracy: 0.8458
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 330/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4892 - accuracy: 0.8440 - val_loss: 0.4957 - val_accuracy: 0.8464
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 331/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4882 - accuracy: 0.8442 - val_loss: 0.4947 - val_accuracy: 0.8470
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 332/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4872 - accuracy: 0.8445 - val_loss: 0.4938 - val_accuracy: 0.8475
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 333/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4863 - accuracy: 0.8446 - val_loss: 0.4930 - val_accuracy: 0.8475
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 334/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4854 - accuracy: 0.8450 - val_loss: 0.4922 - val_accuracy: 0.8475
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 335/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4846 - accuracy: 0.8452 - val_loss: 0.4915 - val_accuracy: 0.8475
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 336/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4839 - accuracy: 0.8454 - val_loss: 0.4909 - val_accuracy: 0.8477
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 337/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4832 - accuracy: 0.8457 - val_loss: 0.4902 - val_accuracy: 0.8480
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 338/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4825 - accuracy: 0.8458 - val_loss: 0.4896 - val_accuracy: 0.8483
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 339/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4818 - accuracy: 0.8460 - val_loss: 0.4891 - val_accuracy: 0.8479
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 340/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4812 - accuracy: 0.8462 - val_loss: 0.4885 - val_accuracy: 0.8479
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 341/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4806 - accuracy: 0.8465 - val_loss: 0.4880 - val_accuracy: 0.8478
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 342/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4801 - accuracy: 0.8469 - val_loss: 0.4874 - val_accuracy: 0.8482
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 343/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4795 - accuracy: 0.8469 - val_loss: 0.4870 - val_accuracy: 0.8482
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 344/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4790 - accuracy: 0.8471 - val_loss: 0.4865 - val_accuracy: 0.8480
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 345/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4785 - accuracy: 0.8472 - val_loss: 0.4861 - val_accuracy: 0.8479
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 346/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4780 - accuracy: 0.8474 - val_loss: 0.4857 - val_accuracy: 0.8478
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 347/500
235/235 [==============================] - 2s 8ms/step - loss: 0.4775 - accuracy: 0.8475 - val_loss: 0.4853 - val_accuracy: 0.8481
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 348/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4771 - accuracy: 0.8476 - val_loss: 0.4849 - val_accuracy: 0.8480
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 349/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4767 - accuracy: 0.8476 - val_loss: 0.4846 - val_accuracy: 0.8485
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 350/500
235/235 [==============================] - 2s 9ms/step - loss: 0.4762 - accuracy: 0.8478 - val_loss: 0.4843 - val_accuracy: 0.8488
[ 0.  0.  0. ...  0. -0.  0.]
Sparsity at: 0.9718515289699571
Epoch 351/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5502 - accuracy: 0.5111 - val_loss: 1.3582 - val_accuracy: 0.5502
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 352/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3599 - accuracy: 0.5463 - val_loss: 1.3264 - val_accuracy: 0.5555
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 353/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3401 - accuracy: 0.5501 - val_loss: 1.3149 - val_accuracy: 0.5579
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9846097103004292
Epoch 354/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3309 - accuracy: 0.5516 - val_loss: 1.3087 - val_accuracy: 0.5598
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 355/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3253 - accuracy: 0.5521 - val_loss: 1.3047 - val_accuracy: 0.5613
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 356/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3213 - accuracy: 0.5533 - val_loss: 1.3017 - val_accuracy: 0.5623
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 357/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3182 - accuracy: 0.5538 - val_loss: 1.2992 - val_accuracy: 0.5625
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 358/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3155 - accuracy: 0.5547 - val_loss: 1.2971 - val_accuracy: 0.5637
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 359/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3132 - accuracy: 0.5550 - val_loss: 1.2954 - val_accuracy: 0.5638
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 360/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3113 - accuracy: 0.5558 - val_loss: 1.2939 - val_accuracy: 0.5644
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 361/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3096 - accuracy: 0.5566 - val_loss: 1.2927 - val_accuracy: 0.5648
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 362/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3082 - accuracy: 0.5575 - val_loss: 1.2916 - val_accuracy: 0.5650
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 363/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3069 - accuracy: 0.5575 - val_loss: 1.2907 - val_accuracy: 0.5654
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 364/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3058 - accuracy: 0.5580 - val_loss: 1.2898 - val_accuracy: 0.5658
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 365/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3047 - accuracy: 0.5584 - val_loss: 1.2891 - val_accuracy: 0.5660
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 366/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3038 - accuracy: 0.5590 - val_loss: 1.2884 - val_accuracy: 0.5669
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 367/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3029 - accuracy: 0.5595 - val_loss: 1.2878 - val_accuracy: 0.5678
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 368/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3021 - accuracy: 0.5598 - val_loss: 1.2872 - val_accuracy: 0.5679
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 369/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3014 - accuracy: 0.5602 - val_loss: 1.2867 - val_accuracy: 0.5683
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 370/500
235/235 [==============================] - 2s 9ms/step - loss: 1.3007 - accuracy: 0.5606 - val_loss: 1.2862 - val_accuracy: 0.5681
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 371/500
235/235 [==============================] - 2s 8ms/step - loss: 1.3001 - accuracy: 0.5610 - val_loss: 1.2858 - val_accuracy: 0.5685
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 372/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2996 - accuracy: 0.5611 - val_loss: 1.2854 - val_accuracy: 0.5685
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 373/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2991 - accuracy: 0.5616 - val_loss: 1.2851 - val_accuracy: 0.5686
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 374/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2986 - accuracy: 0.5619 - val_loss: 1.2847 - val_accuracy: 0.5691
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 375/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2982 - accuracy: 0.5619 - val_loss: 1.2844 - val_accuracy: 0.5692
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 376/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2978 - accuracy: 0.5620 - val_loss: 1.2841 - val_accuracy: 0.5691
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 377/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2975 - accuracy: 0.5622 - val_loss: 1.2839 - val_accuracy: 0.5693
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 378/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2971 - accuracy: 0.5623 - val_loss: 1.2836 - val_accuracy: 0.5692
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 379/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2967 - accuracy: 0.5624 - val_loss: 1.2833 - val_accuracy: 0.5691
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 380/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2964 - accuracy: 0.5627 - val_loss: 1.2831 - val_accuracy: 0.5695
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 381/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2961 - accuracy: 0.5628 - val_loss: 1.2828 - val_accuracy: 0.5699
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 382/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2957 - accuracy: 0.5630 - val_loss: 1.2826 - val_accuracy: 0.5700
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 383/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2954 - accuracy: 0.5631 - val_loss: 1.2824 - val_accuracy: 0.5699
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 384/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2951 - accuracy: 0.5630 - val_loss: 1.2822 - val_accuracy: 0.5701
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 385/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2948 - accuracy: 0.5629 - val_loss: 1.2820 - val_accuracy: 0.5701
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 386/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2945 - accuracy: 0.5630 - val_loss: 1.2818 - val_accuracy: 0.5704
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 387/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2942 - accuracy: 0.5631 - val_loss: 1.2816 - val_accuracy: 0.5703
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 388/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2939 - accuracy: 0.5632 - val_loss: 1.2814 - val_accuracy: 0.5706
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 389/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2936 - accuracy: 0.5634 - val_loss: 1.2812 - val_accuracy: 0.5707
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 390/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2934 - accuracy: 0.5634 - val_loss: 1.2810 - val_accuracy: 0.5707
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 391/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2931 - accuracy: 0.5636 - val_loss: 1.2808 - val_accuracy: 0.5709
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 392/500
235/235 [==============================] - 2s 9ms/step - loss: 1.2928 - accuracy: 0.5636 - val_loss: 1.2806 - val_accuracy: 0.5708
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 393/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2925 - accuracy: 0.5635 - val_loss: 1.2805 - val_accuracy: 0.5710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 394/500
235/235 [==============================] - 2s 8ms/step - loss: 1.2923 - accuracy: 0.5638 - val_loss: 1.2803 - val_accuracy: 0.5710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 395/500
235/235 [==============================] - 2s 7ms/step - loss: 1.2920 - accuracy: 0.5638 - val_loss: 1.2801 - val_accuracy: 0.5709
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 396/500
235/235 [==============================] - 2s 7ms/step - loss: 1.2918 - accuracy: 0.5638 - val_loss: 1.2800 - val_accuracy: 0.5709
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 397/500
235/235 [==============================] - 2s 7ms/step - loss: 1.2915 - accuracy: 0.5638 - val_loss: 1.2798 - val_accuracy: 0.5710
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 398/500
235/235 [==============================] - 2s 7ms/step - loss: 1.2913 - accuracy: 0.5639 - val_loss: 1.2796 - val_accuracy: 0.5711
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 399/500
235/235 [==============================] - 2s 7ms/step - loss: 1.2911 - accuracy: 0.5639 - val_loss: 1.2795 - val_accuracy: 0.5712
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9846097103004292
Epoch 400/500
235/235 [==============================] - 2s 7ms/step - loss: 1.2908 - accuracy: 0.5640 - val_loss: 1.2793 - val_accuracy: 0.5711
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9846097103004292
Epoch 401/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5670 - accuracy: 0.4518 - val_loss: 1.5330 - val_accuracy: 0.4625
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 402/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5355 - accuracy: 0.4424 - val_loss: 1.5285 - val_accuracy: 0.4410
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 403/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5328 - accuracy: 0.4365 - val_loss: 1.5270 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 404/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5316 - accuracy: 0.4367 - val_loss: 1.5262 - val_accuracy: 0.4402
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 405/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5308 - accuracy: 0.4368 - val_loss: 1.5256 - val_accuracy: 0.4398
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 406/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5302 - accuracy: 0.4370 - val_loss: 1.5251 - val_accuracy: 0.4399
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 407/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5297 - accuracy: 0.4373 - val_loss: 1.5246 - val_accuracy: 0.4401
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 408/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5292 - accuracy: 0.4374 - val_loss: 1.5243 - val_accuracy: 0.4403
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 409/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5288 - accuracy: 0.4376 - val_loss: 1.5239 - val_accuracy: 0.4405
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 410/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5284 - accuracy: 0.4375 - val_loss: 1.5237 - val_accuracy: 0.4405
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 411/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5281 - accuracy: 0.4377 - val_loss: 1.5234 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 412/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5277 - accuracy: 0.4376 - val_loss: 1.5231 - val_accuracy: 0.4410
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 413/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5274 - accuracy: 0.4375 - val_loss: 1.5228 - val_accuracy: 0.4404
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 414/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5270 - accuracy: 0.4378 - val_loss: 1.5225 - val_accuracy: 0.4404
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 415/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5267 - accuracy: 0.4378 - val_loss: 1.5222 - val_accuracy: 0.4426
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 416/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5264 - accuracy: 0.4381 - val_loss: 1.5220 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 417/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5261 - accuracy: 0.4375 - val_loss: 1.5218 - val_accuracy: 0.4405
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 418/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5259 - accuracy: 0.4377 - val_loss: 1.5216 - val_accuracy: 0.4425
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 419/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5257 - accuracy: 0.4376 - val_loss: 1.5215 - val_accuracy: 0.4424
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 420/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5255 - accuracy: 0.4376 - val_loss: 1.5212 - val_accuracy: 0.4403
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 421/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5253 - accuracy: 0.4377 - val_loss: 1.5211 - val_accuracy: 0.4403
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 422/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5252 - accuracy: 0.4378 - val_loss: 1.5209 - val_accuracy: 0.4401
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 423/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5250 - accuracy: 0.4380 - val_loss: 1.5208 - val_accuracy: 0.4403
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 424/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5249 - accuracy: 0.4376 - val_loss: 1.5208 - val_accuracy: 0.4404
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 425/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5248 - accuracy: 0.4378 - val_loss: 1.5206 - val_accuracy: 0.4404
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 426/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5247 - accuracy: 0.4377 - val_loss: 1.5205 - val_accuracy: 0.4403
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 427/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5246 - accuracy: 0.4379 - val_loss: 1.5204 - val_accuracy: 0.4405
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 428/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5244 - accuracy: 0.4375 - val_loss: 1.5203 - val_accuracy: 0.4405
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 429/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5243 - accuracy: 0.4373 - val_loss: 1.5201 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 430/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5242 - accuracy: 0.4377 - val_loss: 1.5201 - val_accuracy: 0.4407
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 431/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5241 - accuracy: 0.4379 - val_loss: 1.5200 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 432/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5240 - accuracy: 0.4380 - val_loss: 1.5199 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 433/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5239 - accuracy: 0.4378 - val_loss: 1.5198 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 434/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5238 - accuracy: 0.4377 - val_loss: 1.5198 - val_accuracy: 0.4405
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 435/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5237 - accuracy: 0.4378 - val_loss: 1.5197 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 436/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5237 - accuracy: 0.4379 - val_loss: 1.5196 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 437/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5236 - accuracy: 0.4379 - val_loss: 1.5195 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 438/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5235 - accuracy: 0.4380 - val_loss: 1.5194 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 439/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5234 - accuracy: 0.4378 - val_loss: 1.5193 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 440/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5233 - accuracy: 0.4381 - val_loss: 1.5193 - val_accuracy: 0.4405
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 441/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5232 - accuracy: 0.4382 - val_loss: 1.5192 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 442/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5232 - accuracy: 0.4380 - val_loss: 1.5192 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 443/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5231 - accuracy: 0.4380 - val_loss: 1.5191 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 444/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5230 - accuracy: 0.4379 - val_loss: 1.5190 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 445/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5230 - accuracy: 0.4381 - val_loss: 1.5190 - val_accuracy: 0.4407
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 446/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5229 - accuracy: 0.4385 - val_loss: 1.5190 - val_accuracy: 0.4426
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 447/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5228 - accuracy: 0.4385 - val_loss: 1.5189 - val_accuracy: 0.4406
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 448/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5227 - accuracy: 0.4385 - val_loss: 1.5188 - val_accuracy: 0.4407
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 449/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5227 - accuracy: 0.4380 - val_loss: 1.5188 - val_accuracy: 0.4407
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 450/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5226 - accuracy: 0.4383 - val_loss: 1.5187 - val_accuracy: 0.4409
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 451/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5226 - accuracy: 0.4386 - val_loss: 1.5187 - val_accuracy: 0.4410
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 452/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5225 - accuracy: 0.4381 - val_loss: 1.5186 - val_accuracy: 0.4413
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 453/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5225 - accuracy: 0.4381 - val_loss: 1.5185 - val_accuracy: 0.4409
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 454/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5224 - accuracy: 0.4383 - val_loss: 1.5185 - val_accuracy: 0.4413
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 455/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5224 - accuracy: 0.4384 - val_loss: 1.5185 - val_accuracy: 0.4413
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 456/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5223 - accuracy: 0.4383 - val_loss: 1.5184 - val_accuracy: 0.4413
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 457/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5222 - accuracy: 0.4388 - val_loss: 1.5183 - val_accuracy: 0.4413
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 458/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5222 - accuracy: 0.4385 - val_loss: 1.5183 - val_accuracy: 0.4414
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 459/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5222 - accuracy: 0.4385 - val_loss: 1.5182 - val_accuracy: 0.4414
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 460/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5221 - accuracy: 0.4385 - val_loss: 1.5182 - val_accuracy: 0.4414
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 461/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5221 - accuracy: 0.4383 - val_loss: 1.5182 - val_accuracy: 0.4417
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 462/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5220 - accuracy: 0.4385 - val_loss: 1.5181 - val_accuracy: 0.4415
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 463/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5220 - accuracy: 0.4382 - val_loss: 1.5181 - val_accuracy: 0.4416
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 464/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5219 - accuracy: 0.4388 - val_loss: 1.5181 - val_accuracy: 0.4417
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 465/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5219 - accuracy: 0.4385 - val_loss: 1.5181 - val_accuracy: 0.4417
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 466/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5219 - accuracy: 0.4387 - val_loss: 1.5180 - val_accuracy: 0.4416
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 467/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5218 - accuracy: 0.4385 - val_loss: 1.5180 - val_accuracy: 0.4416
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 468/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5218 - accuracy: 0.4385 - val_loss: 1.5179 - val_accuracy: 0.4419
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 469/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5217 - accuracy: 0.4383 - val_loss: 1.5179 - val_accuracy: 0.4419
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 470/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5217 - accuracy: 0.4387 - val_loss: 1.5178 - val_accuracy: 0.4418
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 471/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5217 - accuracy: 0.4385 - val_loss: 1.5179 - val_accuracy: 0.4419
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 472/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5216 - accuracy: 0.4385 - val_loss: 1.5179 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 473/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5216 - accuracy: 0.4388 - val_loss: 1.5178 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 474/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5216 - accuracy: 0.4386 - val_loss: 1.5178 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 475/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5215 - accuracy: 0.4387 - val_loss: 1.5178 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 476/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5215 - accuracy: 0.4388 - val_loss: 1.5177 - val_accuracy: 0.4419
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 477/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5215 - accuracy: 0.4385 - val_loss: 1.5177 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 478/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5215 - accuracy: 0.4387 - val_loss: 1.5177 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 479/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5214 - accuracy: 0.4388 - val_loss: 1.5177 - val_accuracy: 0.4436
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 480/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5214 - accuracy: 0.4387 - val_loss: 1.5177 - val_accuracy: 0.4418
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 481/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5214 - accuracy: 0.4385 - val_loss: 1.5176 - val_accuracy: 0.4418
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 482/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5213 - accuracy: 0.4389 - val_loss: 1.5176 - val_accuracy: 0.4436
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 483/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5213 - accuracy: 0.4390 - val_loss: 1.5176 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 484/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5213 - accuracy: 0.4389 - val_loss: 1.5176 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 485/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5213 - accuracy: 0.4387 - val_loss: 1.5176 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0.  0.]
Sparsity at: 0.9893374463519313
Epoch 486/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5213 - accuracy: 0.4387 - val_loss: 1.5176 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 487/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5212 - accuracy: 0.4387 - val_loss: 1.5176 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 488/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5212 - accuracy: 0.4387 - val_loss: 1.5176 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 489/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5212 - accuracy: 0.4391 - val_loss: 1.5175 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0. -0.  0.]
Sparsity at: 0.9893374463519313
Epoch 490/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5212 - accuracy: 0.4386 - val_loss: 1.5175 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 491/500
235/235 [==============================] - 3s 12ms/step - loss: 1.5211 - accuracy: 0.4387 - val_loss: 1.5175 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 492/500
235/235 [==============================] - 2s 7ms/step - loss: 1.5211 - accuracy: 0.4389 - val_loss: 1.5175 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 493/500
235/235 [==============================] - 2s 8ms/step - loss: 1.5211 - accuracy: 0.4389 - val_loss: 1.5175 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 494/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5211 - accuracy: 0.4390 - val_loss: 1.5175 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 495/500
235/235 [==============================] - 2s 10ms/step - loss: 1.5211 - accuracy: 0.4389 - val_loss: 1.5175 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 496/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5210 - accuracy: 0.4387 - val_loss: 1.5174 - val_accuracy: 0.4438
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 497/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5210 - accuracy: 0.4390 - val_loss: 1.5174 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 498/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5210 - accuracy: 0.4390 - val_loss: 1.5174 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0. -0. -0.]
Sparsity at: 0.9893374463519313
Epoch 499/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5210 - accuracy: 0.4388 - val_loss: 1.5175 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
Epoch 500/500
235/235 [==============================] - 2s 9ms/step - loss: 1.5209 - accuracy: 0.4391 - val_loss: 1.5175 - val_accuracy: 0.4437
[ 0.  0.  0. ... -0.  0. -0.]
Sparsity at: 0.9893374463519313
In [14]:
magnitude_histories
Out[14]:
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In [ ]:
with open('output/neural-network-pruning/pickle-jar/magnitude_histories'+str(j)+'.pickle', 'wb') as file:
            pickle.dump(magnitude_histories, file)